System and method for corrosion and erosion monitoring of fixed equipment

Abstract

The present disclosure relates to monitoring and detecting corrosion and / or erosion of pipes, vessels, and other components in an industrial facility. The monitoring system may include an arrangement of multiple guided wave (GW) transducers and a longitudinal wave (LW) transducer attached to the pipe component to collectively measure localized corrosion of the pipe component without requiring thickness mapping. The monitoring system may employ an intelligent amplified multiplexer / switch to control the operation of the multiple transducers, which may be controlled and operated to generate waves in the kilohertz range and the megahertz range using the same hardware.

Description

[0001] Related applications

[0002] This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 982,751, filed on February 28, 2020, entitled “System and Method for Corrosion and Erosion Monitoring of Pipelines and Vessels,” (Docket No. MX-2020-PAT-0029-US-PRO), which is also a continuation-in-part of International PCT Patent Application No. PCT / US2020 / 063683, filed on December 8, 2020 (Docket No. MX-2019-PAT-0423-WO-PCT|BW007592.00491). All of the foregoing patent applications are hereby incorporated by reference in their entirety. Technical Field

[0003] The present disclosure relates to the field of corrosion and erosion monitoring of fixed equipment (e.g., pipelines, containers, tanks, etc.). Specifically, the present disclosure relates to a corrosion and / or erosion monitoring system comprising mechanical components, hardware, software, analysis and / or combinations thereof. In one embodiment, the mechanical components and hardware may include one or more ultrasonic transducers, base units, gateways and / or combinations thereof. The system may also include a software platform for remote monitoring. In some embodiments, the system may also include analytical tools for front-end services and back-end services for remote monitoring and / or diagnosis. More specifically, in some embodiments, the present disclosure may relate to a system and method for corrosion and erosion monitoring of fixed equipment, wherein the system / method combines ultrasonic thickness monitoring using longitudinal waves with ultrasonic area monitoring using one or more guided waves, whereby representative thickness measurements are complemented by an area monitoring feature to detect localized corrosion / erosion between representative thickness measurement locations. In another embodiment, a system and method for optimizing asset health monitoring is disclosed, which includes an analysis scheme. Background Art

[0004] The use of ultrasonic transducers for ultrasonic monitoring of the condition and integrity of structural assets, including pipelines and pressure vessels, such as those used in the oil, gas, and power generation industries, is well known. Currently, corrosion and erosion monitoring systems and techniques that incorporate / use ultrasonic transducers are known to include thickness monitoring at a location and area monitoring (also known as guided wave inspection). However, these two systems and techniques are typically independent of each other. In addition to ultrasonic (UT) testing, internal corrosion of pipeline systems is sometimes monitored using radiographic (RT) thickness testing to measure the wall thickness of selected components at specified intervals over the life of the system.

[0005] Thickness monitoring ultrasonic transducers and systems employing them typically measure the thickness of a pipe / vessel wall at the point where the ultrasonic transducer is located. In other words, they do not provide any information about the thickness of the pipe / vessel wall at locations surrounding the exact point where the ultrasonic transducer is located. Thus, if corrosion / erosion occurs at a location outside the ultrasonic transducer, the corrosion / erosion may not be detected unless thickness monitoring is accompanied by ultrasonic transducer mapping. Of course, ultrasonic transducer mapping increases inspection costs. However, these ultrasonic transducers and systems are advantageously permanently mounted on pipes / vessels.

[0006] Conversely, area-monitoring ultrasonic transducers and systems employing them typically measure the thickness of a pipe / vessel's wall across a larger area, typically beyond the location on the pipe / vessel where the thickness-monitoring ultrasonic transducer is mounted. Such area-monitoring ultrasonic transducers and systems employing them typically develop a thickness map of the pipe / vessel's wall spanning the area being measured. While this generated thickness map is theoretically beneficial, currently, such guided wave inspections are extremely complex because typical hardware in this area generates ten to twenty different guided wave modes, and the sheer number of modes and the complexity of the analysis negatively impact the reliability of the inspection results. Furthermore, guided wave inspections are typically not permanently installed on pipes and vessels. Furthermore, highly localized corrosion cannot be reliably detected using temporarily installed guided wave systems, as described in API 574 (API 574, Inspection Practice for Pipeline System Components, Fourth Edition, 2016).

[0007] Furthermore, existing permanently installed corrosion monitoring systems do not utilize sufficient data to determine the placement of sensors within industrial facilities, such as refineries and petrochemical plants, that utilize pipeline systems to transport fluids. The pipeline systems may transport fluids to one or more tanks and / or chemical processing units. Some pipeline systems handle specialized fluids at specified temperatures and / or pressures; these pipeline systems may transport highly corrosive fluids at high temperatures and pressures.

[0008] Furthermore, many industrial facilities face health and safety issues. They may transport fluids that may be flammable and / or toxic. Thus, a failure in the piping system could result in a leak into the atmosphere and / or exposure to plant personnel. Furthermore, some facilities operate for years without planned outages. Therefore, the reliability of the piping system and its components is important.

[0009] In addition to health and safety issues, unplanned outages caused by pipeline system failures are also a problem from a commercial perspective. Considering the potential safety, health, environmental, and commercial risks associated with pipeline failures, the condition of pipeline systems must be monitored to accurately project their remaining life and determine safe repair or replacement dates.

[0010] For the reasons stated above, certain individuals would appreciate improvements in systems and methods for corrosion and erosion monitoring of fixed equipment (eg, pipelines, vessels, etc.). Summary of the Invention

[0011] In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part thereof, and in which various embodiments in which aspects of the present disclosure may be practiced are shown by way of illustration. It will be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present disclosure. It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless otherwise indicated, may be direct or indirect, wired or wireless, and this specification is not intended to be limiting in this regard.

[0012] A system of one or more computers can be configured to perform specific operations or actions by virtue of having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes the system to perform actions. One or more computer programs can be configured to perform specific operations or actions by virtue of including instructions that, when executed by a data processing device, cause the device to perform actions. One general aspect includes a system for detecting localized corrosion near a longitudinal wave (LW) transducer attached to a component that transports material across a distance. The system sometimes also involves a probe assembly that includes the LW transducer and one or more guided wave (GW) transducers attached to the component at a location near the LW transducer. The one or more GW transducers are activated by an analog waveform signal.

[0013] The probe assembly may further include a smart amplifier multiplexer with high current and high voltage inputs, the smart amplifier multiplexer being configured to selectively activate one of the LW transducer and one of the one or more GW transducers. The system may further include: a transmit channel for transmitting signals from a monitoring controller to the probe assembly; the system may further include: a receive channel, the monitoring controller may include: a pulser; an analog-to-digital (ADC) converter; an adjustable gain amplifier; a processor and a memory storing computer-executable instructions, which, when executed by the processor, cause the monitoring controller to perform steps for receiving signals from the probe assembly at the monitoring controller. In some embodiments, the steps may include: generating a short spike signal by the pulser, the short spike signal traveling through the transmit channel to activate the LW transducer by the smart amplifier multiplexer; generating a smooth low-frequency waveform signal by the same pulser, the low-frequency waveform signal traveling through the transmit channel to activate the first of the one or more GW transducers by the smart amplifier multiplexer; in response to the generation of the short spike signal, receiving an indication of a point thickness measurement based on a time-of-flight measurement from the probe assembly by the adjustable gain amplifier and the ADC converter via the receive channel; and in response to the generation of the smooth low-frequency waveform signal, receiving an indication of localized corrosion using a change in a signal characteristic second only to the time-of-flight (ToF) by the adjustable gain amplifier and the ADC converter via the receive channel. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer memory devices, each configured to perform the actions of the method.

[0014] Implementations may include one or more of the following features. The system may include a digital switch configured to transmit a predetermined number of predetermined voltage level pulses. The pulser may include a high voltage high frequency pulser having a high voltage capacitor in a range of 0.7 uF to 5.3 uF, wherein the predetermined number of predetermined voltage level pulses is three, wherein the predetermined voltage level pulses are 0 V, 50 V, and -50 V, wherein the short spike signal is at a frequency of approximately 5 MHz routed to a LW transducer, and wherein the smooth low frequency waveform signal is at a frequency of approximately 50-500 kHz routed to a GW transducer.

[0015] The smart amplifier multiplexer of the probe assembly may be configured to receive signals passing through the transmit channel, filter the received signals, and route the received signals to / from one of the LW transducer and the first of the one or more GW transducers. The smart amplifier multiplexer may include a switch assembly having a transmit switch, a receive switch, and an amplifier. The smart amplifier multiplexer may include a temperature measurement interface implemented as a resistance temperature detector (RTD) interface.

[0016] The intelligent amplifier multiplexer of the probe assembly may include a low-pass filter that switches a signal received via the transmit channel to trigger the LW transducer or the first of the one or more GW transducers. The probe assembly may include one LW transducer and four GW transducers, wherein each GW transducer is permanently attached to a portion of the component within approximately three feet (or other distance) of the one LW transducer, wherein each GW transducer is an area monitoring ultrasonic transducer, and wherein the one LW transducer is a thickness monitoring ultrasonic transducer. In some examples, when the first of the one or more GW transducers is activated, the first of the one or more GW transducers generates a non-dispersive zero-order shear-horizontal wave, and wherein the monitoring controller detects localized corrosion near the LW transducer. Furthermore, in some examples, the foregoing is performed without storing a thickness map.

[0017] The transmitting channel may include a first cable conductor, and the receiving channel may include a second cable conductor different from the first cable conductor. The transmitting channel may include a first cable conductor, and the receiving channel may include a wireless communication channel. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0018] A system of one or more computers can be configured to perform specific operations or actions by virtue of having software, firmware, hardware, or a combination thereof installed on the system that, when operated, causes the system to perform actions. One or more computer programs can be configured to perform specific operations or actions by virtue of including instructions that, when executed by a data processing device, cause the device to perform actions. One general aspect includes a method for screening probe assemblies installed in a pipeline system. The method further includes, before training a model, setting a group sensitivity hyperparameter, a threshold measurement hyperparameter, and a group size hyperparameter for the model. The method further includes, by executing the model on a processor, grouping a first group of probe assemblies based on at least historical pipe wall thickness measurements collected over a time period from the probe assemblies installed in the pipeline system. The method further includes assigning a unique group ID to each group of probe assemblies. The method further includes, after training the model, selecting, by the model, an optimization function from a plurality of optimization functions for use in the model. The method further includes identifying, by the model, a single probe assembly corresponding to each group ID for use in pipe wall thickness monitoring of the pipeline system. The method further includes: transmitting, by a thickness monitoring controller associated with the pipeline system, a pipe wall thickness measurement value from the individual probe assemblies for each group ID for verification. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer memory devices, each configured to perform the actions of the method.

[0019] Implementations may include one or more of the following features.

[0020] The method may include one or more steps to disregard all remaining probe assemblies in each group ID during inspection, except for a single probe assembly from each group ID. The grouping of the first group of probe assemblies is further based on at least inspection information provided to the system and historical pipe wall thickness measurements collected from probe assemblies installed on the pipeline system over a period of time. The pipeline system may include a tank, and wherein a first probe assembly of the probe assemblies is configured to measure a wall thickness of the tank. The method may also include the steps of storing historical pipe wall thickness measurements collected from the probe assemblies installed on the pipeline system over an extended period of time in a computer memory communicatively coupled to the processor; and training the model by the processor using at least the historical pipe wall thickness measurements stored in the computer memory. The model may include an artificial neural network. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0021] One general aspect includes a system for detecting general corrosion (e.g., without localized corrosion) of a plurality of components transporting material across a distance. The system may also include a plurality of probe assemblies attached to one or more components, wherein the probe assemblies may include at least one thickness-monitoring ultrasonic transducer and an area-monitoring ultrasonic transducer configured to detect corrosion (e.g., general corrosion and / or localized corrosion) of the components. The system may also include a data store configured to store historical wall thickness measurements collected over a time period from measurements performed by the probe assemblies. The system may also include a model trained on the historical wall thickness measurements in the data store, and hyperparameters may include a group sensitivity hyperparameter, a threshold measurement hyperparameter, and a group size hyperparameter. The system may also include a monitoring device, which may include a processor and a memory storing computer-executable instructions. When the computer-executable instructions are executed by the processor, the computer-executable instructions cause the system to perform steps, which may also include: grouping a first set of probe assemblies based on the model; assigning a unique group ID to each group of probe assemblies; selecting an optimization function from a plurality of optimization functions based on the model; identifying a probe assembly corresponding to each group ID for use in wall thickness monitoring of the component based on the model and the selected optimization function; and sending a wall thickness measurement from the probe assembly for each group ID by a thickness monitoring controller associated with the component for inspection. In another embodiment, the system may output a list corresponding to the unique identifier of any group ID instead of sending the wall thickness measurement value for inspection. As discussed in various embodiments disclosed herein, an inspector may receive and react accordingly to the output of the system. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer memory devices, each configured to perform the actions of the method.

[0022] Implementations may include one or more of the following features. In the system, wherein the probe assembly identified from each group ID may include more than one probe assembly from the plurality of probe assemblies, and wherein the memory of the monitoring device stores computer-executable instructions that, when executed by the processor, cause the system to perform steps, the steps including: disregarding all remaining probe assemblies in each group ID during inspection except for the more than one probe assembly from each group ID; and verifying that the wall thickness measurement of the more than one probe assembly from each group ID is general corrosion rather than localized corrosion. The wall thickness measurement of the probe assembly from a first group ID may include a wall thickness of a pipe component at the probe assembly. The wall thickness measurement of the probe assembly from a first group ID may include a thickness of a wall of a tank component at the probe assembly. The method may include verifying that the pipe wall thickness measurement of the single probe assembly is general corrosion (e.g., without localized corrosion) by: (i) generating a probability map of all pipe wall thickness measurements associated with the pipeline system, (ii) grouping the plotted pipe wall thickness measurements by nominal thickness, and (iii) identifying a nonlinear relationship on the probability map of the pipe wall thickness measurements grouped by nominal thickness to confirm general corrosion (e.g., without localized corrosion). The pipe wall thickness monitoring may include steps for analyzing, by the probe assembly, original wall thickness, wall thickness loss over time, calibration error, and measurement location repeatability error. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0023] Implementations may include one or more of the following features. The method may also include the step of verifying that the pipe wall thickness measurement of the single probe assembly is general corrosion (e.g., without localized corrosion) by generating a probability map of all pipe wall thickness measurements associated with the pipeline system, grouping the plotted pipe wall thickness measurements by nominal thickness, and identifying a nonlinear relationship on the probability map of the pipe wall thickness measurements grouped by nominal thickness to confirm general corrosion (e.g., without localized corrosion). The pipe wall thickness monitoring may include the step of analyzing, by the probe assembly, original wall thickness, wall thickness loss over time, calibration error, and measurement location repeatability error. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present disclosure is illustrated by way of example and not limitation in the accompanying figures in which like reference numerals indicate similar elements and in which:

[0025] Figure 1 is a diagram of a system for corrosion / erosion monitoring;

[0026] Figure 2 yes Figure 1 a diagram of a thickness monitoring controller and a piezoelectric element of a system;

[0027] Figure 3 yes Figure 2 A diagram of a thickness monitoring controller;

[0028] Figure 4 is formed Figure 2 a diagram of a switch assembly forming part of a piezoelectric assembly;

[0029] Figure 5 yes Figure 2 A diagram of a piezoelectric component;

[0030] Figure 6 、 Figure 7 and Figure 8 is a graphic representation of the method used for corrosion / erosion monitoring;

[0031] Figure 9 、 Figure 10 、 Figure 11 and Figure 12 is a diagram showing signal modulation;

[0032] Figure 13A and FIG. 13B (collectively, “ FIG. 13 ”) is a diagram of an illustrative pipeline with an installed MUT sensor according to one or more aspects of the features disclosed herein;

[0033] Figure 14 is an illustrative network architecture for an industrial facility according to various aspects of the present disclosure;

[0034] Figure 15 is an illustrative diagram of probe assembly grouping in one embodiment of the present disclosure;

[0035] Figure 16A 、 Figure 16B and Figure 16C (collectively referred to as "FIG. 16") shows curves on a graph. Figure 16A FIG. 1 is a graph showing probability curves of measured values ​​used to verify general corrosion versus localized corrosion. Figure 16B is a graph plotting risk levels versus TMLs according to various aspects disclosed herein. Figure 16C shows the shift of a curve giving risk levels versus TMLs after screening according to various aspects disclosed herein;

[0036] FIG17 is a graph showing the cumulative thickness distribution of pipes corroded by naphthenic acid;

[0037] Figure 18Ais a corrosion sensor analysis showing TML measurements by date in one embodiment of the present disclosure;

[0038] Figure 18B It shows the image Figure 18A Another corrosion sensor analysis graph of TML measurements by date but with a higher group sensitivity setting;

[0039] Figure 18C It shows the image Figure 18A Yet another corrosion sensor analysis graph of TML measurements by date but with an even higher group sensitivity setting;

[0040] Figure 19A and Figure 19B is a diagram according to one or more aspects of the present disclosure;

[0041] Figure 20A and Figure 20B Also a diagram according to one or more aspects of the present disclosure;

[0042] Figure 21 shows an illustrative artificial neural network configured to operate in conjunction with the systems, methods, and algorithms disclosed herein; and

[0043] Figure 22 is a flow chart showing illustrative steps of a method performed according to some embodiments disclosed herein;

[0044] Figure 23 According to some embodiments disclosed herein Figure 1 A diagram of a simplified piping and instrumentation diagram (PID) corresponding to an illustrative corrosion / erosion monitoring system is shown.

[0045] Figure 24A and Figure 24B (collectively referred to as "FIG. 24") are illustrative waveforms generated using a low frequency guided wave (GW) transducer according to some embodiments disclosed herein.

[0046] Figure 25A and Figure 25B (collectively referred to as "FIG. 25") are also illustrative waveforms generated using a low frequency guided wave (GW) transducer according to some embodiments disclosed herein.

[0047] Figure 26A and Figure 26B (collectively referred to as "FIG. 26") illustrates the difference between a baseline pipe and a pipe having a defect of varying depths according to some embodiments disclosed herein.

[0048] In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part thereof, and in which various embodiments in which aspects of the present disclosure may be practiced are shown by way of illustration. It will be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the present disclosure. It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless otherwise indicated, may be direct or indirect, wired or wireless, and this specification is not intended to be limiting in this regard. DETAILED DESCRIPTION

[0049] Although the present disclosure is susceptible to being implemented in different forms of embodiments, what is shown in the drawings and described in detail herein are specific embodiments, with the understanding that the present disclosure should be considered as an illustration of the principles of the present disclosure and is not intended to limit the present disclosure to that shown and described herein. Thus, unless otherwise indicated, the features disclosed herein may be combined to form additional combinations that are not shown for the sake of simplicity. It will be further appreciated that, in some embodiments, one or more elements shown by way of example in the drawings may be removed and / or replaced with alternative elements within the scope of the present disclosure.

[0050] Various aspects of the present disclosure relate to monitoring and detection of corrosion and / or erosion of pipes, containers and other components in an industrial facility. The monitoring system may include an arrangement of multiple guided wave (GW) transducers and one or more longitudinal wave (LW) transducers, which are attached to pipeline components to jointly measure localized corrosion of pipeline components without the need for thickness mapping. The monitoring system may employ an intelligent amplifier multiplexer or intelligent switch to digitally drive and control the operation of the multiple GW transducers and one or more LW transducers. In some embodiments, a shared sensor (e.g., transducer) hardware may be controlled and operated to generate waves in the kilohertz and megahertz range. The monitoring system disclosed herein may further simplify the analysis of the waves by limiting the number of guided waves, thereby improving the efficiency of the system.

[0051] One method of corrosion monitoring is to use ultrasonic transducers to determine the thickness of remaining material and, thereby, the ongoing integrity of pipelines, pipes, vessels, and tanks. By employing this approach, leaks and their consequences, such as environmental damage, direct and incidental equipment damage, and process interruptions or slowdowns, can be prevented. Historically, corrosion monitoring has relied primarily on manual ultrasonic inspections: Inspectors use portable ultrasonic equipment to collect thickness measurements at predetermined measurement locations, known as condition measurement locations (CMLs, formerly known as thickness monitoring locations (TMLs)). Manual thickness measurements are often limited in accuracy (deviation from the true thickness value) and precision (deviation from a mean thickness value). These limitations can impact subsequent analysis and calculated corrosion rates associated with the scheduled retirement or repair of an asset. In particular, when addressing general corrosion, the limitations of manual thickness measurements often lead to over-monitoring (e.g., too many CMLs, too frequent inspections), uncertainty, and a tendency to force asset owners and corrosion engineers to trade risk against a component's lifecycle cost.

[0052] Temporarily or permanently installed thickness monitoring equipment can help overcome these technical shortcomings and uncertainties and significantly improve the prediction of maintenance and asset replacement. However, the high cost of these systems often hinders the large-scale deployment of permanently installed systems. As a result, the industry is forced to continue using old-fashioned, inaccurate, and imprecise inspection methods with unnecessarily high safety margins, many uncertainties, and unnecessarily high costs. However, the emergence of the Industrial Internet of Things (IIoT) and Nondestructive Examination (NDE) 4.0 has achieved a paradigm shift.

[0053] When considering the risk of CMLs as part of a traditional inspection schedule or RBI program, it is difficult to distinguish between the majority of low-risk CMLs and the smaller number of high-risk CMLs. Applying a one-size-fits-all, hardware-centric approach has proven to be inadequate and expensive. Instead, the differences in risk and the resulting inspection activities (number of CMLs and number of inspections) require adaptive hardware and analytical solutions to reduce the uncertainty associated with risk and cost while improving plant safety.

[0054] Regarding the scenario of low-risk CMLs, generally, low-risk CMLs are usually inspected only once every 5 or 10 years, and the number of CMLs is typically very small. In fact, in the case of comprehensive corrosion, a circuit would theoretically require only one CML. However, due to technical limitations in accuracy and precision, low-risk circuits are often equipped with an unnecessarily high number of CMLs. In addition, insufficient confidence in the inspection requires additional inspections beyond the planned inspections. This high level of inspection activity across a high number of CMLs and a high number of inspections leads to unnecessarily high inspection costs. In addition, there are costs associated with the evaluation of the quality of thickness measurements by asset owners, corrosion engineers, and fixed equipment teams.

[0055] Modern analytical tools can help optimize inspection activities by reducing waste and costs without increasing risk levels. The relationship between inspection activity and risk reveals that as inspection activity increases, risk decreases. However, from a certain activity level, further increases in inspection activity do not lead to additional reductions in risk; moreover, it is important to remember that increased inspection activity is directly associated with increased costs.

[0056] To recap, ultrasonic thickness measurement is based on measuring the time-of-flight (ToF) of an ultrasonic pulse in the material being inspected. Multiplying the material velocity v(T) with the temperature and dividing by 2 gives the actual thickness t of the material at the measured location. Actual .

[0057]

[0058] The quality of manual measurements is determined by two factors, called precision and accuracy.

[0059] The first limiting factor to accuracy describes the inherent and subjective nature of manual measurement. In this case, it describes the deviation in thickness measurements due to factors such as the influence of different inspectors, their practices and techniques, slight deviations in the measurement location, slightly different instruments, etc. Therefore, people usually determine an average thickness reading for further analysis. When calculating an average, the deviation or standard deviation also needs to be calculated. Therefore, a correct thickness reading will mean a number of thickness measurements and its standard deviation, for example 0.32 ± 0.03 inches. Permanently installed ultrasonic systems greatly improve the accuracy of thickness measurements because factors that negatively affect thickness measurements are eliminated. Manual inspection thickness measurements have a standard deviation of about 0.03 inches (0.762 mm), while permanently installed systems typically improve accuracy to 0.001 inches (0.0254 mm). This is an improvement of about 97%.

[0060] The second limiting factor for accuracy describes the deviation of the average thickness value from the true thickness value. This deviation can be the result of an incorrect time-of-flight measurement, ToF, or due to the use of an incorrect temperature-dependent material velocity v(T). Although time-of-flight measurements are highly accurate with resolution in the nanosecond range, it is common practice to use material velocity values ​​from references, which can differ significantly from the actual material velocity in a fixed installation in the field.

[0061] Therefore, improving the accuracy of a thickness measurement requires determining the actual material velocity. This can be achieved by measuring the actual material velocity during the thickness measurement, or by measuring the material velocity once and applying a temperature correction factor to compensate for temperature variations. For the latter, reference literature provides a typical value of 1% per 55°C. The temperature coefficient determined based on actual field measurements can differ significantly from the literature value. Values ​​range from 0.4% to 1.2%, depending on material composition, material age, and the number and magnitude of temperature cycles experienced.

[0062] Sophisticated, permanently installed ultrasonic monitoring systems provide precise and accurate thickness measurements for high-risk CMLs. Accuracy is important when considering how thickness measurements are used by inspectors and capital equipment owners to schedule repair or replacement of assets.

[0063] In some scenarios, thickness measurements are used to calculate short-term and long-term corrosion rates. The long-term corrosion rate is then used to determine the remaining life and, based on information such as the corrosion allowance and minimum allowable thickness, the equipment's respective retirement dates. Uncertainty in thickness measurements propagates through the corrosion rate calculations and, therefore, the retirement dates. Gaussian error propagation laws can be used to determine the corrosion rate and retirement dates based on thickness measurement uncertainty. Using typical field values ​​from manual inspections, one finds that the repair date exhibits an uncertainty of plus or minus several years. Repairing or replacing at the beginning of the repair window results in higher-than-necessary lifecycle costs, while scheduling repairs later in the window increases the risk of leaks. In one example, the dilemma is whether to repair or replace in 2021 or 2023. This decision relies primarily on the experience of the inspector or asset owner, making it more of an art than a science leading to data-driven decision-making. Implementing sophisticated, permanently installed systems can significantly improve this situation by reducing the maintenance window to a more precise, data-driven window. These smaller maintenance windows resulted in a clear, data-driven decision to repair or replace in 2024.

[0064] A holistic approach using permanently installed sensor systems and analytical tools can transform experience-based decision-making, which is an art, into data-driven decision-making, which is a science. It also improves safety by reducing the amount and frequency of inspectors' exposure to hazardous areas. The shift to optimizing corrosion monitoring with sensors and software has opened up further opportunities. The evolution of models with machine learning and artificial intelligence can further refine corrosion monitoring without compromising safety.

[0065] Figure 1 and Figure 2 A system 100 for monitoring corrosion and erosion of fixed equipment (e.g., pipelines / vessels) is shown. System 100 includes a data analysis and visualization platform 110, an optional gateway 120, a thickness monitoring controller 130, a thickness monitoring ultrasonic transducer 140 for standardization purposes, and at least one probe assembly 150. Each probe assembly 150 includes a switch assembly 160, at least one thickness monitoring ultrasonic transducer 170, and at least one area monitoring ultrasonic transducer 180.

[0066] The data analysis and visualization platform 110 includes a data analysis portion 112 and a visualization portion 114. The data analysis portion 112 is typically a cloud-based, driven software configured to receive (typically wirelessly) signals from one or both of the gateway 120 and the thickness monitoring controller 130. These signals are analyzed by the data analysis portion 112 and converted into visuals for display on the visualization portion 114. The visualization portion 114 can be any suitable device, such as a computer monitor, a tablet, a mobile phone, etc., that will assist the individual monitoring the platform 110 in understanding the information regarding the corrosion / erosion identified by the system 100. The individual may also have the ability to change the image / information on the visualization portion 114 by providing further input to the software.

[0067] The gateway 120 can be configured to receive (typically wirelessly) signals from the thickness monitoring controller 130 and transmit (typically wirelessly) such signals to the platform 110. For example, it may be more economical to use the gateway 120 to establish a cellular connection rather than having the thickness monitoring controller 130 have its own data plan at a facility. In this case, the thickness monitoring controller 130 would communicate with the gateway 120 using, for example, the XBee protocol. In another example, if there is no good cellular connection at the location of the thickness monitoring controller 130, the gateway 120 can be installed at a higher location to establish a cellular connection, and the thickness monitoring controller 130 would submit data to the gateway 120 using, for example, the XBee protocol.

[0068] like Figure 3As best shown, thickness monitoring controller 130 includes a modem 131, a microprocessor 132, a pulser 133, an analog-to-digital converter (ADC) 134, an adjustable gain amplifier 135, a transmit channel 136, and a receive channel 137. Modem 131 is configured to communicate with one or both of platform 110 and gateway 120. Modem 131 can employ any suitable communication option, including but not limited to XBee 915MHz and LTE-M / NB. Modem 131 is configured to communicate with microprocessor 132. Microprocessor 132 can be any type of microprocessor that will provide the required functionality. One such microprocessor 132 is the LPC4370 manufactured and sold by NXP Semiconductors. Microprocessor 132 is configured to communicate with both pulser 133 and ADC 134. Pulser 133 is preferably a high-voltage, high-frequency pulser with a high-voltage capacitor. ADC 134 is preferably a 16-bit, 2 msps (millions of samples per second), although other types of ADCs may be used as appropriate. ADC 134 is configured to communicate with an adjustable gain amplifier 135 (sometimes also commonly referred to as a variable gain amplifier). Adjustable gain amplifier 135 preferably has a dB range of 26-54 dB and a frequency range of 10 kHz to 300 kHz, although other ranges may be used as appropriate. Pulser 133 is configured to communicate with transmit channel 136 to transmit signals to transmit channel 136. Adjustable gain amplifier 135 is configured to communicate with receive channel 137 to receive signals from receive channel 137. Monitoring controller 130 is preferably configured to accommodate a desired number of amplitude scans ("A-scans") (or waveform displays). In the illustrated embodiment, controller 130 is configured to accommodate 16 A-scans (one from thickness measurement ultrasonic transducer 140 and five each from three different probe assemblies 150). Of course, it should be understood that the controller 130 can be configured to accommodate more or less than 16 A-scans, as appropriate, as the number of probe assemblies 150 varies and / or depending on the number of ultrasound transducers 170 / 180 included in each probe assembly 150 (as will be discussed in further detail below).

[0069] The thickness-monitoring ultrasonic transducer 140 is configured to receive signals from the transmit channel 136 of the thickness-monitoring controller 130 and to transmit signals to the receive channel 137 of the thickness-monitoring controller 130. As previously described, the thickness-monitoring ultrasonic transducer 140 is used for standardization purposes and, therefore, serves to calibrate the measurement system when a set of ultrasonic transducers (in this example, at least one thickness-monitoring ultrasonic transducer 170 and at least one area-monitoring ultrasonic transducer 180) is employed. The standardized thickness-monitoring ultrasonic transducer 140 ensures that the system 100 always performs the same way and functions properly, as required by industry standards. In the illustrated embodiment, the standardized thickness-monitoring ultrasonic transducer 140 is configured to perform a single A-scan. In practice, the thickness-monitoring ultrasonic transducer 140 is typically placed on a standard block or a thickness-calibrated sheet of metal to serve as a standardization transducer.

[0070] like Figure 1 As shown, system 100 includes three different / distinct probe assemblies 150A, 150B, and 150C (each also referred to as a probe assembly 150). Depending on the system 100, the number of probe assemblies 150 provided in system 100 may be less than three (e.g., one or two) or may be greater than three (e.g., four, five, etc.). Those skilled in the art will appreciate that, depending on the number of probe assemblies 150 provided in system 100, minor variations / modifications may be required to system 100.

[0071] As mentioned above, each probe assembly 150 includes a switch assembly 160. Figure 4As best shown, the switch assembly 160 includes a power supply 161, a transmit switch 162, a microcontroller 163, a memory 164, a receive switch 165, an amplifier 166, and an optional resistance temperature detector (RTD) interface 167. The power supply 161 communicates with the transmit channel 136 of the thickness monitoring controller 130. The transmit switch 162 communicates with the transmit channel 136 of the thickness monitoring controller 130. The transmit switch 162 preferably has five "switch" channels 162a, 162b, 162c, 162d, and 162e, the purpose and function of each of which will be discussed herein. The microcontroller 163 communicates with the transmit channel 136 of the thickness monitoring controller 130, the transmit switch 162, the memory 164, and the receive switch 165. The microcontroller 163 can be any type of microcontroller that will provide the desired functionality. One such microcontroller 163 is the PIC18 manufactured and sold by Microchip Technology. The memory 164 is preferably a non-volatile memory. The receive switch 165 preferably has four "switch" channels 165a, 165b, 165c, and 165d, the purpose and function of each channel being discussed below. The amplifier 166 communicates with the receive channel 137 of the thickness monitoring controller 130 and the receive switch 165. In some examples, the amplifier 166 may have an amplification of 26-48 dB and a frequency range of 10 kHz to 300 kHz, but other levels / ranges may be set as appropriate. In some examples, the amplifier 166 is a two-stage amplifier, where 26 dB of amplification is set for a single-stage option and 48 dB of amplification is set for a two-stage option, which can be selected by populating or depolulating components on an amplification board. If at least one thickness monitoring ultrasonic transducer 170 includes an RTD 171 (described below), an optional RTD interface 167 is provided. In the illustrated embodiment, each switch assembly 160 is instructed by the controller 130 to acquire five A-scans (one from the thickness-monitoring ultrasonic transducer 170 and one from each of the four area-monitoring ultrasonic transducers 180).

[0072] As mentioned above, each probe assembly 150 includes at least one thickness monitoring ultrasonic transducer 170. Figure 1As shown, each probe assembly 150 includes a thickness-monitoring ultrasonic transducer 170. Depending on the system 100 and the probe assembly 150, the number of thickness-monitoring ultrasonic transducers 170 provided in each probe assembly 150 may be greater than one (e.g., two, three, four, etc.), as appropriate. Those skilled in the art will appreciate that, depending on the number of thickness-monitoring ultrasonic transducers 170 provided in each probe assembly 150, minor variations / modifications may be required to the probe assembly 150 and / or the system 100. Each thickness-monitoring ultrasonic transducer 170 may optionally have an associated RTD 171 to measure the temperature of the pipe / vessel at or near the location where the thickness measurement is taking place. Each thickness-monitoring ultrasonic transducer 170 communicates with the fifth "switch" channel 162e of the transmit switch 162 and, if the thickness-monitoring ultrasonic transducer 170 includes an RTD 171, also communicates with the RTD interface 167.

[0073] The thickness monitoring ultrasonic transducer 170 (as well as the thickness monitoring ultrasonic transducer 140) operates by generating high-frequency ultrasonic waves (e.g., 5 MHz). These ultrasonic waves are often referred to as longitudinal waves (LW), and as such, the thickness monitoring ultrasonic transducer 170 may also be referred to as an LW transducer. In the illustrated embodiment, each thickness monitoring ultrasonic transducer 170 is configured to perform a single A-scan. Unlike the thickness monitoring ultrasonic transducer 140, the thickness monitoring ultrasonic transducer 170 is not placed on a standard block or a thickness-calibrated metal sheet, but rather is placed on a pipe / vessel to measure the thickness of the pipe / vessel at the location where the thickness monitoring ultrasonic transducer 170 is mounted.

[0074] As described above, each probe assembly 150 includes at least one area monitoring ultrasonic transducer 180. Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5As shown, each probe assembly 150 includes four area-monitoring ultrasonic transducers 180A, 180B, 180C, and 180D (each also referred to as an area-monitoring ultrasonic transducer 180). Depending on the system 100 and the probe assembly 150, the number of area-monitoring ultrasonic transducers 180 provided in each probe assembly 150 may be less than four (e.g., one, two, or three) or greater than four (e.g., five, six, etc.), as appropriate. Those skilled in the art will appreciate that minor variations / modifications may be required to the probe assembly 150 and / or system 100, depending on the number of area-monitoring ultrasonic transducers 180 provided in each probe assembly 150. First area-monitoring ultrasonic transducer 180A communicates with first "switch" channel 162a of transmit switch 162 and first "switch" channel 165a of receive switch 165. Second area-monitoring ultrasonic transducer 180B communicates with second "switch" channel 162b of transmit switch 162 and second "switch" channel 165b of receive switch 165. The third area-monitoring ultrasonic transducer 180C communicates with the third “switch” channel 162c of the transmit switch 162 and the third “switch” channel 165c of the receive switch 165. The fourth area-monitoring ultrasonic transducer 180D communicates with the fourth “switch” channel 162d of the transmit switch 162 and the fourth “switch” channel 165d of the receive switch 165.

[0075] In one embodiment, the probe assembly 150 may include a thickness transducer 170 and a set of area transducers 180, each connected to a switch / preamplifier (preamp) assembly 160. In a different embodiment, the thickness transducer 170 and the area transducer 180 may be combined into a single larger probe that is connected to the switch / preamplifier assembly 160 via a single multi-conductor cable. In another embodiment, it may also be a set of larger probes (thickness + 2 area, area + area, etc.).

[0076] The area-monitoring ultrasonic transducer 180 operates by generating low-frequency ultrasonic waves (e.g., 50 kHz to 500 kHz). These ultrasonic waves are often referred to as guided waves (GW), and as such, the area-monitoring ultrasonic transducer 180 may also be referred to as a GW transducer. One such type of guided wave from a GW transducer, the zero-order shear horizontal wave (referred to as SHO in plates and T(0,1) in pipelines), is of interest for its non-dispersive behavior. In the illustrated embodiment, each area-monitoring ultrasonic transducer 180 is configured to perform a single A-scan.

[0077] The GW transducer 180 may be in the form of a piezoelectric patch transducer, but may alternatively be in other forms, such as, for example, a face-shear piezoelectric element. In some embodiments, a patch transducer may be used instead of or in addition to the stacked face-shear piezoelectric elements to provide a higher signal amplitude. Also, in some embodiments, such as Figure 1 and Figure 5 As best shown, GW transducers 180A, 180B, 180C, 180D are positioned in a rectangular configuration around LW transducer 170, with GW transducer 180A positioned at the upper left of LW transducer 170, GW transducer 180B positioned at the lower left of LW transducer 170, GW transducer 180C positioned at the lower right of LW transducer 170, and GW transducer 180D positioned at the upper right of LW transducer 170. When applied to a pipe / vessel, a line from GW transducer 180A to GW transducer 180B is parallel to a line from GW transducer 180C to GW transducer 180D, and a line from GW transducer 180A to GW transducer 180D is parallel to a line from GW transducer 180B to GW transducer 180C. Furthermore, when applied to a pipe / vessel, a straight line from GW transducer 180A to GW transducer 180C intersects LW transducer 170 and a straight line from GW transducer 180B to GW transducer 180D intersects LW transducer 170, thereby providing an "X" configuration.

[0078] When the system 100 is associated with a pipe / container, the system 100 can be used to measure the corrosion / erosion of the pipe / container. In one embodiment, a method 200 for measuring the corrosion / erosion of a pipe / container is described below and shown in FIG. Figure 6 、 Figure 7 and Figure 8 middle.

[0079] The method 200 includes a step 205 of manually measuring the actual longitudinal velocity and temperature of the pipe / vessel to be inspected.

[0080] The method 200 includes a step 210 of manually measuring the actual guided wave velocity and temperature of the pipe / vessel to be inspected.

[0081] Method 200 includes step 215 of performing a thickness normalization measurement using the normalized thickness-monitoring ultrasonic transducer 140 and the RTD 171 (it should be understood that, like the thickness-monitoring ultrasonic transducer 170 , the normalized thickness-monitoring ultrasonic transducer 140 may also optionally include the RTD 171 ).

[0082] Method 200 includes step 220 of performing a measurement using probe assembly 150A. Step 220 includes sub-step 220a of performing a thickness measurement using thickness-monitoring ultrasonic transducer 170 and RTD 171. Step 220 includes sub-step 220b of performing an area thickness measurement using area-monitoring ultrasonic transducers 180A, 180B, 180C, and 180D at a first frequency. Sub-step 220b includes sub-step 220b1 of performing an axial scan, whereby area-monitoring ultrasonic transducer 180A is excited and data is recorded by area-monitoring ultrasonic transducer 180B. The measurement performed in sub-step 220b1 is often repeated as specified in the configuration settings, and the multiple A-scans are averaged. Sub-step 220b includes sub-step 220b2 of performing an axial scan, whereby area-monitoring ultrasonic transducer 180C is excited and data is recorded by area-monitoring ultrasonic transducer 180D. The measurements performed in sub-step 220b2 are repeated three times as specified in the configuration settings, and multiple A-scans are averaged. Sub-step 220b includes sub-step 220b3, which performs a circumferential scan, whereby area-monitoring ultrasonic transducer 180A is excited and data is recorded by area-monitoring ultrasonic transducer 180D. The measurements performed in sub-step 220b3 are repeated three times as specified in the configuration settings, and multiple A-scans are averaged. Sub-step 220b includes sub-step 220b4, which performs a circumferential scan, whereby area-monitoring ultrasonic transducer 180C is excited and data is recorded by area-monitoring ultrasonic transducer 180C. The measurements performed in sub-step 220b4 are repeated three times as specified in the configuration settings, and multiple A-scans are averaged. Thus, channels 162a, 162c (which are associated with GW transducers 180A, 180C) serve as waveguide transmit channels, while channels 162b, 162d (which are associated with GW transducers 180B, 180D) serve as waveguide receive channels. The receive path further proceeds via amplifier 166 to receive channel 137 of thickness monitoring controller 130.

[0083] Step 220 includes sub-step 220c of repeating sub-step 220b at a second frequency, the second frequency being different from the first frequency.

[0084] Step 220 includes sub-step 220d of repeating sub-step 220b at a third frequency, the third frequency being different from both the first frequency and the second frequency.

[0085] Method 200 includes step 225 , which includes repeating step 220 to perform measurements using probe assembly 150B.

[0086] Method 200 includes step 230 , which includes repeating step 220 to perform measurements using probe assembly 150C.

[0087] Thus, method 200 combines ultrasonic thickness monitoring using longitudinal waves with ultrasonic area monitoring using guided waves; moreover, in various embodiments, there is only one specific non-dispersive shear wave mode (SH0 or T(0,1)). Rather than attempting to develop a thickness map, method 200 takes multiple representative thickness measurements, which is supplemented by an area monitoring feature to detect localized corrosion / erosion between the multiple representative thickness measurement locations. System 100 utilizes novel electronics that utilize a single circuit to deliver two distinctly different excitation signals (e.g., high-frequency ultrasonic (5 MHz) for thickness monitoring and low-frequency ultrasonic (50-500 kHz) for area monitoring) from two different types of ultrasonic transducers (e.g., LW transducer 170 and GW transducer 180). Each excitation signal needs to be generated and processed differently. More specifically, pulser 133 of controller 130 is a digital switch capable of delivering only predetermined fixed voltage level pulses: high voltage, low voltage, and zero voltage. The high and low voltage levels are typically adjustable within the range of 5V to 90V and -5V to -90V, respectively, although different voltage levels are possible. Microprocessor 132 signals pulser 133 to output one of the fixed voltage levels to transmit channel 136: for example, a high voltage for a specified time period. An example of a pulse for energizing LW transducer 170: processor 130 instructs pulser 133 to output 0V, followed by a high voltage for a 100ns period, followed by a low voltage for a 100ns period, followed by 0V. The described sequence will produce a bipolar square wave with a frequency of 5 MHz suitable for energizing LW transducer 170. For GW transducer 180, different frequencies and signal amplitudes are required.

[0088] like Figure 9 、 Figure 10 、 Figure 11 and Figure 12 As best shown, the waveform required to excite the GW transducer 180 can have a rather complex shape, for example: a 5 cycle sine wave superimposed on a Figure 12 This will allow for a smoother transition from a no-signal to a signal state. To generate a suitable waveform combination for the GW transducer 180 with respect to a pulser 133 digital output, such as Figure 9 、 Figure 10 、 Figure 11 and Figure 12The waveform 300 shown uses the series resistance of the transmission channel 136 and the impedance of the GW transducer 180. The impedance of the GW transducer 180 within a frequency range (50-500 kHz) used to generate GW waves is generally composed mainly of capacitance. This capacitance and the series resistance of the transmission channel 136 mentioned above form a low-pass filter. The pulser 133 generates a high-frequency (usually in the range of tens of MHz) digital waveform 300 under the instruction of the microprocessor 132. When this high-frequency digital waveform 300 passes through the transmission channel 136 and the capacitance of the GW transducer 180, a waveform 310 (e.g., different from the original output from the pulser 133) is generated. Figure 10 Changing the high frequency digital waveform from the pulser 133 can generate a series of analog waveforms once it passes through the series resistance of the transmission channel 136 and the capacitance of the transducer 180, for example: Figure 11 The sine without Hanning window indicated by 320 in FIG, or Figure 12 Indicated at 330 are Hanning windowed sine, chirp (frequency variation during the pulse duration), ramp-up, sawtooth, etc. Of course, other waveforms besides those described and shown can be generated.

[0089] Reference Figure 9 In one example, the monitoring controller 130 may include a low frequency (e.g., 50-300 kHz) receive path that is available on all channels (e.g., 16 channels) and used in parallel for multiple waveguides. One or more receive paths may also be used, for example, for legacy signals. On all channels, an internal (e.g., protective) series resistor may be combined with the capacitance of the transducer. Furthermore, this combination may act as a low pass filter and convert the high frequency modulation into a low frequency waveform signal. Figure 9 As shown, a low-frequency wavelet output may be possible using a modulated pulser 133. Those skilled in the art will recognize, after reviewing the entire disclosure herein, that measurements improve as the number of cycles increases. For example, measurements taken at GW transducers 180A, 180B, 180C, and 180D after two (or three, five, or other numbers) cycles and at different frequencies may provide deviations from the intended frequency of between -4% and -7% and from the intended voltage range of between -8.5% and +12.5%.

[0090] In one embodiment, a chirp signal can be used to excite multiple frequencies from a channel simultaneously. Appropriate software filtering can decode the individual frequency responses from a single A-scan.

[0091] By employing system 100 and method 200, the time-of-flight and amplitude of a reflected echo at a defect on a pipe / vessel can be assessed. More specifically, by transmitting an excitation signal from GW transducer 180C and receiving it at GW transducers 180B and 180D, the reflected echo will be earlier in the time trajectory at the GW transducer 180B or 180D that is closer to the damage (e.g., GW transducer 180B if the damage is to the left of GW transducers 180B and 180D, or GW transducer 180D if the damage is to the right of GW transducers 180B and 180D). Defects such as pittings or corrosion / erosion patches typically increase in size over time. Consequently, the amplitude of the echo reflected from the defect will increase over time. Thus, a permanently installed system allows one to monitor changes in amplitude next to the time of flight. Monitoring changes in the A-scan, for example after baseline subtraction and digital filtering, reduces the complexity of the analysis and increases confidence in the test results. Next to baseline subtraction, other digital signal processing tools or machine learning algorithms can be used for feature extraction or pattern recognition, which additionally increases the confidence level and helps detect changes earlier in time. The collected signals and / or filtered measurements can be stored in a data analysis platform 112 for analysis and display on a visualization platform 114. Examples of graphical user interfaces and analyses performed by a data analysis and visualization platform 110 (including Figure 14 The remote alarm device 1410) is disclosed herein throughout this disclosure.

[0092] Further with respect to the aforementioned examples involving GW transducers 180B, 180C, and 180D, in various embodiments, GW transducer 180C generates a zero-order shear horizontal wave (referred to as SH0 in plates and T(0,1) in pipelines) having a non-dispersive behavior. A wave is non-dispersive when its velocity is independent of its frequency, so that a small wave composed of several waves of different frequencies does not spread out as it propagates through the material. With GW transducer 180C in a non-dispersive shear wave mode, the system is able to detect and locate localized corrosion near the GW transducer. Thus, by supplementing with area monitoring, system 100 is able to detect localized corrosion / erosion between multiple representative thickness measurement locations. The GW transducer can be an area monitoring ultrasonic transducer, while the LW transducer can be a thickness monitoring ultrasonic transducer.

[0093] Reference Figure 2A system is disclosed for detecting localized corrosion near a longitudinal wave (LW) transducer attached to a component that transports material across a distance. In some examples, the component is a pipe that transports gaseous, liquid, or other materials. In other examples, the component may be a tank, container, or other type of component that transports material (including storing material prior to transport). The system includes a probe assembly 150 comprising a LW transducer and one or more guided wave (GW) transducers attached to the component proximate to the LW transducer. In one embodiment, the probe assembly 150 includes a LW transducer 170 and four GW transducers 180A, 180B, 180C, and 180D, with each GW transducer 180A, 180B, 180C, and 180D being arranged such that each GW transducer is permanently attached to the component within approximately three feet (or other distance, such as six inches, one foot, four feet, six feet, or other distance) of the LW transducer. In some embodiments, four GW transducers may be arranged around a circular shape (e.g., a circle, ellipse, oval, or other circular shape), with the LW transducer located at or near the center of the circular shape. In other embodiments, there may be more or fewer than four GW transducers, and they may be arranged in a pattern other than a circular shape (e.g., a rectangular pattern, a square pattern, a triangular pattern, or other patterns). In any case, the GW transducers in other embodiments are attached in a permanent or near-permanent manner, thereby eliminating the need for a precisely measured thickness map for localized corrosion on a component (e.g., a pipeline, plate, or other material).

[0094] exist Figure 2 In the illustrated embodiment, the GW transducers 180A, 180B, 180C, and 180D can be activated by analog waveform signals. Consequently, the probe assembly 150 including the GW transducers can include a smart amplifying multiplexer 162, 165 configured to selectively activate the LW transducer 170 or one of the GW transducers 180A, 180B, 180C, and 180D, but not simultaneously activate both the LW and GW transducers. In some embodiments, the smart amplifying multiplexer can receive inputs including high current and / or high voltage. These inputs are received via a transmit channel 136, which transmits signals from a monitoring controller 130 to the probe assembly 150. Conversely, a receive channel 137 is configured to receive signals from the probe assembly 150 at the monitoring controller 130.

[0095] The monitoring controller 130 may include one or more electrical components that facilitate its operation, such as a pulser 133, an analog-to-digital converter (ADC) 134, an adjustable gain amplifier 135, a processor 132, and / or a memory storing computer-executable instructions that, when executed by the processor, cause the monitoring controller to perform operational steps. The monitoring controller 130 may generate one or more output signals that travel via a transmission channel 136 of the system. Figure 10 As shown, the modulation of the output signal can depend on the impedance of the transducer. In some embodiments involving other types of transducers, the system can be modified to generate independent waveforms for these different types of transducers. In addition, in some embodiments, other combinations of series resistance and parallel capacitance can be suitable for monitoring controller 130 to generate a smooth waveform based on the capacitance of the transducer.

[0096] In one example, the monitoring controller 130 can generate a short spike signal from the pulser 133, which travels through the transmission channel 136 to activate the LW transducer in the probe assembly 150 via the smart amplifier multiplexer (162, 165). In another example, the monitoring controller 130 can generate a smooth low-frequency waveform signal from the same pulser 133, which travels through the same transmission channel 136 to activate the GW transducer via the smart amplifier multiplexer (162, 165). The same pulser 133 is used to selectively generate a short spike signal or a smooth low-frequency waveform signal, as appropriate. In some examples, the pulser can selectively generate these signals using a digital switch configured to transmit a predetermined number of pulses at a predetermined voltage level. For example, the pulser can include a high-voltage, high-frequency pulser with a high-voltage capacitor in a range of 0.7 μF to 5.3 μF. However, in other examples, the pulser may have an upper range even higher than 5.3 μF. The predetermined number of voltage levels may be any number that allows the pulser to selectively generate a suitable waveform signal. In one example, the pulser may transmit three predetermined voltage level pulses, where the predetermined voltage levels are 0V, 50V, and -50V. However, in other examples, the predetermined voltage levels may be other preset values ​​to which the system is tuned to operate. For example, the aforementioned short spike signal generated by the pulser 133 may be approximately 5 MHz, while the aforementioned smooth low-frequency waveform signal may be approximately 50-500 kHz. In other examples, the signals generated by the pulser 133 may be different or adjusted to accommodate the activation of the LW transducer and the GW transducer in a probe assembly 150. However, the monitoring controller 130 is configured to utilize a set of shared hardware to selectively generate signal waveforms in the kHz and MHz ranges. System 100 utilizes a single circuit that delivers two distinct excitation signals (e.g., high-frequency ultrasonic waves (5 MHz) for thickness monitoring and low-frequency ultrasonic waves (50-500 kHz) for area monitoring) from two different types of ultrasonic transducers (e.g., LW transducer 170 and GW transducer 180). Each excitation signal needs to be generated and processed differently. An alternative design could be to multiplex and then demultiplex, but this would require two different hardware / circuits. However, system 100 utilizes a single circuit to provide this functionality.

[0097] In addition, the monitoring controller 130 may store computer-executable instructions that, when executed by the processor, cause the monitoring controller to process signals received on a receive channel 137. In one example, in response to the generation of a short spike signal, the monitoring controller 130 may receive an indication of a spot thickness measurement based on a time-of-flight measurement from the probe assembly 150 via the receive channel 137 via the adjustable gain amplifier 135 and the ADC converter 134. Time-of-flight (ToF) is a measurement of the time it takes for a wave to travel a distance in a component that is transporting material (e.g., liquid, gas, or other form) across a distance. The spot thickness measurement provides a location measurement made by an activated LW transducer 170 mounted on a component, such as the pipe shown in FIG13B. In another example, in response to the generation of a smooth low-frequency waveform, monitoring controller 130 can receive an indication of localized corrosion from probe assembly 150 via receive channel 137 using adjustable gain amplifier 135 and ADC converter 134, using a change in a signal characteristic (e.g., amplitude) second only to ToF. The presence / detection of an echo (e.g., amplitude) indicates the presence of localized corrosion. ToF can be used to determine the location of localized corrosion.

[0098] The smart amplifier multiplexers 162, 165 of the probe assembly 150 may include one or more electrical components that facilitate their operation, such as a transmit switch 162 and a receive switch 165 (collectively, the "switch assembly"), an amplifier 166, a resistance temperature detector (RTD) interface 167, a controller 163, and / or a memory 164 storing computer-executable instructions that, when executed by the controller 163, cause the smart amplifier multiplexer to perform operational steps. The smart amplifier multiplexer may be configured to receive a signal passing through a transmit channel 136, filter the received signal, and then route the received signal to one of the LW transducer 170 and one of the plurality of GW transducers 180A, 180B, 180C, 180D. In some examples, the transmit channel 136 and the receive channel 137 may include a cable conductor. In another example, the transmit channel 136 may be a cable conductor, while the receive channel 137 may be a different cable conductor than the transmit channel 136. In other examples, one or both of transmit channel 136 and receive channel 137 can be wireless communication channels that enable monitoring controller 130 to communicate with probe assembly 150 without tethering. At least one benefit of such an arrangement is that the monitoring controller can be reused and operated with more than one probe assembly 150. Of course, given the nature of the high voltage and / or high current signal waveforms generated and / or received by monitoring controller 130, it is also beneficial for transmit channel 136 and receive channel 137 to include a pluggable / removable hardwired cable interface with a probe assembly 150, so that monitoring controller 130 can be unplugged and reused in multiple probe assembly installations. For example, probe assembly 150 can include a wireless communication module, such as a Bluetooth module, for transmitting measurements such as thickness and area monitoring A-scans.

[0099] In one example, the smart amplifier multiplexers 162, 165 of the probe assembly 150 may include a low-pass filter that switches a signal received via the transmit channel 136 to trigger the LW transducer 170 or one of the plurality of GW transducers 180A, 180B, 180C, 180D, but not simultaneously. In some examples, the received signal may trigger two or more GW transducers 180A, 180B, 180C, 180D (e.g., a pair of GW transducers 180A, 180C, or another grouping of GW transducers), and one or more transducers may be configured to read the signal passing through the component / fixture. In some examples, the intelligent preamplifier switch 160 may include five switching channels: a first channel for thickness measurement with both transmit and receive (by LW transducer 170); two waveguide transmit channels (e.g., channel 2 and channel 4); and two waveguide receive channels (e.g., channel 3 and channel 5), where the receive path proceeds via a preamplifier / amplifier 166 to a receive channel 137 of a monitoring controller 130. The received signal can be averaged, in some examples up to eight times for a 26 dB preamplifier 166, and up to six times for a 48 dB preamplifier 166 in other examples. In other examples, averaging in multiples of six or eight can be used, depending on the configuration and channels. In some examples, a time scale can be automatically adjusted to account for different sampling frequencies. Samples of each waveform type can be received and stored in one or more memories 164. As described herein, the intelligent amplifier multiplexers 162, 165 of the probe assembly 150 can operate by generating a non-dispersive zero-order shear-horizontal wave through a GW transducer 180B when activated. As a result, the monitoring controller 130 detects localized corrosion occurring near a LW transducer 170, near the GW transducer 180B, without requiring or storing a thickness map.

[0100] Reference Figure 2The system includes a probe assembly 150 including a memory 164 storing computer-executable instructions that, when executed by a controller 163 of the probe assembly 150, cause the probe assembly to perform one or more method steps. In one embodiment, the probe assembly 150 may include a low-frequency (LW) transducer 170 and a plurality of large-wavelength (GW) transducers 180A, 180B, 180C, and 180D. Upon receiving a smoothed low-frequency waveform signal via a transmit channel 136, the smart amplification multiplexers 162 and 165 activate a first GW transducer 180A. In response to this activation, a different GW transducer 180B in the probe assembly 150 may measure a first echo signal received through the material of the component shared by the GW transducers 180A and 180B. The first echo signal has a specific time-of-flight and signal characteristics (e.g., amplitude) that are detected by the GW transducer 180B. Furthermore, to improve the accuracy of localized corrosion detection, in some embodiments, another GW transducer 180C on the same component may also measure an echo signal. This echo signal may differ from the echo signal received by the other GW transducer 180B, taking into account their distance from the GW transducer 180A activated by the waveform signal. The echo signal received by the GW transducer 180C has its own time of flight and signal characteristics (e.g., amplitude).

[0101] An amplifier 166 and controller 163 in probe assembly 150 process the signals detected by GW transducers 180B and 180C into a combined echo signal by baseline subtraction and digital filtering. In some examples, the digital filtering includes bandpass filtering to reduce high-frequency noise. The combined echo signal is transmitted to monitoring controller 130 via receive channel 137.

[0102] Figure 24A and Figure 24B 1 shows waveforms generated using a low frequency guided wave (GW) transducer according to various embodiments disclosed herein. In particular, Figure 24A An illustrative measurement on channel 5 using a GW transducer (e.g., a Fraunhofer IKTS transducer) at 120 kHz, 16 bits, and sampling at 2 MSPS is shown in accordance with various embodiments disclosed herein. Likewise, Figure 24B An illustrative measurement on channel 5 using a GW transducer (eg, a Fraunhofer IKTS transducer) at 120 kHz, 16 bits, sampling at 2 MSPS is shown in accordance with various embodiments disclosed herein. Figure 24A and Figure 24B The advantages of signal averaging are shown in Figure 2. Figure 24BAs shown, the waveforms compare the results with and without signal averaging. For example, Figure 24A The blue line in the figure corresponds to a waveform without averaging, while the orange line corresponds to a waveform averaged four times. Figure 24A As shown, signal averaging helps reduce Figure 2 background noise in the system.

[0103] Figure 25A and Figure 25B Also shown are waveforms generated using a low frequency guided wave (GW) transducer according to various embodiments disclosed herein. In particular, Figure 25A An illustrative measurement on channel 3 using a GW transducer (such as a PI transducer) at 120 kHz, 16 bits, and sampling at 2 MSPS is shown according to various embodiments disclosed herein. Similarly, Figure 24B 24 shows an illustrative measurement on channel 3 using a GW transducer (such as a PI transducer) at 120 kHz, 16 bits, and sampling at 2 MSPS according to various embodiments disclosed herein. Figure 25A and Figure 25B The waveform signal shown in FIG can be used to reduce the Figure 2 background noise in the system.

[0104] Figure 26A and Figure 26B Shown is a difference between a baseline pipe and a pipe with a defect of varying depths according to some embodiments disclosed herein. Figure 26A A time series is shown in which a GW transducer (e.g., "transmitter 9," which may correspond to GW transducer 180A) generates a 125 kHz non-dispersive zero-order shear horizontal wave that is read by another GW transducer (e.g., "receiver 16," which may correspond to GW transducer 180B) on the same pipeline. The baseline wave signal (represented by the black wave) corresponds to the signal received on a baseline pipeline component; at the same time, parallel wave signals corresponding to a pipeline component with a defect of 10% depth size (represented by the blue wave), a defect of 20% depth size (represented by the red wave), and a defect of 50% depth size (represented by the green wave) are shown next to the baseline wave signal. Holes (e.g., localized corrosion) cause Figure 26A An additional echo in the time series is shown. The larger the size / depth of the localized corrosion, the greater the amplitude of the echo.

[0105] Likewise, Figure 26BA time series is shown in which a GW transducer (e.g., "transmitter 9," which may correspond to GW transducer 180A) generates a 125 kHz non-dispersive zero-order shear horizontal wave that is read by another GW transducer (e.g., "receiver 16," which may correspond to GW transducer 180B) on the same pipe. However, as a difference in the time series, Figure 26B The baseline wave signal in the figure (shown as a black wave) is juxtaposed with the signals corresponding to a pipe component with a defect at 10% of the depth size (shown as a blue wave), a defect at 20% of the depth size (shown as a red wave), and a defect at 50% of the depth size (shown as a green wave). In this way, the baseline subtraction is more clearly visualized in Figure 26B to show the echo caused by localized corrosion on the pipeline.

[0106] As explained in International PCT Patent Application PCT / US2020 / 063683 (Docket No.: MX-2019-PAT-0423-WO-PCT|BW007592.00491), filed on December 8, 2020, which is incorporated herein by reference, aspects of the present disclosure relate to monitoring and detection of corrosion and / or erosion of pipes, vessels and other components in an industrial facility. The monitoring system may include a software platform for remote monitoring and analysis of historical measurements collected by multiple sensors attached to pipes and components. The monitoring system may include analytical tools for monitoring, diagnosis and / or prediction of localized corrosion and / or general corrosion. By employing the analytical system disclosed herein, thickness monitoring locations (TMLs) can be optimized to, among other things, reduce the number of measurement locations without risk of compromise, i.e., down-selecting. As explained in this disclosure, through screening, by strategically reducing the number of probe components that need to be sampled during an inspection, the amount of time / cost of an inspection is reduced while maintaining (or even reducing) the risk profile of the industrial facility.

[0107] Figure 23 Shows some embodiments disclosed herein Figure 1A simplified piping and instrumentation diagram (PID) corresponding to an illustrative corrosion / erosion monitoring system is shown. The simplified PID 2300 includes a number of probe assemblies, shown as circles, numbered 19 through 84. For example, three different / distinct probe assemblies 150A, 150B, and 150C are shown. Of course, the number of probe assemblies in PID 2300 can be any number as appropriate. In one example, as explained herein, a human operator / inspector can focus inspection on a screened list of TMLs. These screened TMLs can represent more effective candidate measurement locations to capture the overall corrosion behavior of the entire asset while still being able to inspect for localized corrosion. For example, by screening the number of TMLs, significant time / energy and cost can be saved so that only those probe assemblies that are most likely to detect localized corrosion are reviewed by the human operator / inspector. Rather than inspecting all probe assemblies from the 19th to the 84th probe assemblies, or even randomly inspecting fewer than all probe assemblies from the 19th to the 84th probe assemblies, screened TMLs are a better way to identify which TMLs to measure. In some examples, an inspector may use a handheld or other manual device to measure the wall thickness at numbered locations on simplified PID 2300. In other examples, rigs or harnesses of the same sort may be pre-installed at numbered locations on simplified PID 2300, allowing the inspector to measure the wall thickness at each thickness measurement location. In yet another example, the inspector may be an automated machine that performs measurements at screened TMLs at specific time intervals. Even in an automated measurement system, screening TMLs is advantageous because it reduces the processing power and network bandwidth required for measurement data generated by a measurement device at each numbered location on simplified PID 2300. For example, some large industrial facilities may have thousands of probe assemblies, which would result in an excessive amount of data being generated. Furthermore, once any localized corrosion is identified and repaired, a human operator can indicate as much, and any models can be updated to reflect the new wall thickness values. Additionally, in some examples, if a localized corrosion is incorrectly identified, supervisory input into a machine learning or neural network executed in a digital analytics platform can refine its alerts and models accordingly.

[0108] Figure 13A An illustrative pipeline is shown with multiple sensors 1301 installed on a pipe according to one or more aspects of the features disclosed herein. The pipe may have a flow of a liquid in the direction indicated by the arrow. During an inspection, an approach may be to Figure 13AEach of the sensors 1-6 shown in FIG. 1 is inspected and measured. In another example, a randomly selected sensor can be inspected and measured. According to the various systems and methods disclosed herein, in another example, multiple thickness monitoring locations (TMLs) shown at each of the sensors 1-6 can be intelligently considered, and a smaller / narrower set of TMLs can be selected for inspection. Furthermore, according to the various systems and methods disclosed herein, during the TML screening process, the TMLs can be grouped based on one or more criteria. In a simplified example, the screening criteria can identify and exclude sensors that have historically measured only general corrosion within their region (e.g., sensors 1 and 3). Thus, by screening, system 100 avoids clustering and instead uses grouping to screen out sensors that are redundant for estimating the health of the mechanical component. This saves time and resources. Conversely, some existing systems attempt to reduce risk by adding more TMLs and inspecting these TMLs. However, the risk-based inspection (RBI) approach described in various aspects of the present disclosure provides a superior process and system. An RBI approach may also employ a model that takes into account other criteria, such as the type of fluid being transported in the pipeline system, the temperature inside and outside the pipe / component, the bends / configuration of the pipeline components, and other criteria. For example, a measurement at a bend may be weighted to be more likely to be selected as a screened portion of a group because, historically, locations near a bend in a pipeline will be areas with more turbulence and friction, and thus, likely higher levels of corrosion and acidity.

[0109] 13B , the probe assembly 1302 may comprise a tethered device for capturing accurate point measurements of the thickness of a component. In another embodiment, the probe assembly may comprise a tethered device for capturing accurate point measurements and area monitoring. For example, the device in FIG13B or a comparable device may be used to capture area monitoring of the thickness of a pipe component. In yet another embodiment, the probe assembly 2 may comprise a wireless device that captures accurate point measurements without requiring direct contact with a pipe component requiring thickness monitoring. The probe assembly may comprise one or more thickness monitoring ultrasonic transducers, area monitoring ultrasonic transducers, and / or combinations thereof, configured to validate general corrosion in a pipeline system (e.g., to confirm that no localized corrosion is detected).

[0110] FIG13B is a diagram of an illustrative pipeline with installed sensors. Sensor 1302 can be any of a variety of sensor types, configured to measure the thickness of the pipeline at or near the point on the pipeline where sensor 1320 is installed. Sensor 1302 is typically installed in a permanent location and remains attached to the pipeline for an extended period of time (e.g., for the lifespan of the pipeline circuit, for more than five years, for more than three years, or for another period of time). Although FIG13B shows sensors 1302 installed on the exterior of the pipeline and tethered with wires, in some examples according to one or more aspects of the present disclosure, the sensors may be untethered and wirelessly transmit data to one or more wireless receiver / transceiver devices. Additionally, although FIG13B shows sensors in a linear pattern along the longitudinal length of the pipeline, the present disclosure contemplates sensors installed in any of a number of different patterns. For example, the density of installed sensors may be based on the direction of gravity and the type of material being transported in the pipeline. For example, assume in one example that the pipeline in FIG13B transports a liquid from left to right along the length of the pipeline, with the bottom of the pipeline being the portion of the pipeline where sensors 1302 are installed. In such an example, the sensors installed on the pipeline can be distributed around the circumference of the pipeline, taking into account that weather conditions (e.g., rain, hail, sun) may expose a portion of the pipeline to be more likely to deteriorate while internal conditions of the pipeline (e.g., more liquid contacting the bottom of the pipeline than the top of the pipeline) may make the interior portion of the pipeline more likely to deteriorate.

[0111] Figure 14 is an illustrative network architecture of an industrial facility with sensors, communication components, and other components according to various aspects of the present disclosure. A data analysis platform 112 can be communicatively coupled to one or more networked components via a network, such as a local area network 1408. For example, the data analysis platform 112 can output to a visualization platform 114 for generating one or more illustrative charts included herein. A monitoring system may include a software platform 112 to remotely monitor and analyze historical measurements collected by multiple sensors attached to pipes and components. The monitoring system may include analytical tools for monitoring, diagnosing, and / or predicting areas that are candidates for localized corrosion (e.g., because the system cannot confirm generalized corrosion in the area). By employing the analytical system disclosed herein, TML can be optimized to, among other things, reduce the number of measurement locations without risk of compromise, i.e., screening.

[0112] In another example, the data analysis platform 112 may trigger the generation of an alarm at a remote alarm device 1410. The remote alarm device 1410 may cause an immediate inspection of one or more components, or cause specific pipeline components to be prioritized for a subsequent inspection of the facility.

[0113] As measurements and other data are collected by system 1400, the data may be stored in a data store 1406 that is communicatively coupled to and accessible to data analysis platform 112. In some examples, the data may be stored in computer memory 1404, however, the amount of computer memory required may be high. Instead, in some examples, a model 1412, such as a machine learning artificial neural network, may be stored in computer memory 1404 for execution by a processor 1402, while historical data and other data may be stored in a data store 1406. In some examples, the data store may be relocated to platform 112, but for illustrative purposes, the data store is shown as being in communication with platform 112 via local area network 1408.

[0114] Figure 15 FIG. 1 is an illustrative diagram of a grouping of multiple sensors (eg, probe assemblies) in accordance with an embodiment of the present disclosure. Figure 15 As shown, each probe assembly can be assigned a unique TML identifier (TML ID). The TML ID can be any unique letter, character, or other identifier to uniquely identify each TML (i.e., probe assembly). Figure 15 , the bold rectangle surrounding the selected TML ID number illustrates the grouping of probe assemblies. At 1502, on March 7, 2007, the system has grouped probe assemblies 4, 5, 6, and 7 into a single group based on one or more rules. At 1504, on March 7, 2008, a graphical representation of the data stored in computer memory 1404 illustrates that system 1400 has adjusted the grouping to include / exclude one or more TMLs. At 1504, the model may suggest that the probe assembly corresponding to TML ID number 4 should no longer be part of the group ID corresponding to the bold rectangle in 1504. Consequently, one or more probe assemblies selected for that group ID may also change. Finally, at 1506, on March 7, 2009, a graphical representation illustrates that system 1400 has further adjusted the grouping to now group probe assemblies 5 and 6 into a first group ID and probe assemblies 7 and 8 into a different / independent second group ID. As a result, as discussed below with reference to FIG. 16 , the screening and risk profile for the entire system 100 will change.

[0115] In one example, grouping TMLs into a set ID can be accomplished in one of several different ways. For example, the initial grouping of circuits for multiple components at a facility can be based on measurement data levels. For each date that a probe assembly is measured, a new grouping can be triggered if a probe assembly meets any of the following conditions: (i) if the probe assembly is the first TML for that circuit; (ii) if the (absolute) difference between the measured value and the measured value of the previous TML exceeds approximately 0.5 to 3.0 standard deviations of all measurements for that date, the value of this parameter can be decreased for a more conservative grouping or increased for a more aggressive grouping; (iii) if the nominal wall thickness measurement of the TML is different than the nominal wall thickness measurement of the previous TML, or if the TML has only one measurement value historically (across all dates). In another example, grouping of TMLs can be accomplished in a multi-step process. In a first step, all measurements taken on a group of connected components (e.g., a circuit) on a specific date (or any other predetermined time period, e.g., within a one-hour window, within the same week, or other period) can be compared to determine how many pairs (or tuples) were measured on that specific date. In one example, any TML pair with a lower measurement than a predetermined percentage (e.g., 70%, 80%, 60%, 75%, or other percentage) of the total measurements during the survey year (or other time period) is eliminated. Next, the minimum measurement of all TMLs can be identified and all TMLs grouped with that TML in an earlier (e.g., first) step are assigned to the same group ID. Other examples of rules for grouping TMLs will become apparent to those skilled in the art after reading the entire disclosure herein.

[0116] Other illustrative rules for grouping TMLs are also contemplated in the present disclosure. For example, in some rules, groups can be reallocated based on the percentage of TMLs to groups. For a circuit of multiple components with at least two measurement dates, groups of TMLs that are grouped together must be at least a predetermined threshold percentage of the two dates to remain in the same group, but TMLs that do not meet this threshold can be individually assigned to different groups using one or more rules. In another example, measurement dates that do not have enough TMLs can be dropped. For each circuit of multiple components, in some examples, system 1400 can consider only those measurement dates that have at least a predetermined threshold percentage of the maximum number of TMLs for any date. TMLs that appear on dates that do not meet this threshold can be individually assigned to independent groups.

[0117] In some examples, system 1400 may discard (e.g., abandon) seemingly invalid measurements based on a lack of historical data and continue to regroup TMLs based on one or more of the rules described herein. The thresholds employed are hyperparameters that can be adjusted based on the diversity and quality of the dataset. This adjustment can be made at the end of the data validation and verification process. In one example, the threshold percentage may be set to 75%, but for some TMLs, previous measurements may not have occurred in many years. In some embodiments, a hyper-grid may be generated and used to adjust the parameters and / or hyperparameters of system 1400. In some examples, the threshold setting may be strongly correlated to how many TML measurements a system 1400 has collected for each TML ID. Thus, the threshold can be adjusted up or down based on how much data is available to system 1400.

[0118] 16 and 17 illustrate graphs of various data collected and / or analyzed by system 1400 . Figure 16A A probability plot (normal to 95%) of the measured values ​​is shown, with the percentages on the Y-axis and the measured values ​​on the X-axis. The system 1400 assumes by default that general corrosion has been detected, except when the curve is shown to not be running vertically (such as in Figure 16A Data analysis platform 112 can verify that the pipe wall thickness measurement made by the probe assembly indicates general corrosion rather than localized corrosion by performing one or more steps. For example, in some embodiments, verification is performed by generating a probability map of all pipe wall thickness measurements associated with the pipeline system, grouping the plotted pipe wall thickness measurements by nominal thickness, and identifying a nonlinear relationship in the probability map of the pipe wall thickness measurements grouped by nominal thickness to confirm that the corrosion is likely not general corrosion. Furthermore, if the map shows a linear relationship, the TML corresponding to those data points in the map indicates general corrosion. This approach is an improvement over systems that already use standard deviation to create normal probability plots. Furthermore, the verification step provides further assurance that system 1400 is accurately detecting general corrosion and, accordingly, acting to screen appropriate probe assemblies for components installed in the facility. System 1400 should not generate an alarm (e.g., from device 1410) for general corrosion because general corrosion is ubiquitous and typically not a primary concern during inspections. Conversely, general corrosion is considered in planning and scheduling mass replacement of components in a facility.

[0119] Reference Figure 16B and Figure 16C , these figures show the relationship between the risk of falsely identifying general corrosion and the number of thickness measurement locations (TMLs). Although the amount of risk is asymptotic to a minimum risk threshold 1602 regardless of the number of measurement locations, Figure 16B The risk level plotted against the number of probe assemblies (ie, TMLs) decreases as more TMLs are added. Figure 16C middle, Figure 16C A shift after screening is shown in the curve plotting risk level versus number of TMLs. Figure 16B and Figure 16C Will be combined below Figure 22 17 is a diagram showing a cumulative thickness distribution of naphthenic acid corroded pipes in a prior art system known in the art.

[0120] Figure 18A is a corrosion sensor analysis showing TML measurements by date for a particular circuit ID (or asset ID). The X-axis corresponds to the TML identifier. For practical purposes, probe assemblies installed on a pipeline system may be assigned identifiers in a sequential or otherwise ordered order along the circuit formed by the pipeline system. Each TML may have an ID giving its upstream or downstream location on the pipeline. Additional data cleaning and / or scrubbing of the TML based on the location data may be performed to harmonize / standardize the measured data for analysis. Each TML may be assigned a nominal thickness from the time the pipeline was first installed. One or more publicly accessible databases (e.g., the Meridian database) may provide data, including measurements and specifications of nominal thickness. Also, as Figure 18A As shown in the legend on the right hand side, measurements can be taken over a period of time so that historical data spanning at least several years (i.e., an extended period of time) can be stored and analyzed. In this example, nearly 25 years of wall thickness measurement data are stored, analyzed, and plotted. Figure 18A middle. Figure 18A The graph plot 1802 in FIG. 1 corresponds to a measurement taken on August 3, 2015. Meanwhile, the other graphs in the figure correspond to thickness measurements taken by various TMLs on corresponding dates spanning nearly 25 years backward (e.g., an extended period of time).

[0121] The data analysis platform 112 can set the model 1412 Figure 18AThe graphs plotted in correspond to one or more hyperparameters. A hyperparameter is typically set before starting the training / learning process on the model; in contrast, the values ​​of other parameters are learned through the training of the model. Figure 18A In FIG, a graphical user interface for adjusting the group sensitivity hyperparameter is displayed at the top. The visualization platform 114 may include a graphical tool / slider through which the hyperparameter can be adjusted. Figure 18A , the grouping_sensitivity hyperparameter is shown set to the "standard" setting. Figure 18B In , the group sensitivity hyperparameter is shown set to a "medium" setting. As a result, the number of groups is Figure 18B There are only 61 in Figure 18A In addition, due to the changes in hyperparameter settings and TML selection methods, Figure 18B The figure 1812 drawn in Figure 18A The graph 1802 in is slightly different. In addition, for Figure 18C The group sensitivity hyperparameter is set to "high", Figure 18C The graph drawn in 1822 is increasingly different from Figure 18A and Figure 18B The number of groups was approximately seventy-five, while the total number of TMLs remained constant at one hundred and fifty-five.

[0122] The grouping sensitivity hyperparameter refers to the sensitivity or aggressiveness of the TML grouping and can be applied in the initial grouping stage. In some examples, when the (absolute) difference between the measurement value of a TML and the measurement value of the previous TML is greater than 1 standard deviation (SD) of all measurements for that day, the TML can be assigned to a new group. This threshold can be adjusted for more conservative or more aggressive grouping. A threshold value of less than 1 SD will cause the grouping to be more sensitive to changes in measurements and will result in a more conservative grouping. On the other hand, a threshold value greater than 1 SD will cause the grouping to be less sensitive to changes in measurements and will result in a more aggressive grouping (e.g., a higher grouping ratio). In one example, five different grouping sensitivities can be implemented, such as Figure 18CAs shown, the sensitivity decreases from the most conservative to the most aggressive as follows: high (0.5SD), standard (1SD), medium (1.5SD), low (2SD), and very low (3SD). In another example, above or below the above five groupings can be used to provide more refined or coarse sensitivity. Since the group sensitivity hyperparameters are applied in the initial grouping stage, just like the case hyperparameters, all subsequent grouping steps can be rerun based on the initial grouping results, that is, the entire grouping cycle is repeated five times, once for each of the five grouping sensitivity levels.

[0123] Note that, Figure 18A 、 Figure 18B and Figure 18C (collectively referred to as "Figure 18") lists multiple TML selection methods that can be applied to measurements to optimize the grouping and plotting of data points. Although Figure 18 lists three optimization functions, namely the median of the TML within a group ID (median_TML_within_groupID), the minimum average of the TML within a group ID (minimum_average_TML_within_groupID), and the minimum deviation from the mean (minimum_variation_from_mean), other optimization functions may be used according to one or more aspects of the present disclosure. For example, a TML position (TML_position) optimization function may be used, where if one TML is to be selected, the TML at the center of the group is selected. If two TMLs are to be selected, the group is divided into two subgroups and the TML at the center of each subgroup is selected, and so on. Other examples of TML selection methods are also considered herein. For example, the optimization function may be a minimum_average_TML_within_groupID optimization function. In the minimum_average_TML_within_groupID method, which is used to determine which TMLs to select from each group, the method selects the TML with the lowest average measure within each group (across dates). For example, in one illustrative system employing the minimum_average_TML_within_groupID optimization function, the system can calculate the average measure for each TML (across dates), sort the TMLs in each group by the average measure (e.g., ascending), and select the top n TMLs based on the number (n) of TMLs to be selected from each group. Similarly, the median_TML_within_groupID optimization function is similar to the minimum_average_TML_within_groupID optimization function, but is based on the median rather than the minimum average.

[0124] In another example, the optimization function can be a minimum_variation_from_mean optimization function. In the minimum_variation_from_mean method used to determine which TMLs to select from each group, the method selects the TML with the lowest average variation compared to the mean measurement of the group. For example, in an illustrative system using the minimum_variation_from_mean optimization function, the system can calculate a mean group measurement for each date. Then, for each TML, the absolute difference from the mean is calculated for each date, and for each TML, the average deviation (e.g., the absolute difference from the mean) is calculated. Next, the minimum_variation_from_mean optimization function sorts the TMLs in each group by the average deviation (e.g., in ascending order) and selects the top n TMLs based on the number (n) of TMLs to be selected from each group.

[0125] After finalizing the grouping, system 1400 determines a TML candidate selection method and the number of probe assemblies required for each group. The number of candidates per group can be another hyperparameter. By default, system 1400 can use the larger of 1% or 1 for each TML group. If more than one TML candidate is to be selected, the TML group can be divided into equally large subgroups while preserving the order of the TMLs. System 1400 can then apply a TML candidate selection method based on one or more scenarios described herein.

[0126] Figure 19A 1902 is a graph showing the measured TC thickness (mm) of a component versus time. In addition, the graph may provide the temperature calibration value (degrees Celsius), the temperature coefficient (e.g., 1%), the corrosion rate ST (mm per year), the corrosion rate LT (mm per year), the remaining life of the component (years), the remaining half-life (also years), and the actual thickness (mm).

[0127] in addition, Figure 19B 19 is a rectified graph showing FSH (percentage) values ​​versus thickness values ​​(mm or other units). The graph also shows the thickness ranges for Gate A 1904 and Gate B 1906. Alternatively, the graph can be plotted using mV values ​​instead of FSH. Furthermore, in some examples, the graph can be displayed as HF rather than rectified.

[0128] Figure 20A2002 is a graph of an acid battery facility showing the measured TC thickness (mm) of a component versus time. In addition, the graph may give the temperature calibration value (degrees Celsius), the temperature coefficient (e.g., 1%), the corrosion rate ST (mm per year), the corrosion rate LT (mm per year), the remaining life of the component (years), the remaining half life (also years), and the actual thickness (mm). In addition, Figure 20B 2 is a graph showing the calibration of an acid battery facility by plotting FSH (percentage) values ​​versus thickness (in millimeters or other units). The graph also shows the thickness ranges for gate A 2004 and gate B 2006. Alternatively, the graph can be plotted using mV values ​​instead of FSH. Furthermore, in some examples, the graph can be displayed as HF rather than calibrated.

[0129] Figure 21 A simplified example of an artificial neural network 2100 is shown on which a machine learning algorithm can be executed. Figure 21 This is merely one example of nonlinear processing employing an artificial neural network; other forms of nonlinear processing may be used to implement a machine learning algorithm based on the features described herein.

[0130] exist Figure 21 In FIG, each of the input nodes is connected to a first set of processing nodes. The external source 2102 fed into the input nodes may be metrics from the results of the steps of the method disclosed herein. Each of the first set of processing nodes is connected to each of the second set of processing nodes. Each of the second set of processing nodes is connected to each of the output nodes. Although only two sets of processing nodes are shown, any number of processing nodes may be implemented. Similarly, although Figure 21 Each group in the has only four input nodes, five processing nodes, and two output nodes, but any number of nodes can be implemented per group. Figure 21 21. The data flow in FIG. 21 is shown from left to right as follows: data can be input to an input node, can flow through one or more processing nodes, and can be output by an output node. Input to the input node can come from an external source 2102. Output 2104 can be sent to a feedback system 2106 and / or data storage. Feedback system 2106 can send output to the input node for successive processing iterations using the same or different input data.

[0131] In an illustrative method using feedback system 2106, the system may use machine learning to determine an output. The output may include a leakage area boundary, a multi-sensor detection event, a confidence value and / or a classification output. The system may use a suitable machine learning model, including an xg-boosted decision tree, an autoencoder, a perceptron, a decision tree, a support vector machine, a regression and / or a neural network. The neural network may be a suitable type of neural network, including a feedforward network, a radial basis network, a recurrent neural network, a long / short term memory, a gated recurrent unit, an autoencoder, a variable autoencoder, a convolutional network, a residual network, a Kohonen network and / or other types. In one example, the output data in the machine learning system may be represented as a multidimensional array, an extension of a two-dimensional table (such as a matrix) to data with higher dimensions.

[0132] A neural network may include an input layer, a plurality of intermediate layers, and an output layer. Each layer may have its own weights. The input layer may be configured to receive as input one or more feature vectors disclosed herein. The intermediate layers may be convolutional layers, pooling layers, dense (fully connected) layers, and / or other types. The input layer may pass input to the intermediate layers. In one example, each intermediate layer may process the output from the previous layer and then pass the output to the next intermediate layer. The output layer may be configured to output a classification or a real value. In one example, the layers in the neural network may use an activation function such as a sigmoid function, a hyperbolic tangent function, a rectified linear function, and / or other functions. In addition, the neural network may include a loss function. In some examples, a loss function may measure the number of missed positive examples; alternatively, the loss function may measure the number of false positive examples. The loss function can be used to determine the error when comparing an output value to a target value. For example, when training a neural network, the output of the output layer may be used as a prediction and compared to a target value of a training example to determine an error. The error may be used to update the weights of each layer of the neural network.

[0133] In one example, the neural network may include a technique for updating the weights of one or more of the layers based on the error. The neural network may use gradient descent to update the weights. Alternatively, the neural network may use an optimizer to update the weights in each layer. For example, the optimizer may use various techniques or a combination of techniques to update the weights in each layer. Where appropriate, the neural network may include a mechanism to prevent overfitting - regularization (such as L1 or L2), dropout, and / or other techniques. The neural network may also increase the amount of training data used to prevent overfitting.

[0134] In one example, Figure 21The illustrated nodes can perform various types of processing, such as discrete calculations, computer programming, and / or mathematical functions implemented by a computing device. For example, an input node can include logical inputs from different data sources, such as one or more data servers. A processing node can include parallel processing performed on multiple servers in a data center. And, an output node can be a logical output that is ultimately stored in a result data store (such as the same or different data server as the input node). In particular, these nodes need not be distinct. For example, two nodes in any two sets can perform exactly the same processing. The same node can be repeated for the same or different sets.

[0135] Each node can be connected to more than one other node. The connection can connect the output of one node to the input of another node. A connection can be associated with a weighted value. For example, one connection can be weighted as more important or significant than another connection, thereby affecting the degree of further processing as the input traverses the artificial neural network. Such connections can be modified so that the artificial neural network 2100 can learn and / or dynamically reconfigure. Although the nodes are shown as having connections only for Figure 21 Connections are formed between any nodes, but connections can be formed between any nodes. For example, a processing node can be configured to send its output to a previous processing node.

[0136] Input received at an input node may be processed by processing nodes such as a first set of processing nodes and a second set of processing nodes. This processing may result in an output at an output node. The processing may include multiple steps or sequences, as indicated by the connections from the first set of processing nodes and the second set of processing nodes. For example, the first set of processing nodes may be a coarse data filter, while the second set of processing nodes may be a more detailed data filter.

[0137] Artificial neural network 2100 can be configured to perform decision making. As a simplified example for explanatory purposes, artificial neural network 2100 can be configured to detect faces in a photograph. The input nodes can be provided with a digital copy of a photograph. A first group of processing nodes can each be configured to perform specific steps to remove non-face content, such as large, continuous red portions. A second group of processing nodes can each be configured to find a rough approximation of a face, such as face shape and skin color. Multiple subsequent groups can further refine this processing, each group finding further, more specific tasks, where each node performs some form of processing that is not necessarily required to facilitate that task. Artificial neural network 2100 can then make a prediction about that part of the face. This prediction may or may not be correct.

[0138] Feedback system 2106 can be configured to determine whether artificial neural network 2100 made a correct decision. Feedback can include an indication of a correct answer and / or an indication of an incorrect answer and / or a degree of correctness (e.g., a percentage). For example, in the face recognition example provided above, feedback system 2106 can be configured to determine whether a face was correctly identified and, if so, what percentage of faces were correctly identified. The feedback system may already know the correct answer, so that the feedback system can train artificial neural network 2100 by indicating whether it made a correct decision. The feedback system can include human input, such as an administrator telling artificial neural network 2100 whether it made a correct decision. The feedback system can provide feedback (e.g., an indication of whether a previous output was correct or incorrect) to artificial neural network 2100 via input nodes or can transmit such information to one or more nodes. The feedback system can additionally or alternatively be connected to a memory so that the output is stored. The feedback system may not have a correct answer at all, but rather provide a basis for further processing: for example, the feedback system may comprise a system programmed to recognize faces, whereby the feedback allows the artificial neural network 2100 to compare its results with those of a manually programmed system.

[0139] The artificial neural network 2100 can be dynamically modified to learn and provide better input. For example, based on previous input and output and feedback from the feedback system 2106, the artificial neural network 2100 can modify itself. For example, the processing in the nodes can be changed and / or the connections can be weighted differently. Continuing with the example provided above, the face prediction may be incorrect because the photos provided to the algorithm are colored in a way that makes all faces appear red. Thus, a node that excludes photos with a large number of red adjacent parts may be considered unreliable, and the connection to the node may be significantly reduced in weight. Additionally or alternatively, the node can be reconfigured to process the photos in a different manner. The modification can be through predictions and / or guesses made by the artificial neural network 2100, so that the artificial neural network 2100 can change its nodes and connections to test hypotheses.

[0140] The artificial neural network 2100 need not have a set number of processing nodes or a set number of processing nodes, but may increase or decrease its complexity. For example, the artificial neural network 2100 may determine that one or more processing nodes are unnecessary or should be repurposed and either discard or reconfigure the processing nodes based on that. As another example, the artificial neural network 2100 may determine that further processing of all or part of the input is required and, based on that, attach additional processing nodes and / or sets of processing nodes.

[0141] The feedback provided by the feedback system 2100 can be simply reinforcement (e.g., providing an indication of whether the output is correct or incorrect, rewarding the machine learning algorithm with a number of points, etc.) or can be specific (e.g., providing a correct output). For example, a machine learning algorithm may be asked to detect faces in a photograph. Based on an output, the feedback system may indicate a score (e.g., 75% accuracy, an indication that the estimate is accurate, etc.) or a specific response (e.g., specifically where the face was identified). In one example, a human operator / inspector may focus their inspection on a screened list of TMLs. Once any localized corrosion is identified and repaired, the operator may indicate as much so that the model 1412 can be updated to reflect the new wall thickness value. Additionally, in some examples, a localized corrosion may be incorrectly identified in the system 1400, and supervisory input to a machine learning or neural network executed in the digital analytics platform 112 may refine its alerts and models accordingly.

[0142] Artificial neural network 2100 may be supported or replaced by other forms of machine learning. For example, one or more of the nodes of artificial neural network 2100 may implement a decision tree, an associated rule set, logic programming, a regression model, a clustering analysis mechanism, a Bayesian network, a propositional formula, a generative model, and / or other algorithms or forms of decision making. Artificial neural network 2100 may implement deep learning.

[0143] Figure 22 2 is a flowchart showing illustrative steps of a method 2200 performed according to some embodiments disclosed herein. Method 2200 can be performed by a system 1400 when computer-executable instructions stored on a non-transitory computer-readable medium are executed by a processor. Among other things, method 2200 can screen probe assemblies installed on a pipeline system in an industrial facility. As a result, systems and methods for optimizing asset health monitoring are improved because representative measurement locations are identified through screening, and remaining probe assemblies can be disregarded during routine inspections of pipeline systems and other components in an industrial facility.

[0144] about Figure 22In step 2202, the system stores historical pipe wall thickness measurements collected from probe assembly 150A installed on a pipeline system in an industrial facility over a period of time in a computer memory 1406 communicatively coupled to processor 1402. In step 2204, data analysis platform 112 may set one or more hyperparameters, such as, but not limited to, a group sensitivity hyperparameter, a threshold measurement (threshold_measurements) hyperparameter, a group size (group_size) hyperparameter, and / or combinations thereof. Once these hyperparameters are set, in step 2206, system 1400 may begin training a model using at least the historical pipe wall thickness measurements stored in computer memory 1406 and the hyperparameter values ​​stored in computer memory 1404.

[0145] In step 2208, the model stored in the computer memory 1404 can group the first set of probe assemblies from the plurality of probe assemblies installed on the pipeline system. Figure 15 Several methods are provided by which groups can be generated in the model. After the probe components are grouped, the data analysis platform 112 can assign a unique group identifier (group ID) to each group of probe components. The unique group ID can be any identifier that the system 1400 can use to uniquely refer to a group of probe components.

[0146] In step 2210, the data analysis platform 112 selects an optimization function for operation of the system 1400 based on at least the trained model. Numerous illustrative optimization functions are described herein, including, but not limited to, a median_TML_within_groupID optimization function, a minimum_average_TML_within_groupID optimization function, a minimum_variation_from_mean optimization function, and / or a TML_position optimization function. The decision to select a particular optimization function triggers subsequent identification and measurement steps. For example, in steps 2212A, 2212B, 2212C, and 2212D (collectively, "step 2212"), the system 1400 identifies a probe assembly corresponding to each group ID for pipe wall thickness monitoring of the pipeline system based on the model stored in the computer memory 1404 and the selected optimization function. In some examples, the system 1400 may identify a single probe assembly for the entire group ID to represent the region being measured. In other examples, multiple probe assemblies may be identified to represent the group ID. The number of TMLs assigned to be screened from a group can be based on one or more rules. This is based on the maximum standard deviation of the group. In one example, if the maximum standard deviation of any group is less than or equal to 0.25, then one TML is selected from that group. If the maximum standard deviation increases by 0.25, then the number of TMLs selected increases by one (i.e., if it is between 0.25-0.5, then two probe assemblies are selected from the group, and so on). In addition, the step value of 0.25 can be modified to adjust the sensitivity of the TML selection. Values ​​below 0.25 will result in more TMLs being selected from each group (i.e., a conservative approach), while values ​​above 0.25 will result in fewer TMLs being selected from each group (i.e., an aggressive approach).

[0147] In step 2214, during the inspection, system 1400 may disregard all remaining probe assemblies in each group ID, except for the probe assemblies identified from each group ID. The system may measure the wall thickness of each probe assembly identified in each group ID, but exclude the other probe assemblies in that group ID. Thus, the system selects from a plurality of probe assemblies installed on a pipeline system. At least one benefit of using a selected number of probe assemblies during an inspection is the resulting time savings. For example, a human inspector who previously inspected each probe assembly can now inspect measurements on a reduced number of probe assemblies without significantly increasing the risk of missing dangerous localized corrosion. In one example, in step 2214, system 1400 may output a human-readable report listing those probe assemblies for which a human inspector should manually inspect wall thickness measurements. The report may be sorted in various ways, such as by highest risk for localized corrosion, by geographic convenience from the human inspector's known starting location, or in some other order.

[0148] For example, Figure 16B and Figure 16C As shown, when the risk is plotted against the number of measurement locations, the risk approaches a threshold of minimum risk 1602 regardless of how the number of measurement locations increases. Importantly, as the number of measurements decreases, as shown in graph 1604, the delta change in risk increases at an increasingly rapid rate. In other words, reducing the number of probe assemblies sampled increases the risk to an unsafe level. However, the system 1400 and method 2200 disclosed herein shift the graph from the initial risk graph 1606 to a more favorable risk graph 1608. Thus, by identifying those probe assemblies that are statistically most likely to cause general corrosion / degradation of the pipeline wall, the number of probe assemblies that need to be proactively inspected during an inspection is reduced, thereby reducing inspection time / cost while maintaining (or even reducing) the risk profile.

[0149] Finally, in Figure 22 In step 2216, the thickness monitoring controller 130 can receive and send the pipe wall thickness measurements from the probe assembly of each group ID for inspection. The thickness monitoring controller 130 can send the measurement data (and any other data) to the data storage 1406 for historical record retention and analysis, and to the data analysis platform 112 for analysis and visualization generation. For example, the wall thickness measurement may show that a particular section of pipe in the pipeline system is experiencing degradation other than general corrosion, such that it has risen to a dangerous, localized corrosion level and must be replaced within a specific time period. In another example, the pipe wall thickness measurement can be performed at one or more of a pipe, a tank, a container, and / or a pipeline at a facility.

[0150] Although specific embodiments are shown in the drawings and described with respect to the drawings, it is conceivable that those skilled in the art may devise various modifications without departing from the spirit and scope of the appended claims. Therefore, it will be appreciated that the scope of the present disclosure and the appended claims is not limited to the specific embodiments shown in the drawings and described with respect to the drawings, and modifications and other embodiments will be included within the scope of the present disclosure and the appended drawings. In addition, although the foregoing description and the associated drawings illustrate exemplary embodiments in the context of certain exemplary combinations of elements and / or functions, it will be appreciated that different combinations of elements and / or functions may be provided by alternative embodiments without departing from the scope of the present disclosure and the appended claims. In addition, the foregoing description describes a method for describing the execution of multiple steps. Unless otherwise stated, one or more steps in a method may not be required, one or more steps may be performed in an order different from that described, and one or more steps may be formed substantially simultaneously. Each aspect can have other embodiments and can be implemented or executed in a variety of different ways. Furthermore, the present disclosure contemplates a non-transitory computer-readable medium storing computer-executable instructions that, when executed by a controller, cause a probe assembly including a smart amplifier multiplexer to perform the steps of the method disclosed herein.

[0151] It will be understood that the words and terms used herein are for descriptive purposes and should not be construed as limiting. Rather, the words and terms used herein are to be given their broadest interpretations and meanings. The use of "including" and "comprising" and variations thereof is intended to encompass the items listed thereafter and their equivalents, as well as additional items and their equivalents. Furthermore, while transducer is used throughout the disclosure, that term is used interchangeably with the term sensor, as both are used to sense changes in an environment they surround or an object to which they are attached, and their output is immediately and / or ultimately converted into a format that can be used by one or more computer systems disclosed herein. Additionally, while the term processor is used throughout the disclosure, that term is used interchangeably with the term controller, as both are used to execute computer-executable instructions to cause a hardware device to operate in a particular manner or perform a particular function. Likewise, although a memory is sometimes described as storing the aforementioned computer-executable instructions executed by a processor or controller, those skilled in the art, after reviewing the entire disclosure, will recognize that the computer-executable instructions may be hard-coded into the controller or processor, for example, in the form of an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or the like.

Claims

1. A system for detecting localized corrosion near a longitudinal wave (LW) transducer attached to a component transporting material across a distance, the system comprising: a probe assembly comprising the LW transducer and one or more guided wave GW transducers attached to the component at a location proximate to the LW transducer, wherein the one or more GW transducers are activated by an analog waveform signal; The probe assembly further includes a smart amplifier multiplexer with a high current and high voltage input, the smart amplifier multiplexer configured to selectively activate one of the LW transducer and one of the one or more GW transducers; a transmission channel for transmitting signals from a monitoring controller to the probe assembly; a receiving channel for receiving a signal from the probe assembly at the monitoring controller; and The monitoring controller comprises: a pulser; an analog-to-digital converter (ADC); an adjustable gain amplifier; a processor and a memory storing computer executable instructions, which, when executed by the processor, cause the monitoring controller to perform steps comprising: generating a short spike signal by the pulser, the short spike signal traveling through the transmit channel to activate the LW transducer by the smart amplification multiplexer; generating a smooth low-frequency waveform signal by the same pulser, the low-frequency waveform signal traveling through the transmit channel to activate a first of the one or more GW transducers by the smart amplification multiplexer; In response to the generation of the short spike signal, receiving an indication of a point thickness measurement based on a time-of-flight measurement from the probe assembly via the receiving channel by the adjustable gain amplifier and the ADC converter; and In response to the generation of the smoothed low frequency waveform signal, an indication of localized corrosion using a change in a signal characteristic second only to the time of flight is received by the adjustable gain amplifier and the ADC converter via the receive channel.

2. The system of claim 1, wherein: The pulser includes a digital switch configured to transmit a predetermined number of pulses of a predetermined voltage level.

3. The system according to claim 2, in, The pulser includes a high voltage high frequency pulser with a high voltage capacitor in a range of 0.7uF to 5.3uF. Furthermore, the predetermined number of predetermined voltage level pulses is 3, Furthermore, the predetermined voltage level pulses are 0V, 50V and -50V, Moreover, the frequency of the short spike signal is 5 MHz, Furthermore, the frequency of the smooth low-frequency waveform signal is 50-500 kHz.

4. The system of claim 1, wherein: The smart amplifier multiplexer of the probe assembly is configured to receive the signal passing through the transmit channel, filter the received signal, and route the received signal to one of the LW transducer and the first of the one or more GW transducers.

5. The system of claim 4, wherein: The intelligent amplifier multiplexer includes a switch component with a transmitting switch, a receiving switch and an amplifier.

6. The system of claim 4, wherein: The intelligent amplifier multiplexer includes a resistance temperature detector (RTD) interface.

7. The system of claim 1, wherein: The intelligent amplifier multiplexer of the probe assembly includes a low-pass filter that switches a signal received through the transmit channel to trigger the LW transducer or a first one of the one or more GW transducers, but not both types of transducers simultaneously.

8. The system according to claim 1, in, The probe assembly includes one LW transducer and four GW transducers. wherein each GW transducer is permanently attached to a portion of the component within three feet of the one LW transducer, Moreover, each GW transducer is an area monitoring ultrasonic transducer, Furthermore, wherein the one LW transducer is a thickness monitoring ultrasonic transducer.

9. The system of claim 8, wherein: The four GW transducers are arranged along a circumference of a circular shape, wherein the LW transducer is located near the center of the circular shape.

10. The system according to claim 1, in, The change in signal characteristics is a change in amplitude. Furthermore, the probe assembly includes a memory storing computer executable instructions, and when the stored computer executable instructions are executed by a controller of the probe assembly, the stored computer executable instructions cause the probe assembly to perform steps, the steps comprising: receiving a smooth low-frequency waveform signal passing through the transmission channel; activating the first of the one or more GW transducers by the smart amplifying multiplexer; In response to activation of the first of the one or more GW transducers, measuring, by a second of the one or more GW transducers, a first echo signal received through the component, wherein the first echo signal has a first time of flight and a first amplitude; In response to activation of the first of the one or more GW transducers, measuring, by a third of the one or more GW transducers, a second echo signal received through the component, wherein the second echo signal has a second time-of-flight and a second amplitude; processing the first echo signal and the second echo signal into a combined echo signal by an amplifier and a controller of the probe assembly through baseline subtraction and digital filtering; and The combined echo signal is transmitted to the monitoring controller through the receiving channel.

11. The system of claim 1, wherein: The component is a can, and the material is a liquid material.

12. The system of claim 1, wherein: The component is a container, and the material is a gaseous material.

13. The system of claim 1, in, When said first one of said one or more GW transducers is activated, said first one of said one or more GW transducers generates a non-dispersive zero-order shear horizontal wave, And wherein the monitoring controller detects localized corrosion near the LW transducer without storing a thickness map.

14. The system of claim 1, wherein: The transmitting channel includes a first cable conductor, and the receiving channel includes a second cable conductor different from the first cable conductor.

15. The system of claim 1, wherein: The transmitting channel includes a first cable conductor, and the receiving channel includes a wireless communication channel.

16. A method for facilitating localized corrosion detection by a monitoring controller, the monitoring controller comprising a pulse generator, an analog-to-digital converter (ADC), an adjustable gain amplifier, a processor, and a memory storing computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the computer-executable instructions cause the monitoring controller to perform steps comprising: A short spike signal is generated by the pulser, and the short spike signal activates a longitudinal wave LW transducer attached to a fixed device through an intelligent amplification multiplexer; The pulser generates a smooth low-frequency analog waveform signal, and the smooth low-frequency analog waveform signal is activated by the smart amplification multiplexer to activate a first one of a plurality of guided wave GW transducers attached to the fixed equipment at a location proximate to the LW transducer; In response to the generation of the short spike signal, receiving, by the adjustable gain amplifier and the ADC converter, an indication of a thickness measurement of the fixture based on a time-of-flight measurement; and In response to the generation of the smoothed low frequency analog waveform signal, receiving, by the adjustable gain amplifier and the ADC converter, an indication of localized corrosion of the fixture utilizing a change in a signal characteristic second only to a time of flight; The smart amplifier multiplexer receives a high current and high voltage input and is configured to selectively activate one of the LW transducer and one of the plurality of GW transducers.

17. The method according to claim 16, in, The probe assembly's intelligent amplifier multiplexer includes a low-pass filter, and wherein, in response to the generation of the smoothed low frequency analog waveform signal, the first of the one or more GW transducers generates a non-dispersive zero-order shear horizontal wave, And wherein the monitoring controller detects localized corrosion occurring in the fixture without storing a thickness map.

18. The method according to claim 16, in, The change of signal characteristics is a change of amplitude. And wherein the pulser includes a digital switch configured to transmit a predetermined number of pulses of a predetermined voltage level, Moreover, the short spike signal is 5MHz, Furthermore, the smooth low-frequency analog waveform signal is 50-500 kHz.

19. A method performed by a probe assembly permanently attached to a component transporting material, the probe assembly including a memory storing computer executable instructions, the computer executable instructions, when executed by a controller of the probe assembly, causing the probe assembly to perform steps comprising: In response to receiving a short spike signal, a longitudinal wave LW transducer of the probe assembly attached to the component is activated by an intelligent amplifier multiplexer; In response to receiving a smooth low-frequency analog waveform signal, activating a first guided wave transducer of a plurality of guided wave GW transducers of the probe assembly attached to the component by an intelligent amplifier multiplexer; In response to activation of the first GW transducer, measuring, by a second GW transducer of the plurality of GW transducers, a first echo signal received through the component, wherein the first echo signal has a first time of flight and a first amplitude; In response to the activation of the first GW transducer, measuring, by a third GW transducer of the plurality of GW transducers, a second echo signal received through the component, wherein the second echo signal has a second time of flight and a second amplitude; processing the first echo signal and the second echo signal into a combined echo signal by an amplifier and the controller through baseline subtraction and digital filtering; and The combined echo signal is transmitted, wherein the combined echo signal is an indication of localized corrosion in the component.

20. The method of claim 19, in, said first GW transducer generates a non-dispersive zero-order shear horizontal wave in said component, And wherein the combined echo signal detects localized corrosion in the component without having to store a thickness map.

Images