Equipment and method for automatically monitoring effectiveness of cathode protection system of metal pipeline

Abstract

The invention discloses a device and method for automatically monitoring effectiveness of a metal pipeline cathode protection system, and the device comprises a computer processor assembly, a sensor assembly, an electric data bus, a medium, and a protected structure. A computer processor component connected to one or more sensor components placed along an electrical data bus, the sensor components and the electrical data bus being buried or submerged in a medium; the sensor assemblies are placed at a first distance of a protected structure, and adjacent sensor assemblies are separated by a second distance; according to the method, a user can be helped to analyze and check the performance of the cathode protection system, and data are summarized regularly or at any random period, so that normal operation of the performance of the current cathode protection system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline detection, and in particular to a device and a method for automatically monitoring the effectiveness of a cathodic protection system of a metal pipeline. Background Art

[0002] The cathodic protection system is a process technology that is open to the public and maturely applied in my country. Its principles and process characteristics will not be elaborated in detail here. Simply put, cathodic protection involves placing an anode material in an electrolyte (such as soil) with a corrosive metal surface, and providing an electrical connection between the anode material and the cathode corrosive metal to form an electrochemical reaction. The surface with more anodes will be corroded, and the surface with fewer anodes (or more cathodes) will not be corroded. For example, the surface of the metal pipe (cathode) then becomes more negatively polarized than before. In this case, the surface of the metal pipe is the cathode relative to the anode material. If used correctly, all corrosion occurs on the anode material, so that the cathode is protected.

[0003] At present, the corrosion condition of pipelines with cathodic protection is usually detected by measuring the on-off potential between the reference electrode arranged around the pipeline and the pipeline. However, due to the long length of the pipeline, it is not easy to measure the on-off potential between it and the reference electrode. Therefore, it is necessary to use a test pile polarization test piece buried in the adjacent pipeline to measure the on-off potential, that is, connect the test pile polarization test piece to the pipeline, and then measure the on-off potential between the reference electrode and the test pile polarization test piece. This manual measurement method depends on the technical level of the inspector, and is time-consuming, costly, and labor-intensive.

[0004] According to relevant national standards, pipeline corrosion testing is required on a regular basis, and currently this work is still carried out by field technicians recording the measurement results individually. This work is slow, costly, and can endanger the health of personnel due to surrounding vehicle traffic, environmental conditions, and even encounters with snakes and insects around grass and shrubs. Summary of the invention

[0005] The main purpose of the present invention is to provide a device and method for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline, aiming to solve the existing technical problems.

[0006] To achieve the above object, the present invention provides an apparatus for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline, comprising a computer processor assembly, a sensor assembly, an electrical data bus, a medium, and a protected structure;

[0007] The computer processor assembly is connected to one or more sensor assemblies positioned along an electrical data bus, the sensor assemblies and electrical data bus being buried or immersed in a medium;

[0008] The sensor components are placed at a first distance from the protected structure, and adjacent sensor components are spaced at a second distance;

[0009] Wherein, the first distance is greater than zero; the distance between the electrical data bus and the protected structure is smaller than the distance between the top surface of the medium and the protected structure, and the porous plug of the reference electrode on the sensor assembly is separated from the protected structure and does not directly contact the protected structure.

[0010] Furthermore, the computer processor component includes,

[0011] a central controller for performing operations for data collection and control features;

[0012] a power supply, which supplies power to the sensor components on the electrical data bus based on the instructions of the central controller;

[0013] a central memory for storing data collected by some or all of the sensor assemblies during an operating cycle; and,

[0014] Central input / output interface for sending control commands to the sensor components via the electrical data bus.

[0015] Furthermore, the sensor assembly comprises:

[0016] A controller, used to perform data collection and control operations on the sensor;

[0017] one or more sensors for detecting and sensing a voltage associated with a counter / reference electrode;

[0018] One or more reference electrodes used as a reference when measuring electrode potential;

[0019] a memory for storing data collected from the sensor; and,

[0020] Input / output interface for sending collected data back to the computer processor assembly via an electrical data bus.

[0021] Further, the sensor and counter / reference electrode are operably coupled to each other or the counter electrode is implemented within the sensor.

[0022] Further, the sensor and / or counter / reference electrode is located or accommodated outside the sensor assembly housing.

[0023] Further, the computer processor assembly is configured to retrieve data collected from each sensor assembly individually or collectively in any order.

[0024] Furthermore, the central controller includes:

[0025] A training module for training analytical models using a machine learning process;

[0026] Training set data, including historical material data and historical environmental data. Historical material data includes data related to materials and the data collection process, and historical environmental data includes data related to conditions or parameters in the data collection process;

[0027] The analysis model is configured as a trained analysis object;

[0028] processing resources configured to execute software instructions, process data, make parameter control decisions, perform signal processing, read data from memory, write data to memory, and perform other processing operations;

[0029] memory resources configured to store software instructions associated with controlling the functionality of the system and its components;

[0030] Communication resources, including wired and wireless communication resources, enable the central controller to receive sensor data related to the data collection process through the reference node.

[0031] Furthermore, the analysis model includes:

[0032] A process condition vector, including a plurality of data fields, each of which corresponds to a specific process condition;

[0033] The processor includes a first neural layer, a second neural layer and a third neural layer, each neural layer includes a plurality of nodes.

[0034] Furthermore, the operation steps of the analysis model include:

[0035] Collect training set data, including historical material data and historical environmental data;

[0036] a processor that inputs historical condition data into an analytical model in a central controller;

[0037] Generate forecast data based on historical condition data;

[0038] Compare predicted data with actual measured data;

[0039] determining whether the forecast data matches the historical condition data based on the comparison generated in the above step;

[0040] Adjust internal functions associated with analytical models;

[0041] The method for monitoring the effectiveness of a cathodic protection system for a metal pipeline is applied to the above-mentioned device for monitoring the effectiveness of a cathodic protection system for a metal pipeline, and specifically comprises the following steps:

[0042] receiving a plurality of voltage readings at respective locations of a plurality of reference electrodes;

[0043] Determine selected potentials that indicate effectiveness of cathodic protection systems;

[0044] Each voltage reading from each reference electrode is compared to the selected potential;

[0045] determining a plurality of voltage differences between respective voltage readings at respective reference electrodes and the selected potential;

[0046] determining whether each voltage difference is within a threshold level;

[0047] determining the position of a reference electrode having a voltage difference exceeding a threshold level;

[0048] Locations of the metallic pipeline corresponding to locations of the reference electrode having a voltage difference exceeding a threshold level are determined to have an ineffective cathodic protection system.

[0049] The beneficial effects of the present invention are embodied in:

[0050] In the present invention, a sensor system is arranged around the buried metal pipeline, and connected to the computer processing component through a cable bus, forming a set of automatic evaluation device and system for pipeline cathodic protection system. The system can monitor the corrosion potential of the metal pipeline at a specified time interval, and provide data that can identify and predict several important conditions: including the effectiveness of the CP system, the location of insufficient protection, interference from other mechanisms, CP system failure, etc., and can regularly or even automatically test the metal pipeline at a very low cost to avoid human operation errors.

[0051] In the present invention, periodic measurements are automatically received by a transceiver and a plurality of sensors arranged. The sensor system may receive measurements only at selected time intervals and may be in a standby state until power is provided or changed or a request to collect measurements by a plurality of sensors has been received. The sensor system detects irregularities in the received metrics or sensor data by various sensors at specific locations adjacent to the structure. The sensor data is stored in a system database and may be analyzed for monitoring and sending alarms.

[0052] In the present invention, the automatic data collection function of the sensor system according to the present disclosure can collect the current status of the cathodic protection system, so that the effectiveness of the cathodic protection can be immediately accessed in real time. When the pipeline or cathodic protection system needs immediate attention, this improved report accessibility may enable timely remediation.

[0053] In the present invention, rapid repeatability of the test is provided. Combined with the accumulation of data collected over time, the sensor system according to the present disclosure can provide detailed, long-term information about the system, which is not available from data manually obtained on-site, which may take days to obtain and weeks or longer to process. The system of the present invention has greater monitoring capabilities than manually obtained data, and can predict abnormal conditions and take action in advance to maintain the integrity of the pipeline and reduce the possibility of pipeline failure.

[0054] The present invention can help users analyze and check the performance of the cathodic protection system and summarize the data regularly (or at any random period) to ensure that the current cathodic protection system is operating normally. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the structure of the device for automatically monitoring the effectiveness of the cathodic protection system of a metal pipeline according to the present invention;

[0056] Figure 2 is a schematic diagram of the structure of a computer processor assembly of the present invention;

[0057] Figure 3 It is a structural schematic diagram of the sensor assembly of the present invention;

[0058] Figure 4 A flow chart of a method for evaluating a cathodic protection system for the present invention;

[0059] Figure 5 It is a node structure block diagram of the central controller of the present invention;

[0060] Figure 6 is a block diagram of the operation of the analysis model in the central controller of the present invention;

[0061] Figure 7 The figure is a flowchart of the operation of the training module for training the analysis model according to the present invention.

[0062] Description of reference numerals:

[0063] 200. Computer processor assembly; 210. Sensor assembly; 220. Electrical data bus; 230. Medium; 240. Protected structure.

[0064] 310, controller; 320, sensor; 330, reference electrode; 340, memory; 350, input / output interface;

[0065] 510, central controller; 520, power supply; 530, central memory; 540, central input / output interface;

[0066] 810, training module; 820, training set data; 822, historical material data; 824, historical environmental data; 830, analysis model; 840, processing resources; 850, memory resources; 860, communication resources; 870, data server;

[0067] 900, process condition vector; 905, data field; 910, processor; 920a, first neural layer; 920b, second neural layer; 930c, third neural layer; 930, node; 940, prediction data. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0069] See also Figure 1 , the present invention provides an apparatus for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline, comprising a computer processor assembly 200, a sensor assembly 210, an electrical data bus 220, a medium 230, and a protected structure 240;

[0070] The computer processor assembly 200 is connected to one or more sensor assemblies 210 (see, e.g., Figure 1 The sensor assembly 210 and the electrical data bus 220 are buried or immersed in a medium 230 (e.g., natural soil, water, etc.);

[0071] The sensor assembly 210 is placed at a first distance (i.e. Figure 1D2), that is, multiple reference nodes 210a, 210b, 210c... are placed at a specified distance of the protected structure 240, and the reference nodes are spaced apart from each other at a selected distance. For example, the first reference node 210a and the adjacent second reference node 210b can be spaced apart from each other at a selected distance D1. In some embodiments, each adjacent reference node can be evenly spaced apart from each other throughout the electrical data bus 220, that is, the third reference node 210c and the adjacent second reference node 210b can be spaced apart from each other at a selected distance D1. In addition, the fourth reference node (not shown in the figure) can be spaced apart from the third reference node 210c at a selected distance D1: However, in other embodiments, each adjacent reference node can be spaced apart at a different distance on the entire electrical data bus 220. For example, the first reference node 210a and the adjacent second reference node 210b can be spaced apart from each other at a selected distance D1, but the second reference node 210b and the adjacent third reference node 210c can be spaced apart from each other at a distance different from the distance D1. Typically D1 can be chosen to be less than 6 meters.

[0072] The second distance (i.e. Figure 1 D1) in the middle.

[0073] The computer processor assembly 200 includes a central controller, a power supply, and a rectifier. However, in other embodiments, individual components may be further added, or some of the listed components may be omitted, such as the rectifier may be omitted, or it may exist but be located away from the computer processor assembly 200.

[0074] In one embodiment, computer processor assembly 200 is coupled to one end of an electrical data bus 220 .

[0075] The electrical data bus 230 includes a cabling structure such as coaxial cable, CAT6, fiber optic cable, WiFi, LoRaWAN, NB-IOT, 4G or any type of wired or radio data bus, also including data carrying lines.

[0076] The protected structure 240 can be any type of partially or completely metal structure, including metal pipes, storage tanks, reinforced concrete structures, seawalls, bridges, buildings, or transportation systems. For ease of explanation, the protected structure 240 here refers to a metal pipe (hereinafter, the metal pipe is used as an example for explanation). The fluid being transported or stored can be oil and natural gas, propane, or any petroleum product, and can be in any fluid form, including liquid, gas, pressurized gas, liquefied gas, or any other fluid form.

[0077] It should also be noted that although Figure 1The electrical data bus 220 with the reference node 210 shown in FIG. 2 is arranged above the metal pipe 240, but this is shown only as an example. For example, the electrical data bus 220 may be arranged below the metal pipe 240. In another example, the electrical data bus 220 may be arranged side by side in parallel around the metal pipe 240, and may be any other position adjacent to the metal pipe 240.

[0078] In the present system, power is provided to the electrical data bus 220 by the power supply of the computer processor assembly 200, and the reference node is powered accordingly, and a potential reading is obtained between the reference electrode in the reference node and the protected structure 240. The voltage difference between the reference electrode in the first reference node 210a and the metal pipeline 240 can be sensed at the first reference node 201a. In some embodiments, the voltage difference measured by the sensor system at the first reference node 210a can be interpreted as indicating the effectiveness of the cathodic protection system on the pipeline. When the polarization potential shown by the measurement is more negative than the established threshold indicating the protected potential, it indicates that the cathodic protection system is operating normally and is in a protective state.

[0079] In one embodiment, after the potentials are obtained at each reference node 210 , the values ​​are compared to previous data and nearby sensor node 210 data to assess the accuracy and functionality of the individual sensor nodes 210 .

[0080] In one embodiment, the reference node 210 is buried and remains in an idle mode until the computer processor assembly 200 supplies power to the reference node 210 to retrieve the data collected at the reference node 210. The reference node 210 can also be configured to continuously, periodically, or intermittently collect voltage readings at that particular location over a selected time period, and the system can be programmed to provide power at selected time intervals or always remain powered. In addition, the system can remain idle until it is specifically instructed to turn on or is manually turned on by a local operator. When the reference node 210 receives a request from the computer processor assembly 200 to report its data (in some embodiments, the node can report data based on a schedule, event, or continuously rather than on request), the data is transmitted to the computer processor assembly 200 via the electrical data bus 220. In one or more embodiments, each reference node is individually addressable so that the computer processor assembly 200 can request data in any order.

[0081] In addition, since the sensor system according to one or more embodiments of the present disclosure measures whether the cathodic protection system is operating at a specific location (e.g., the location where the reference node is located), any shortcomings of the cathodic protection system can be identified and responded to in real time or substantially real time, thereby avoiding any potential damage to property and life.

[0082] See also Figure 2 , the computer processor component 200 (the computer processor component 200 may include additional components as required, such as rectifiers, level shifters, etc., but is not limited to the additional components Figure 2 The components shown) include,

[0083] A central controller 510 for performing operations of data collection and control features; the central controller 510 is operably coupled to a power supply 520. The power supply 520 is capable of supplying power to the reference node 210 on the electrical data bus 220 based on instructions from the central controller 510; the central controller 510 may include any circuits, features, components, electronic components, etc.;

[0084] The central controller 510 may include any processor-based or microprocessor-based system, including systems using microcontrollers, reduced instruction set computers (RISC), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), logic circuits, and any other circuits or processors capable of performing the functions described herein. For example, the central controller 510 may be implemented using a Raspberry Pi.

[0085] In an embodiment, the central controller 510 may be included in or otherwise implemented by a processing circuit, such as a microprocessor, a microcontroller, an integrated circuit, a chip, a microchip, etc. The central controller 510 may be connected to a communication bus.

[0086] A power supply 520 that supplies power to the sensor assembly 210 on the electrical data bus 220 based on instructions from the central controller 510;

[0087] Central memory 530, for storing data collected by some or all sensor components 210 during an operation cycle; central memory 530 may include random access memory (RAM), read-only memory (ROM), a hard drive, or a removable storage drive, such as an optical drive, a solid-state disk drive (e.g., flash RAM), etc. Central memory 530 may also be other similar devices for loading computer programs or other instructions into a computer or processor.

[0088] For example, when the power to the electrical data bus 220 is turned on by the power supply 520 during an operation cycle, the reference nodes collect self-voltage data at their respective locations and store it in their respective memories 340. The central controller 510 can request the reference nodes to send back the data collected and stored in their memories 340. When the data from the reference nodes are all collected, the data is stored in the central memory 530 of the computer processor assembly 200. When the operation cycle ends and the power supply 520 to the electrical data bus 220 is cut off, the reference nodes do not collect data along the electrical data bus 200.

[0089] In one embodiment, since the memory 340 used in the reference nodes is volatile, turning off the power 520 will erase the previously collected data in the memory 340. However, since all data from the various reference nodes 210 is transmitted and stored in the central memory 530, no data is lost or omitted.

[0090] The central input / output interface 540 is used to send control instructions to the sensor assembly 210 via the electrical data bus 220 .

[0091] See also Figure 3 The sensor assembly 210 includes a controller 310 for performing data collection and control operations on the sensor 320; the controller 310 includes a processing circuit, such as a microprocessor, a microcontroller, an integrated circuit, a chip, a microchip, a processing unit, a logic circuit, etc.

[0092] One or more sensors 320 for detecting and sensing a voltage associated with a counter / reference electrode 330;

[0093] one or more counter / reference electrodes 330;

[0094] Memory 340, for storing data collected from sensor 320; memory 340 stores data collected from sensor 320. Memory 340 may include any suitable memory capable of storing recorded data. For example, the storage device used may be a static random access memory (SRAM). When the sensor system according to the present disclosure is powered by the computer processor assembly 200, the sensor 320 collects data over a specific time period and the SRAM retains the data value. These data stored in memory 340 are retrieved via the electrical data bus 220 and may be loaded into the computer processor assembly 200. Memory 340 may also store computer programs or other instructions for loading into the controller 310, such as update software, data filtering or analysis software, geolocation data, or data from other sensors.

[0095] In one or more embodiments, the memory 340 may be included on the controller 310.

[0096] Input / output interface 350 is used to send the collected data back to computer processor assembly 200 via electrical data bus 220. Input / output interface 350 includes various connections within reference node 200, connections to adjacent reference nodes and electrical data bus 220, and other connections required to communicate data with computer processor assembly 200 via electrical data bus 200.

[0097] In one embodiment, the input / output interface 350 includes a data input / output interface and a power interface. For example, some electrical connections or lines can be used to transmit both data and power.

[0098] The input / output interface 350 includes electrical connections for sending collected data back to the computer processor assembly 200 via the electrical data bus 220 .

[0099] In one embodiment, reference sensor assembly 210 is closer to metal pipe 240 than a boundary of the electrolyte (eg, the earth's surface).

[0100] In some embodiments, sensor 320 and reference electrode 330 can be operably coupled to each other. However, in other embodiments, reference electrode 330 can be implemented within sensor 320 rather than as a separate component.

[0101] In one embodiment, the reference electrode 330 comprises a potential sensing electrode.

[0102] In one or more embodiments, sensor 320 and / or reference electrode 330 may be located or housed externally of the reference node 210 housing.

[0103] In one embodiment, the reference node 210 will include one reference electrode 330, and a second reference electrode 330 will be placed at a known distance from the reference node 210. In this way, a common corrosion engineering test known as a DC voltage gradient measurement can be performed.

[0104] In one or more embodiments, the power supply of the reference node 210 can be obtained from the computer processor assembly 200 through the electrical data bus 220. When the computer processor assembly 200 provides power to the reference node 210 through the coupling, the reference node 210 along the electrical data bus 220 is activated. With power supplied to each component of the reference node 210, the sensor 320 measures the voltage between the reference electrode 330 and the metal structure 240. The voltage data collected at each reference node 210 is stored in the corresponding memory 340 of each reference node 210. For example, the voltage data collected at the first reference node 210a is collected in the memory within the first reference node 210a. Similarly, the voltage data collected at the second reference node 210b is collected in the memory within the second reference node 210b, and the current data collected at the third reference node 210c is collected in the memory within the third reference node 210c.

[0105] In one embodiment, the first distance is greater than zero. Specifically, it can be between about 2 cm and 2.2 meters. More preferably, the first distance is greater than 3 cm and less than 1 meter.

[0106] In one embodiment, the distance between the electrical data bus 220 and the protected structure 240 is smaller than the distance between the top surface of the medium 230 and the protected structure 240. Specifically, the distance from the top surface of the medium 230 to the reference node 210 is D3.

[0107] Preferably, distance D3 is large enough so that traffic directly on the soil surface, whether pedestrian or vehicular traffic, does not affect the electrical data bus 220.

[0108] In one embodiment, the porous plug of the reference electrode on the sensor assembly 210 is separated from the protected structure 240 and does not directly contact the protected structure 240. Instead, it is adjacent to the protected structure 240. This is because it is necessary to leave space for the medium 230 so that it can be used as an electrolyte to form an electrochemical circuit.

[0109] In one embodiment, the computer processor assembly 200 is configured to retrieve data collected from each sensor assembly 210 individually or collectively in any order. For example, the computer processor assembly 200 may issue a request to retrieve data stored in the second reference node 210b and retrieve only data stored in the first reference node 210b. In this case, the data stored in the first reference node 210a and the third reference node 210c may be stored in their respective memories but cannot be retrieved by the computer processor assembly 200. In another example, the computer processor assembly 200 may issue a request along the electrical data bus 220 to retrieve data stored in all reference nodes. That is, data from the first, second and third reference nodes 210a, 210b, 210c may be collected sequentially or simultaneously. In another example, the computer processor assembly 200 may issue a request to retrieve data stored in the order of the second reference node 210b, the third reference node 210c and the first reference node 210a. That is, each reference node can be identified by an address scheme, and the computer processor component 200 can locate and identify each reference node by the address scheme, and collect data from the first, second and third reference nodes 210a, 210b, 210c in any order. The communication between the computer processor component 200 and the reference node is not limited to the addressing request model. Therefore, in one or more embodiments, the reference node can push data to the computer processor component 200, or send data on a schedule, or send data continuously, or other digital communication paradigms. When power from the computer processor component 200 is not provided to the reference node, the reference node is located below the medium 230 adjacent to the transmission structure 220. For example, the reference node can be buried in this position idly. However, when power is supplied, the reference node collects data for a set period of time during power-on and stores the voltage data in a memory. The computer processor component 200 can collect the voltage data stored in the memory of the reference node before the power is turned off. When the power is turned off, since the memory is a volatile memory such as SRAM in some embodiments, the previously collected data is erased, and the newly collected data will be stored in the memory at the next power supply.

[0110] Data may be collected at any time and for any period of time at the computer processor assembly 200. The number and rate of return readings may be handled by any acceptable processor based on the rate provided by its memory, data transfer rate, processing power, speed, and other system parameters.

[0111] The computer processor assembly 200 includes a central controller 510, a power supply 520, a central memory 530, and a central input / output interface 540. The computer processor assembly 200 may include additional components as required, such as a rectifier, a level shifter, etc., and is not limited to the components shown in the drawings.

[0112] The central controller 510 is operably coupled to a power supply 520. The power supply 520 is capable of supplying power to the reference node 210 on the electrical data bus 220 based on instructions of the central controller 510.

[0113] The central controller 510 may include any circuits, features, components, electronic components, etc., which are configured to perform various operations of the data collection and control features described herein. For example, the central controller 510 may include any processor-based or microprocessor-based system, including systems using microcontrollers, reduced instruction set computers (RISC), application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), graphics processing units (GPUs), logic circuits, and any other circuits or processors capable of performing the functions described herein. For example, the central controller 510 may be implemented using Raspberry Pi. In some embodiments, the central controller 510 may be included in a processing circuit or otherwise implemented by a processing circuit, such as a microprocessor, a microcontroller, an integrated circuit, a chip, a microchip, etc. The central controller 510 may be connected to a communication bus. The central controller 510 may also include a central memory 530. The central memory 530 may include a random access memory (RAM), a read-only memory (ROM), a hard drive or a removable storage drive, such as an optical drive, a solid-state disk drive (e.g., flash RAM), etc. The central memory 530 may also be other similar devices for loading computer programs or other instructions into a computer or processor.

[0114] The central memory 530 is responsible for storing data collected from some or all reference nodes 210 during an operation cycle. For example, when the power to the electrical data bus 220 is turned on by the power supply 520 during an operation cycle, the reference nodes 210 collect self-voltage data at their respective locations and store it in their respective memories 340. The central controller 510 can request the reference nodes 210 to send back the data collected and stored in their memories 340. When the data from the reference nodes 210 are all collected, the data is stored in the central memory 530 of the computer processor assembly 200. When the operation cycle ends and the power supply 520 supplied to the electrical data bus 220 is cut off, the reference nodes 210 will not collect data along the electrical data bus 200. In some embodiments, since the memory 340 used in the reference node 210 is volatile, turning off the power supply 520 will erase the previously collected data in the memory 340. However, since all data from the various reference nodes 210 are transmitted and stored in the central memory 530, no data is lost or omitted.

[0115] See also Figure 4, a method 700 of evaluating a cathodic protection system for protecting a metal pipeline from corrosion includes receiving a plurality of voltage readings at respective locations of a plurality of reference electrodes using a reference electrode (see step 710 ).

[0116] Method 700 includes determining a selected potential indicative of the effectiveness of a cathodic protection system (see step 720). As described above, the measured potential or polarization of the protected object (i.e., the metal pipeline) indicates the level of cathodic protection received by the metal pipeline. For example, the industry standard minimum standard is -850mV relative to a Cu / CuSO electrode, which can be a selected potential indicating the effectiveness of a cathodic protection system that meets industry standards. However, other measurements and standards based on other standards can also be selected. That is, the effectiveness of the cathodic protection system can be shown by the potential of polarization decay. Here, the industry standard minimum standard is 100mV. Therefore, depending on the standard, the selected potential that shows the effectiveness of the cathodic protection system may vary.

[0117] Method 700 includes comparing each voltage reading from a respective reference electrode to a selected potential (see step 730).

[0118] The method 700 includes determining a plurality of voltage differences between each voltage reading at each reference electrode and the selected potential (see step 740).

[0119] Method 700 also includes determining whether each voltage difference is within a threshold level (see step 750). For example, in some cases, a voltage reading obtained using a Cu / CuSO4 electrode using a reference electrode may be -840mV. In some cases, if the voltage difference is less than about 0.5%, the sensor system according to the present disclosure can determine that the voltage reading is within an acceptable threshold level. In addition, the sensor system can determine that the measurement value within the threshold level meets industry standards and ensures the effectiveness of the cathodic protection system. However, other more stringent threshold levels can be selected, and the 0.5% voltage difference is only used as an example to determine the position of the reference electrode with a voltage difference exceeding the threshold level (see step 760). For example, if one of the reference electrodes, i.e., the third reference node 210c, displays a voltage reading exceeding the threshold level, the position can be identified by the computer processor component. The computer processor component is configured to identify the position of each reference electrode through an addressing scheme, and if one of the reference electrodes deviates from the selected potential (e.g., -850mV) according to the standard, it can output the position of the reference electrode.

[0120] The method 700 includes determining a portion of the metal pipeline corresponding to a location of a reference electrode having a voltage difference exceeding a threshold level to have an ineffective cathodic protection system (see step 770). That is, the sensor system can accurately identify which portion of the metal pipeline is not protected by the cathodic protection system.

[0121] See also Figure 5 The central controller 510 is configured to control the automatic operation of the power supply, automatic data collection through the reference node, and other operations. The central controller 510 uses machine learning to adjust the parameters of the above automatic operations. The central controller 510 can adjust the parameters of various operations, thereby improving efficiency.

[0122] In one embodiment, the central controller 510 includes an analysis model 830 and a training module 810. The training module 810 trains the analysis model 830 using a machine learning process so that various automatic operations can be performed effectively. Parameters that can be used to train the analysis model 830 through the training module 810 include, but are not limited to, historical data patterns of potentials of specific reference nodes from specific locations, historical data models of corrosiveness at specific locations, traditional data patterns of potentials associated with electrolyte types (e.g., soil, water, air, etc.), historical data patterns of potentials based on metal pipeline coating types, and conditions at specific locations that affect electrolytes (e.g., precipitation, climate, etc.).

[0123] Soil resistivity can be determined by applying a potential difference between a first electrode and a second electrode and measuring the resulting current density, or by forcing or otherwise providing an electric current therethrough and measuring the resulting potential.

[0124] Although the training module 810 is shown in the figure as being separate from the analysis model 830, in practice, the training module 810 can be a part of the analysis model 830. The central controller 510 includes or stores the training set data 820. The training set data 820 includes historical material data 822 and historical environmental data 824. The historical material data 822 includes data related to materials and data collection processes. For example, it can include historical data patterns of potentials related to electrolyte types (e.g., soil, water, air, etc.), historical data patterns of potentials based on metal pipeline coating / protective layer types, traditional data patterns of potentials based on material types used for metal pipelines, soil resistivity patterns at specific locations, historical data models of metal pipeline attenuation rates, historical database patterns of potentials relative to reference electrode types / materials, etc. The historical environmental data 824 includes data related to conditions or parameters during the data collection process. For example, historical potential data patterns from a specific reference node at a specific location, historical data patterns for corrosivity at a specific location, historical environmental conditions at a specific location that affect electrolytes (such as precipitation, climate, etc.), soil resistivity patterns at a specific location, data collection time frames required by various government standards and regulations (such as once a month, twice a month, or other requirements).

[0125] The following describes that the training module 810 utilizes historical material data 822 and historical environmental data 824 to train an analysis model 830 with a machine learning process.

[0126] In one embodiment, the training set data 820 links the historical material data 822 with the historical environmental data 824. By linking the two data sets 822, 824, the central controller 510 can generate automatic reports that comply with national standards more efficiently and accurately. For example, by using the machine learning process, the central controller 510 can automatically power the reference node and collect the potential according to the timeline specified by the relevant standards, and can also automatically investigate the metal pipeline section that must be inspected in this cycle. In this way, no human intervention is required in the entire power supply process and data collection process.

[0127] In one embodiment, the analysis model 830 includes a neural network. The training of the analysis model 830 will be described below in conjunction with the neural network. However, other types of analysis models or algorithms may be used without departing from the scope of the present disclosure. The training module 810 uses machine learning to train the neural network through the training set data 820. During the training process, the neural network receives historical environmental data 824 from the training set data as input. During the training process, the neural network outputs predicted data for each relevant parameter. For example, for a pattern of potential historical data based on the type of material used for a metal pipeline, the neural network can output the time to repair the pipeline itself or the cathodic protection system that protects the pipeline based on the type of material of the pipeline, the age of the material, the decay rate of the material, and the potential data trend based on the type, age, and decay rate of the pipeline material, and generate predicted maintenance time data. In one embodiment, the neural network includes multiple neural layers. Various neural layers include neurons that define one or more internal functions. This internal function is based on weighted values ​​associated with neurons in each neural layer of the neural network. During training, the central controller 510 compares the predicted historical material data of each set of historical material data 822 with the actual historical material data associated with the data collection process, and the control system generates an error function indicating the degree of match between the predicted data and the historical material data. The central controller 510 then adjusts the internal functions of the neural network. Since the neural network generates prediction data based on the internal functions, adjusting the internal functions will result in different prediction data being generated for the same set of historical material data 822, and may also result in the prediction data producing a larger error function (a poorer match to the historical material data 822) or a smaller error function. The historical environmental data 824 is provided to the neural network and undergoes a training process as described above. The training module 810 again adjusts the internal functions of the neural network based on the historical environmental data 824. This process is repeated for both data sets 822, 824 in a large number of iterations of monitoring the error function and adjusting the internal functions of the neural network until a set of internal functions is found that results in prediction data that matches the historical material data 822 and the historical environmental data 824 throughout the training set.

[0128] At the beginning of the training process, the predicted data may not match the historical material data 822 or the historical environmental data 824 very well. However, as the training process proceeds through multiple iterations of adjusting the internal functions of the neural network, the error function will tend to become smaller and smaller until a set of internal functions is found that results in the predicted data matching the historical material data 822 and the historical environmental data 824. Identifying a set of internal functions that produces predicted data that matches the historical material data 822 and the historical environmental data 824 is considered to correspond to the completion of the training process.

[0129] In one embodiment, the central controller 510 includes processing resources 840, memory resources 850, and communication resources 860. The data server 870 can be operably coupled to the central controller 510 and share the data analysis process with the central controller 510. The processing resources 840 may include one or more controllers or processors. The processing resources 840 are configured to execute software instructions, process data, make parameter control decisions, perform signal processing, read data from memory, write data to memory, and perform other processing operations. The processing resources 840 may include physical processing resources 840 located at a computer processor component. In some embodiments, the processing resources 840 may include virtual processing resources 840 that are remote from the computer program or component site. That is, the processing resources 840 may include cloud-based processing resources, including processors and servers accessed through one or more cloud computing platforms.

[0130] In some embodiments, the memory resource 850 may include one or more computer readable memories. The memory resource 850 is configured to store software instructions associated with the functions of the control system and its components, including but not limited to the analysis model 830. The memory resource 850 may store data associated with the functions of the central controller 510 and its components. The data may include training set data 820, current process condition data, and any other data related to the operation of the central controller 510 or any of its components. In some embodiments, the memory resource 850 may include a virtual memory resource located away from the computer processor component. That is, the memory resource 850 may include a cloud-based memory resource accessed via one or more cloud computing platforms.

[0131] In some embodiments, the communication resources 860 may include resources that enable the central controller 510 to communicate with components associated with other systems, including the data server 870. For example, the communication resources 860 may include wired and wireless communication resources that enable the central controller 510 to receive sensor data related to the data collection process through a reference node. The communication resources 860 combined with the data server 870 may enable the central controller 510 to communicate with remote external systems. For example, the collected data may be provided to a central server of an entity that manages and operates the pipeline. The communication resources 860 may include or may facilitate communication via one or more networks, such as a wired network, a wireless network, the Internet, or an intranet. The communication resources 860 may enable the components of the central controller 510 to communicate with each other.

[0132] See also Figure 6As described above, the training set data 820 includes data related to the potential (or voltage) data collection process at the reference node. Each previously formed data collection process is performed under specific process conditions. The process conditions of each data collection process are classified into a corresponding process condition vector 900. The process condition vector 900 includes a plurality of data fields. Each data field 905 corresponds to a specific process condition. Figure 6 The example of FIG. 8 shows a single process condition vector 900 that will be passed to a processor 910 of the analytical model 830 during the training process. In this example, the process condition vector 900 includes nine data fields 905.

[0133] The first data field 905 corresponds to the material type of the pipe (including the coating / protective layer of the pipe).

[0134] The second data field 905 corresponds to the age (or decay rate or corrosivity) of the pipeline.

[0135] The third data field 905 corresponds to the soil resistivity of the medium 230 .

[0136] The fourth data field 905 corresponds to regulatory requirements, including federal standards and regulations for each location (state, country, etc.).

[0137] The fifth data field 905 corresponds to the material type of the electrolyte.

[0138] The sixth data field 905 corresponds to the material type of the reference electrode.

[0139] The seventh data field 905 corresponds to the location where the reference electrode is buried (it may be buried in a location where it rains frequently in the summer of 65 years, which may affect the condition of the electrolyte).

[0140] The eighth data field 905 corresponds to a pattern of electrical potential data collected over time (including fluctuations over time, between high voltage peaks and low voltage peaks).

[0141] The ninth data field corresponds to the temperature of the setting where the reference node is located (for example, if the pipeline is buried in a hot location, the soil may dry out, which will affect the role of the soil as an electrolyte).

[0142] In practice, each process condition vector 900 may include a ratio of Figure 6 Each process condition vector 900 may include different types of process conditions without departing from the scope of the present disclosure. Figure 6 The specific process conditions shown in are given as examples only. Each process condition is represented by a numerical value in the corresponding data field 905.

[0143] The processor 910 includes a plurality of neural layers 920a-c. Each neural layer includes a plurality of nodes 930. Each node 930 may also be referred to as a neuron. Each node 930 from the first neural layer 920a receives a data value for each data field from the process condition vector 900. Thus, in Figure 6 In the example of FIG. 1 , each node 930 from the first neural layer 920a receives nine data values ​​because the process condition vector 900 has nine data fields. Each neuron 930 includes Figure 6 Each node 930 of the first neural layer 920a generates a scalar value by applying the internal mathematical function F(x) to the data value of the data field 905 from the process condition vector 900. Further details about the internal mathematical function F(x) are provided below.

[0144] exist Figure 6 In the example of FIG. 9 , each neural layer 920a-920c in the processor 910 is a fully connected layer. This means that each neural layer has the same number of nodes as the subsequent neural layer. Figure 6 In the example of FIG. 1 , each neural layer 920a-920c includes five nodes, however, in some embodiments, the neural layers of processor 910 may be Figure 6 Different numbers of layers are shown.

[0145] In this Figure 6 As shown, each node 930 of the second neural layer 920b receives a scalar value generated by each node 920 of the first neural layer 920a. Figure 6 In the example of , each node of the second neural layer 920b receives five scalar values ​​because there are five nodes 930 in the first neural layer 920a. Each node 930 of the second neural layer 920b generates a scalar value by applying a corresponding internal mathematical function F(x) to the scalar value from the first neural layer 920a. There may be one or more additional neural layers between neural layer 920b and neural layer 920c. The final neural layer 920c of the processor 910 receives five scalar values ​​from the five nodes (not shown) of the previous neural layer. The output of the final neural layer is predicted data 940. In the machine learning process, the analytical model compares the predicted data value 940 with the actual measured value. The analytical model 830 generates an error value that indicates the error or difference between the predicted data 940 and the actual measured data. The error value is used to train the processor 910.

[0146] By discussing the internal mathematical function F(x), the training of the processor 910 can be more fully understood. Although all nodes 930 are labeled with the internal mathematical function F(x), the mathematical function F of each node is unique. In one example, each internal mathematical function has the following form: F(x) = x1*w1+x2*w2+...x n *w n +b.

[0147] In the above equation, each value x1-x n corresponds to the data value received from the node 930 in the previous neural layer, or, in the case of the first neural layer 920a, each value x1-x n corresponds to the corresponding data value of data field 905 from process condition vector 900. Thus, n for a given node is equal to the number of nodes in the previous neural layer. n is a scalar weight value associated with the corresponding node from the previous layer. The analytical model 830 selects the weight value w1-w n The constant b is a scalar bias value that can also be multiplied by a weight value. The value generated by node 930 is based on the weight value w1-w n Therefore, each node 930 has n weight values ​​w1-w n Although not shown above, each function F(x) may also include an activation function. The sum in the above equation is multiplied by the activation function. Examples of activation functions may include a rectified linear unit function, a sigmod function, a hyperbolic tension function, or other types of activation functions. Each function F(x) may also include a transfer function.

[0148] After calculating the error value, the analysis model 830 adjusts the weight values ​​w1-w1 of each node 930 of each neural layer 920a-920c. n In the analysis model 830, the weight value w1-w is adjusted. n Afterwards, the analysis model 830 again inputs the neural layer 920a

[0149] A process condition vector 900 is provided. Because the weight values ​​of each node 930 of the analytical model 830 are different, the predicted data

[0150] The analysis model 830 again generates an error value by comparing the actual measured data with the predicted data 940. The analysis model 830 again adjusts the weight values ​​w1-w associated with each node 930. n The analytical model 830 processes the process condition vector 900 again and generates prediction data 940 and associated error values. The training process includes adjusting the weight values ​​w1-w in iterations. n , until the error value is minimized.

[0151] Figure 6 A single process condition vector 900 is shown being passed to a processor 910. In practice, the training process includes passing a large number of process condition vectors 900 through the analytical model 830, generating prediction data 940 for each process condition vector 900, and generating an associated error value for each prediction data 940. The training process may also include generating an aggregate error value that indicates the average error of all prediction data 940 for a batch of process condition vectors 900. The analytical model 830 adjusts the weight values ​​w1-w2 after processing each batch of process condition vectors 900. n The training process continues until the average error of all process condition vectors 900 is less than the selected threshold tolerance. When the average error is less than the selected threshold tolerance, the training of the processor 910 is complete and the analytical model is trained to accurately calculate the prediction data 940 based on the process conditions.

[0152] See also Figure 7 Here, the operation process for training the analysis model is named as process 1000, which is described in detail as follows:

[0153] At step 1010, training set data is collected, including historical material data 822 and historical environmental data 824. This can be achieved by using a data mining system or process. The data mining system or process can collect training set data by accessing one or more databases related to the sensor assembly and the cathodic protection system and other systems that work in conjunction with these systems, and collect and organize various types of data contained in one or more databases. The data mining system or process can process and format the collected data to generate training set data.

[0154] At step 1020, the historical condition data is input to the processor 910 in the central controller analysis model. In some examples, this may include inputting the historical material data 822 and the historical environmental data 824 into the analysis model 830 with the training module 810. The historical condition data may be provided to the processor 910 in the form of continuous discrete sets. The historical condition data may be provided to the processor 910 as condition vectors. Each set may include one or more vectors that are formatted for receipt and processing by the processor 910. The historical condition data may be provided to the processor 910 in other formats without departing from the scope of the present disclosure.

[0155] In step 1030, forecast data is generated based on the historical condition data. Specifically, the analysis model 830 generates the following for each set of historical condition data: Figure 6 Prediction data 940 is shown.

[0156] At 1040, the predicted data is compared to the actual measured data. Specifically, the predicted data for each set of historical condition data is compared to the historical material data 822 (and historical environmental data 824) associated with the set of historical condition data. The comparison can produce an error function that indicates how well the predicted data matches the historical material data 822 (and historical environmental data 824). The comparison is performed for each set of predicted data. In one embodiment, the process can include generating an aggregate error function or indication that indicates how the totality of the predicted data compares to the historical material data 822 (and historical environmental data 824). These comparisons can be performed by the training module 810 or the analytical model 830.

[0157] At step 1050, it is determined whether the predicted data matches the historical condition data based on the comparison generated at step 1040. For example, if the aggregate error function is greater than the error tolerance, the predicted data determined by process 1000 does not match the historical condition data; if the aggregate error function is less than the error tolerance, the predicted data determined by process 1000 matches the historical condition data.

[0158] In some embodiments, if at step 1050 the predicted data does not match the historical material data 822 (and historical environmental data 824 ), process execution proceeds to step 1060 .

[0159] At step 1060, the internal functions associated with the analysis model 830 are adjusted. For example, the training module 810 adjusts the internal functions associated with the processor 910. From step 1060, the process returns to step 1020. At step 1020, the historical condition data is again provided to the analysis model 830. Because the internal functions of the processor 910 have been adjusted, the analysis model 830 will generate different prediction data from the previous cycle. The process proceeds to steps 1030, 1040, and 1050, and the total error is calculated. If the prediction data does not match the historical condition data, the process returns to step 1060 and the internal functions of the processor 910 are adjusted again. The process is performed in iterations until the processor 910 generates prediction data that matches the historical condition data. In one embodiment, if the prediction data matches the historical condition data, the process step 1050 in the process 1000 proceeds to 1070. At step 1070, it is indicated that the training is complete.

[0160] It should be noted that if the embodiments of the present invention involve directional indications such as up, down, left, right, front, back, etc., then the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture as shown in the accompanying drawings. If the specific posture changes, the directional indication will also change accordingly.

[0161] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or schemes that A and B meet at the same time. In addition, "multiple" refers to more than two. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist.

[0162] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Equipment for automatically monitoring the effectiveness of cathodic protection systems for metal pipelines, characterized in that: include, A computer processor assembly (200), a sensor assembly (210), an electrical data bus (220), a medium (230), and a protected structure (240); The computer processor assembly (200) is connected to one or more sensor assemblies (210) placed along an electrical data bus (220), the sensor assemblies (210) and the electrical data bus (220) being buried or immersed in a medium (230); The sensor components (210) are placed at a first distance from the protected structure (240), and adjacent sensor components (210) are spaced apart at a second distance; The first distance is greater than zero; the distance between the electrical data bus (220) and the protected structure (240) is smaller than the distance between the top surface of the medium (230) and the protected structure (240); the porous plug of the reference electrode on the sensor assembly (210) is separated from the protected structure (240) and does not directly contact the protected structure (240).

2. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 1, characterized in that: The computer processor assembly (200) comprises, a central controller (510) for performing operations of data collection and control features; A power supply (520) for supplying power to the sensor assembly (210) on the electrical data bus (220) based on instructions from the central controller (510); a central memory (530) for storing data collected by some or all of the sensor assemblies (210) during an operating cycle; and, A central input / output interface (540) is used to send control instructions to the sensor assembly (210) via the electrical data bus (220).

3. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 1, characterized in that: The sensor assembly (210) comprises, A controller (310) for performing data collection and control operations on the sensor (320); one or more sensors (320) for detecting and sensing a voltage associated with a counter / reference electrode (330); One or more reference electrodes (330) for use as a reference when measuring electrode potential; a memory (340) for storing data collected from the sensor (320); and An input / output interface (350) is used to send the collected data back to the computer processor assembly (200) via the electrical data bus (220).

4. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 3, characterized in that: The sensor (320) and counter / reference electrode (330) are operably coupled to each other or the counter electrode (330) is implemented within the sensor (320).

5. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 3, characterized in that: The sensor (320) and / or counter / reference electrode (330) are located or housed outside the housing of the sensor assembly (210).

6. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to any one of claims 1 to 5, characterized in that: The computer processor assembly (200) is configured to retrieve data collected from each sensor assembly (210) individually or collectively in any order.

7. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 2, characterized in that: The central controller comprises: A training module (810) configured to train an analytical model (830) using a machine learning process; The training set data (820) includes historical material data (822) and historical environmental data (824). The historical material data (822) includes data related to materials and the data collection process, and the historical environmental data (824) includes data related to conditions or parameters in the data collection process; An analysis model (830), configured to be trained to analyze an object; Processing resources (840) configured to execute software instructions, process data, make parameter control decisions, perform signal processing, read data from memory, write data to memory, and perform other processing operations; memory resources (850) configured to store software instructions associated with controlling the functionality of the system and its components; Communication resources (860), including wired and wireless communication resources, enable the central controller (510) to receive sensor data related to the data collection process through the reference node.

8. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 7, characterized in that: The analysis model includes: A process condition vector (900) includes a plurality of data fields (905). Each data field (905) corresponds to a specific process condition; The processor (910) includes a first neural layer (920a), a second neural layer (920b) and a third neural layer (920c), each neural layer including a plurality of nodes (930).

9. The device for automatically monitoring the effectiveness of a cathodic protection system for a metal pipeline according to claim 8, characterized in that: The operation steps of the analysis model include: Collecting training set data, including historical material data (822) and historical environmental data (824); a processor (910) inputting historical condition data into a central controller analytical model; Generate forecast data based on historical condition data; Compare predicted data with actual measured data; determining whether the forecast data matches the historical condition data based on the comparison generated in the above step; Internal functions associated with the analysis model (830) are adjusted.

10. A method for monitoring the effectiveness of a cathodic protection system for a metal pipeline, characterized in that: The device for monitoring the effectiveness of the cathodic protection system of a metal pipeline as claimed in any one of claims 1 to 9 specifically comprises the following steps: receiving a plurality of voltage readings at respective locations of a plurality of reference electrodes; Determine selected potentials that indicate effectiveness of cathodic protection systems; Each voltage reading from each reference electrode is compared to the selected potential; determining a plurality of voltage differences between respective voltage readings at respective reference electrodes and the selected potential; determining whether each voltage difference is within a threshold level; determining the position of a reference electrode having a voltage difference exceeding a threshold level; Locations of the metallic pipeline corresponding to locations of the reference electrode having a voltage difference exceeding a threshold level are determined to have an ineffective cathodic protection system.

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