Method and apparatus for monitoring health information of a valve

Abstract

Methods, apparatuses, and articles of manufacture are disclosed for monitoring health information of a valve. An example apparatus includes a parameter calculator to calculate an operational value of a health parameter of the valve, a difference calculator to calculate a difference between the operational value of the health parameter and a baseline value of the health parameter, and an alert generator to generate an alert based on the difference to identify a condition of the valve.

Description

Technical Field

[0001] This disclosure relates generally to process control systems, and more specifically to methods and apparatus for monitoring the health information of valves. Background Technology

[0002] In recent years, process control systems (such as those used in chemical, petroleum, and / or other processes) have become increasingly complex with the proliferation of field devices that offer greater processing power than their predecessors. Modern process control systems include a greater number and variety of field devices or instruments for measuring and / or controlling different aspects of the process control environment. In addition to utilizing field devices to monitor and / or control core processes, field devices are increasingly being used for peripheral tasks such as predictive health monitoring.

[0003] A process control system experiencing an increased downtime may suffer from field device failures during operation. Field device failures during operation can also lead to hazardous operating conditions if they provide incorrect or inaccurate data to the process control system. The consequences of faulty field devices (e.g., motors, sensors, valves, etc.) providing electronic feedback to the controller can be mitigated by implementing controlled shutdown of the process equipment or by bypassing the inputs of the faulty field device to the corresponding controller algorithm.

[0004] Field devices within a process control system may be located in challenging environments, such as areas with extreme vibration, high pressure, and / or wide temperature ranges that could accelerate failure. With the increasing sophistication of field devices, process control systems can monitor their predictive health status in these challenging environments. Monitoring field devices using peripheral algorithmic routines can be used to predict potential failures and, instead of stopping the system to replace the field device, enable technicians to replace potentially faulty field devices during scheduled maintenance. Summary of the Invention

[0005] The exemplary apparatus disclosed herein includes: a parameter calculator for calculating the operating value of a valve's health parameter, a difference calculator for calculating the difference between the operating value of the health parameter and a baseline value of the health parameter, and an alarm generator for generating an alarm based on the difference to identify the condition of the valve.

[0006] The exemplary methods disclosed herein include calculating the operating value of a valve's health parameters, calculating the difference between the operating value of the health parameters and the baseline value of the health parameters, and identifying the valve's condition based on the difference.

[0007] An exemplary tangible computer-readable storage medium includes instructions that, when executed, cause a machine to perform at least the following operations: calculate an operating value for a health parameter of a valve, calculate the difference between the operating value of the health parameter and a baseline value of the health parameter, and identify the condition of the valve based on the difference. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of an exemplary valve health monitor device based on the teachings of this disclosure.

[0009] Figure 2 yes Figure 1 A block diagram of an exemplary implementation of an exemplary valve health monitor.

[0010] Figure 3-12 It means that it can be used. Figure 1 A flowchart of an exemplary method for an exemplary valve health monitor to monitor valve health information.

[0011] Figure 13 This is an exemplary graph depicting the health information of the valve during the baseline process.

[0012] Figure 14 It describes the process during operation. Figure 13 An example graph showing the health information of the valve.

[0013] Figure 15 This is an exemplary graph depicting the health information of the valve obtained during the baseline process and operation process.

[0014] Figure 16 It is constructed to execute machine-readable instructions to achieve Figure 3-12 Methods and Figure 1 and / or Figure 2 A block diagram of an exemplary processor platform for an exemplary valve health monitor.

[0015] Wherever possible, the same reference numerals will be used throughout the (multiple) accompanying figures and the accompanying written description to refer to the same or similar parts. Detailed Implementation

[0016] Process control systems have become increasingly complex with the development of individual components featuring increased data acquisition resolution, processing power, and signal conditioning. Process control systems are used to monitor and / or control various aspects of operations performed within a process control environment, such as manufacturing components, handling raw chemical materials, etc. A process control system typically includes at least one controller with accompanying inputs and outputs, which allow the controller(s) to acquire signals from various input field devices and / or instruments and control various output field devices and / or instruments.

[0017] As used herein, the terms "field device" or "instrument" refer to control devices (such as actuators, actuator assemblies, actuator controllers, actuator positioners, sensors, transmitters, valve assemblies, etc.) that can be used throughout a process control system to measure and / or control different aspects of the process control system (e.g., other process control devices). Field devices such as valves (e.g., valve assemblies) can include both electrical and mechanical components. For example, a valve can include electrical components such as digital valve positioners, flow sensors, pressure sensors, valve controllers, etc. In another example, the valve can include mechanical components such as actuators (e.g., hydraulic actuators, pneumatic actuators, etc.), mechanical housings, process connections, etc.

[0018] Field device failures can be caused by a variety of factors, such as continuous operation, environmental factors, and manufacturing defects. In some examples, field devices may operate in high-cycle applications. For instance, a valve may operate continuously in a full-stroke manner, which includes the valve stroke from fully open to fully closed and from fully closed to fully open. Such full-stroke valves can be designed to extend their operational lifespan. However, the timing of unavoidable failures can be unpredictable and may occur during operation. Not knowing when a field device is expected to fail or is about to reach a point of impending failure poses significant problems for the continuous operation of existing process control systems. Sudden field device failures during operation can lead to wear and tear on the field device and the equipment it is monitoring and / or controlling.

[0019] The exemplary valve health monitor (VHM) device disclosed herein relates to process control systems, and more specifically, to monitoring valve health information. Typically, the exemplary VHM device disclosed herein utilizes a controller to obtain information from sensing devices such as actuator controllers (e.g., valve controllers), position sensors (e.g., digital valve positioners, proximity sensors, etc.), process sensors (e.g., flow sensors, pressure sensors, etc.). In some examples, health information may include parameters (e.g., health parameters) that are key indicators of valve health. For example, health information may include parameters such as command or input signals (e.g., stroke setpoints), valve stroke or valve position (e.g., valve position), actuator pressure, drive signals, etc. In some cases, health information may include parameters that can be used to calculate parameters that are key indicators of valve health. For example, health information can be used to calculate dead time (e.g., the time between a change in the command signal and the first movement of the valve position), stroke time (e.g., the time to reach full stroke, the time to reach 98% of full stroke, etc.), time constant parameters (e.g., the time to reach a certain percentage of full stroke), gain values ​​(e.g., the percentage change in valve position divided by the percentage change in the command signal), etc. In some examples, the controller can be triggered to begin obtaining health information from the valve. For example, the valve can send the value of a trigger variable when it begins or ends its full stroke operation.

[0020] In one of the exemplary VHM devices disclosed herein, the controller can obtain baseline health information from a valve during a baseline process. For example, the valve may be a newly manufactured valve that has not yet been put into use (e.g., not yet commissioned). The baseline process may include actuating the valve to perform a full-stroke operation. For example, the VHM device may obtain health information from the valve before, after, or during the operation of one or more full-stroke valves. In some examples, the baseline process may be performed separately from other components. For example, the VHM device may obtain health information from the valve even when it is not coupled to additional components (e.g., process piping, pumps, etc.). In some examples, the baseline process may be performed when the valve is coupled to one or more components. For example, the VHM device may obtain health information from the valve when it is coupled to one or more process connections. For example, the VHM device may obtain health information when the valve performs a full-stroke operation as fluid moves through the valve's process connection. In some cases, the baseline process may occur during the operation of the process control system. For example, the VHM device may periodically obtain health information from the valve and store the health information as baseline health information.

[0021] In one of the exemplary VHM devices disclosed herein, the controller can obtain operational health information from the valve during operation. For example, the valve may be a previously commissioned valve operating in an active process control system. The operation process may include obtaining health information from the valve when it performs a full-stroke operation within the context of routine process control operations. For example, the VHM device may obtain health information from the valve when it is coupled to one or more process connections containing fluid.

[0022] In one of the exemplary VHM devices disclosed herein, the controller can process the health information of the first valve based on health information obtained from the second valve. In some examples, the second valve is the same valve as the first valve (e.g., the same style, the same model, the same size, the same rating, etc.). For example, both the first and second valves can be pneumatically actuated NPS 4 butterfly valves. In some cases, the second valve is similar, but not exactly the same. For example, the second valve may have a similar style, model, type, size, rating, etc., but differ in other aspects (e.g., different size, different rating, etc.).

[0023] The second valve can be in the same process control environment as the first valve. For example, the second valve can be operatively coupled to the same process fluid system as the first valve. In another example, there can be a first group of valves (e.g., identical valves, similar valves, etc.) operatively coupled to the process piping of the first process fluid system. There can also be a second group of ten valves (e.g., identical valves, similar valves, etc.) operatively coupled to the process piping of the second process fluid system. The first group of valves and the second group of valves can be identical to each other, similar to each other, etc. The controller can compare health information from one or more valves in the first group with health information from one or more valves in the second group to identify the condition of one or more valves in the first and second groups, etc.

[0024] Alternatively, the second valve may not be in the same fluid process system as the first valve. For example, the first valve may be operatively coupled to an outdoor fluid process environment, while the second valve may be operatively coupled to an indoor manufacturing process control environment. Health information from the second valve can be transmitted to the first valve via a network. Alternatively, the health information from the second valve can be stored as reference data in a database within the controller of the first valve.

[0025] In some examples, the second valve is not a physical valve. For instance, the second valve may be based on the valve model (e.g., the ideal operating valve). The valve model may include health information at different stages of the valve's lifespan (e.g., ideal dead time parameters at 0 cycles, 100 cycles, 1000 cycles, etc.) and under different operating conditions (e.g., ideal dead time parameters where the ambient temperature of the ideal valve is 20 degrees Celsius, ideal dead time parameters where the process fluid of the ideal valve is 40 degrees Celsius, etc.). The health information of the first valve can be compared with the health information of the second valve to identify the condition of the first valve.

[0026] In one of the exemplary VHM devices disclosed herein, the controller can process the acquired health information. In some examples, the VHM device can compare the processed operational health information with baseline health information to determine a difference. For example, the VHM device can determine a first health parameter of a valve during operation. The VHM device can compare the first health parameter of the valve with a second health parameter, which was obtained during the baseline process. The VHM device can determine the difference between the first health parameter and the second health parameter.

[0027] The VHM device can determine whether a difference meets a threshold (e.g., a difference greater than 100 milliseconds, a difference greater than 5%, etc.). The VHM device can generate a threshold based on the valve's current and / or past health information (e.g., adjusting an existing threshold, creating a new threshold, etc.). Alternatively, the VHM device can generate a threshold based on current and / or past health information obtained from a second valve (e.g., a second valve located in the same process control environment as the valve, or a second valve located in a different process control environment than the valve). The VHM device can identify the condition of a structure based on the difference. For example, the VHM device can identify a valve failure mode or potential failure mode based on the difference. For example, the VHM device can determine the presence of a mechanical fault in a valve actuator based on the difference between a first dead-time health parameter and a second dead-time health parameter, where the difference meets a threshold. In some examples, the VHM device generates a warning based on a difference that meets a threshold. For example, the VHM device can generate a warning based on an identified failure mode, where the identified failure mode is based on a difference that meets a threshold.

[0028] Go to Figure 1The exemplary valve health monitor (VHM) device 100 disclosed herein operates in a process control environment 102 by obtaining health information from a field device 104 (e.g., an electronic valve controller) for a valve assembly 108. In the illustrated example, the field device 104 is an electronic valve controller housed in a housing 106 and coupled to the exemplary pneumatically actuated valve assembly 108, and the electronic valve controller includes at least an actuator 110 and a valve 112 (e.g., a butterfly valve, a gate valve, etc.). However, other valve assemblies (such as electrically actuated valve assemblies, hydraulically actuated valve assemblies, etc.) may be used alternatively or separately. The field device 104 measures one or more parameters of the actuator 110 and / or the valve 112 (e.g., the position of the valve 112) and / or controls the actuator 110 and / or the valve 112. The field device 104 may measure parameters such as valve stroke (e.g., valve position), actuator pressure, drive signal, etc. Field device 104 can control actuator 110 and / or valve 112 via parameters such as commands or input signals (e.g., stroke setpoints). The housing 106 of field device 104 includes connection points for pneumatic tube connections 114. Field device 104 enables pneumatic control of actuator 110 via pneumatic tube connections 114.

[0029] In the example shown, valve assembly 108 is installed in a fluid process system 116 (e.g., a distribution piping system) within a plant environment or processing system. Fluid process system 116 may be located in an environment that exposes field device 104 and / or valve assembly 108 to one or more challenging operating conditions (e.g., extreme vibration, wide temperature range, etc.) and leads to premature failure of field device 104 and / or valve assembly 108 due to accelerated wear. For example, field device 104 and valve assembly 108 may be installed downstream of a positive displacement pump and subjected to significant vibration. Different failure modes of field device 104 and / or valve assembly 108 may occur due to accelerated wear, such as a damaged spring in actuator 110, insufficient air supply to actuator 110 due to decoupling of pneumatic pipe connection 114, mechanical obstructions in valve 112, etc.

[0030] In the example shown, field device 104 is coupled to exemplary VHM device 100. Although field device 104 is... Figure 1The device is depicted as being coupled via a cable 118 comprising one or more wires, but the field device 104 may additionally or alternatively be connected via a wireless network. The exemplary VHM device 100 may be a process control system or part of a process control system that includes a controller for data acquisition and / or processing (e.g., communicatively coupled to a process control system). The exemplary VHM device 100 obtains health information from the field device 104 during operation (e.g., an operational process) to identify the difference between operational health information and previously obtained baseline health information. The difference in health information obtained from the field device 104 may relate to the condition of the valve assembly 108. For example, the condition of the valve assembly 108 may be a degradation or deterioration in structural aspects and / or operational performance of the valve assembly 108, such as decoupling of components attached to the valve assembly 108, decoupling of components attached within the valve assembly 108, corrosion failure of components in the actuator 110, breakage of pneumatic seals in the pneumatic pipe connection 114, etc. Determining whether the difference between the operational health information and the baseline health information increases over time can indicate a degradation or deterioration in the condition (e.g., health) of the valve assembly 108.

[0031] Figure 2 yes Figure 1 This is a block diagram of an exemplary embodiment of the VHM device 100. The exemplary VHM device 100 determines whether the difference between the operating health information of a valve and the baseline health information of the valve identifies a condition of the valve. For example, the VHM device 100 may determine whether the difference between the operating health information obtained from the field device 104 and the baseline health information obtained from the field device 104 identifies a condition of the valve assembly 108. The exemplary VHM device 100 includes an exemplary collection engine 200, an exemplary database 210, an exemplary parameter calculator 220, an exemplary difference calculator 230, an exemplary trend analyzer 240, an exemplary outlier identifier 250, an exemplary fault mode identifier 260, and an exemplary alarm generator 270. The exemplary VHM device 100 is communicatively coupled to the exemplary field device 104 via an exemplary network 280.

[0032] exist Figure 2In the example shown, VHM device 100 includes a collection engine 200 for acquiring, selecting, and processing health information from valves. For example, collection engine 200 may acquire, select, and process health information from field device 104 via network 280. In another example, collection engine 200 may acquire, select, and process health information from database 210. In yet another example, collection engine 200 may acquire, select, and process health information from field device 104 via a direct wired or wireless connection. In some examples, collection engine 200 acquires health information from one or more valves during a period in which baseline health information is acquired (e.g., during post-manufacturing quality checks, during pre-operation commissioning procedures, etc.). In some cases, collection engine 200 generates and / or sends commands (e.g., control commands) to one or more valves during the baseline process. For example, collection engine 200 may generate valve open commands, valve close commands, etc., and / or communicatively couple to... Figure 1 The process control system of valve assembly 108 sends valve opening commands, valve closing commands, etc., to field devices 104 via network 280. In some cases, the collection engine 200 stores the generated and / or sent commands in database 210. In some examples, the collection engine 200 retrieves commands from database 210. Alternatively, the collection engine 200 may obtain commands generated by field devices 104.

[0033] In some examples, the collection engine 200 acquires health information from one or more valves during the time period in which it obtains operational health information (e.g., during valve operation, during process control system operation, etc.). In some examples, the collection engine 200 acquires processed health information, where processed health information includes processed parameters (e.g., scaling parameters, transformation parameters, etc.). In some cases, the collection engine 200 acquires unprocessed health information, where unprocessed health information includes unprocessed parameters (e.g., unscaled parameters, untransformed parameters, etc.).

[0034] In some examples, the collection engine 200 obtains health information from the valve assembly 108 via a communication protocol. For example, the collection engine 200 may obtain health information via one or more communication protocols, such as bus protocols (Controller Area Network (CAN) bus, Modbus, etc.). TM Profibus TM Ethernet protocols (e.g., EtherCAT) TM Profinet TMHealth information is obtained from field device 104 via serial protocols (e.g., RS-232, RS-485, etc.). In some examples, the collection engine 200 obtains health information based on electronic triggering or data acquisition triggering information obtained from valve assembly 108. For example, the collection engine 200 may obtain data acquisition triggering information that includes the value of a trigger variable from field device 104 for valve assembly 108. The collection engine 200 determines whether the data acquisition triggering information includes a start data acquisition command. For example, the collection engine 200 may determine that the obtained trigger variable value includes a start data acquisition command, a stop data acquisition command, etc. In some examples, the collection engine 200 obtains the trigger variable value when valve assembly 108 starts or stops full-stroke valve operation. For example, when field device 104 sends the trigger variable value in response to valve assembly 108 starting full-stroke valve operation, the collection engine 200 may be instructed to start data acquisition.

[0035] In such Figure 2 In the examples shown, the collection engine 200 selects the acquired health information of interest for use by one or more algorithms, processes, programs, etc. In some examples, the collection engine 200 selects one or more subsets of the acquired health information of interest for processing. The collection engine 200 can select one or more subsets of the acquired health information over a time period. For example, the collection engine 200 can select the acquired health information for a specified minute, hour, day, etc. In another example, the collection engine 200 can select the acquired health information when a specific action has occurred (e.g., valve assembly 108 has been operating for more than 100 hours, the valve position is 0% open, etc.). In some cases, the collection engine 200 selects one or more parameters of interest from multiple parameters for processing. For example, the collection engine 200 can select one parameter of interest from a set or list of ten parameters for processing.

[0036] In such Figure 2 In the example shown, the collection engine 200 processes health information by categorizing it into one or more health parameters. For example, health information may include a string of data separated by one or more data delimiters (e.g., hash marker "#", spaces, commas, etc.). Health information located between data delimiters may represent timestamps and / or values ​​of health parameters. Timestamps may indicate the time when field device 104 records and / or processes health information, the time when VHM device 100 obtains health information, etc. In some examples, timestamps include both date and time. However, any other timestamp format may be used alternatively or in addition to the above. For example, timestamps may include a time zone identifier, and the time may be formatted using 12-hour, 24-hour, Unix time, etc.

[0037] In some examples, health information located between data delimiters can represent a description of a health parameter. For example, this description may include the name of the health parameter (e.g., actuator pressure, drive signal, etc.), the unit of measurement for the health parameter (e.g., pound-per-square-inch gauge pressure, milliamperes, etc.), etc. In some examples, the collection engine 200 processes health parameters by determining whether the health parameter is a calculated parameter based on whether further calculation is required. For example, health parameters (such as dead-time health parameters, stroke-time health parameters, time constant health parameters (e.g., t63 time constant health parameters), gain value health parameters, etc.) are calculated parameters. For example, the collection engine 200 may determine that a dead-time health parameter is a calculated health parameter because the dead-time health parameter requires additional calculation for the VHM device 100 to utilize it. In some cases, when the collection engine 200 determines that a health parameter is a calculated parameter, the collection engine 200 modifies the value of a flag (e.g., a flag in computer and / or machine-readable instructions).

[0038] In such Figure 2In the example shown, VHM device 100 includes a database 210 for recording data (e.g., baseline health information, operational health information, baseline values ​​of health parameters, operational values ​​of health parameters, etc.). In some examples, database 210 records flags (e.g., calculation parameter flags) and / or variables associated with the acquired data. For example, VHM device 100 may set calculation parameter flags for health parameters at time of death and store the calculation parameter flags in database 210. Exemplary database 210 can respond to queries for information relating to data in database 210. For example, database 210 may respond to queries for additional data by providing additional data (e.g., one or more data points), by providing an index associated with the additional data in database 210, etc. When there is no additional data in database 210, exemplary database 210 may additionally or alternatively respond to queries by providing an empty index, the end of the database 210 identifier, etc. The exemplary database 210 can be implemented using volatile memory (e.g., synchronous dynamic random access memory (SDRAM), dynamic random access memory (DRAM), RAMBUS dynamic random access memory (RDRAM), etc.) and / or non-volatile memory (e.g., flash memory). The exemplary database 210 can also be implemented using one or more double data rate (DDR) memories (such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc.). The exemplary database 210 can also be implemented using one or more mass storage devices (such as multiple hard disk drives, multiple compact disk drives, multiple digital multifunction disk drives, etc.). Although database 210 is shown as a single database in the example shown, database 210 can be implemented using any number and / or type of database.

[0039] In such Figure 2In the example shown, VHM device 100 includes a parameter calculator 220 for converting (e.g., converting using conversion calculations, converting to different units of measurement, etc.), scaling (e.g., scaling using scaling factors), transforming (e.g., transforming using transformation curves), and / or otherwise processing health parameters obtained from health information into a format usable by the exemplary VHM device 100. In some examples, parameter calculator 220 performs calculations based on unprocessed health parameters, which include analog electrical signal information (e.g., voltage amplitude, current measurement results, etc.), digital electrical signal information (e.g., hexadecimal values ​​based on communication protocol data packets), etc. For example, parameter calculator 220 may calculate valve position parameters based on unprocessed valve position information. Unprocessed valve position information may include voltage amplitude. Parameter calculator 220 may convert the voltage amplitude into a measurement of valve position (e.g., valve 112 is 25% open, etc.). In some examples, parameter calculator 220 stores the calculation results based on unprocessed health parameters in database 210. In some cases, parameter calculator 220 retrieves information from database 210 for processing. For example, parameter calculator 220 may retrieve unprocessed health information, including unprocessed health parameters, from database 210 for processing.

[0040] In some examples, the parameter calculator 220 performs calculations based on processed health parameters. For example, the parameter calculator 220 may calculate actuator pressure based on processed actuator pressure health information. The processed actuator pressure health information may include a first value having a first unit of measurement (e.g., pounds per square inch gauge pressure (PSIG)). The parameter calculator 220 may transform the first value having the first unit of measurement (e.g., PSIG) into a second value having a second unit of measurement (e.g., bar), wherein the second value is based on the transformation from the first unit of measurement to the second unit of measurement.

[0041] In some examples, when calculating health parameters (e.g., unprocessed health parameters, processed health parameters, etc.), parameter calculator 220 calculates differences. For example, parameter calculator 220 may calculate the difference in timestamps when calculating health parameters. For example, parameter calculator 220 may calculate the difference between a first timestamp and a second timestamp when calculating dead-time health parameters. In some cases, parameter calculator 220 determines whether the difference meets a threshold when calculating health parameters. For example, parameter calculator 220 may determine whether the difference between a first valve command value and a second valve command value meets a valve command threshold (e.g., the difference is greater than 0.5 mA). In another example, parameter calculator 220 may determine whether the difference between a first valve position and a second valve position meets a valve position threshold (e.g., the difference is greater than 1%). In some examples, parameter calculator 220 stores the calculation results based on processed health parameters in database 210. In some cases, parameter calculator 220 retrieves processed health parameters from database 210 for processing.

[0042] exist Figure 2 In the example shown, VHM device 100 includes a difference calculator 230 for calculating differences between valve health parameters. In some examples, the difference calculator 230 calculates the difference between the operating value and the baseline value of the health parameter. For example, the difference calculator 230 can calculate the difference between the operating value and the baseline value of the dead time health parameter. In some examples, the difference calculator 230 retrieves the operating value and the baseline value of the health parameter from a database 210. The difference calculator 230 can determine whether the difference meets a threshold. For example, the difference calculator 230 can determine whether the difference between the operating value and the baseline value of the dead time health parameter meets a threshold (e.g., the difference is greater than 100 milliseconds).

[0043] In some cases, the difference calculator 230 calculates the difference between two operated values ​​of a health parameter. For example, the difference calculator 230 can calculate the difference between a first operated value and a second operated value of a dead-time health parameter. In some examples, the difference calculator 230 retrieves the first and second operated values ​​of the health parameter from database 210. The difference calculator 230 can determine whether the difference meets a threshold. For example, the difference calculator 230 can determine whether the difference between the first and second operated values ​​of the dead-time health parameter meets a threshold (e.g., the difference is greater than 100 milliseconds).

[0044] In some examples, the difference calculator 230 calculates the difference between two baseline values ​​of a health parameter. For example, the difference calculator 230 can calculate the difference between a first baseline value and a second baseline value of a dead-time health parameter. In some examples, the difference calculator 230 retrieves the first and second baseline values ​​of the health parameter from a database 210. The difference calculator 230 can determine whether the difference meets a threshold. For example, the difference calculator 230 can determine whether the difference between the first and second baseline values ​​of the dead-time health parameter meets a threshold (e.g., the difference is greater than 100 milliseconds). In response to determining whether the difference meets a threshold, the difference calculator 230 can store the baseline values ​​of the health parameter in the database 210. For example, when the difference between the first and second baseline values ​​of the dead-time health parameter meets a threshold (e.g., the difference is greater than 100 milliseconds), the difference calculator 230 can store the second baseline value of the dead-time health parameter in the database 210.

[0045] In some cases, the difference calculator 230 calculates the difference between two values. For example, the difference calculator 230 can calculate the difference between a first timestamp and a second timestamp. In another example, the difference calculator 230 can calculate the difference between a value and a mean (e.g., average). For example, the difference calculator 230 can calculate the difference between the operated value of the dead-time health parameter and the average baseline value of the dead-time health parameter. In some examples, the difference calculator 230 retrieves these two values ​​from database 210. In some cases, the difference calculator 230 can determine whether the difference meets a threshold. For example, the difference calculator 230 can calculate the difference between the operated value of the dead-time health parameter and the average value of the dead-time health parameter, and determine whether the difference meets a threshold (e.g., the difference is greater than a standard deviation).

[0046] In such Figure 2In the example shown, VHM device 100 includes a trend analyzer 240 for selecting, comparing, and analyzing trends in one or more health parameters of the valve. In some examples, trend analyzer 240 selects a health parameter of interest over a time period and analyzes the value of the selected health parameter. For example, trend analyzer 240 may select the overall response time for valve assembly 108. Trend analyzer 240 may select corresponding data for the overall response time of valve assembly 108. For example, trend analyzer 240 may retrieve corresponding data from database 210. Trend analyzer 240 may determine that a trend indicating the overall response time of valve assembly 108 has increased during that time period. In some examples, trend analyzer 240 determines the trend based on operating values ​​during the time period. In some cases, trend analyzer 240 determines the trend based on trend values. For example, a trend value may be the first value in a first-in-first-out (FIFO) buffer queue acquired and / or processed by collection engine 200. In another example, the trend value of a health parameter may be a baseline value for the health parameter. In another example, the trend value can be a moving window average of a set of values ​​(e.g., baseline value, operation value, etc.), where multiple values ​​are used to calculate the moving window average. For example, trend analyzer 240 can compare the operation value of the dead-time health parameter with the trend value of the dead-time health parameter, where the trend value is the average of the previous ten obtained and / or processed operation values ​​of the dead-time health parameter. In some examples, trend analyzer 240 stores the trend value in database 210. In some cases, trend analyzer 240 retrieves the trend value from database 210.

[0047] In some cases, the trend analyzer 240 performs dynamic analysis based on the valve of interest. The valve may behave differently from cycle to cycle. For example, a valve may have different health parameters based on the density, pressure, temperature, etc., of the process fluid passing through it. A valve may also have different health parameters based on its mechanical structure, for example, via... Figure 1 The irregularities in the air supplied by the pneumatic tube 114 and the varying friction in the valve 112. In some examples, due to the valve dynamics (e.g., varying dynamics), by the trend analyzer 240 and / or more generally by... Figure 1 and / or Figure 2 The calculations performed by the VHM device 100 are used as approximations and / or comparison factors to identify the condition of the valve, such as... Figure 1 Valve assembly 108.

[0048] In some examples, trend analyzer 240 selects, compares, and analyzes one or more health parameters based on operational data of health parameters, according to additional health parameters. For example, trend analyzer 240 can select a first health parameter and a second health parameter, where the second health parameter is a function of the first health parameter. For example, trend analyzer 240 can select a valve position health parameter and an actuator pressure health parameter for a valve. Trend analyzer 240 can select corresponding operational data for the valve position health parameter and the actuator pressure health parameter for the valve assembly 108 during one or more time periods. Trend analyzer 240 can select a first operational value (e.g., 15 PSIG) for the actuator pressure health parameter at a first value (e.g., 40% open) for the valve position health parameter during a first operational time period. Trend analyzer 240 can select a second operational value (e.g., 8 PSIG) for the actuator pressure health parameter at the first value (e.g., 40% open) for the valve position health parameter during a second operational time period. Trend analyzer 240 can compare the first and second values ​​of the actuator pressure health parameter and determine whether the difference meets a threshold (e.g., the difference is greater than 5 PSIG).

[0049] In some cases, trend analyzer 240 selects, compares, and analyzes one or more health parameters based on baseline and operating values, according to additional health parameters. For example, trend analyzer 240 can select a first health parameter and a second health parameter, where the second valve health parameter is a function of the first health parameter. For example, trend analyzer 240 can select a valve position health parameter and an actuator pressure health parameter for a valve. Trend analyzer 240 can select corresponding operating data and baseline data for the valve position health parameter and the actuator pressure health parameter for the valve assembly 108 during one or more time periods. Trend analyzer 240 can select an operating value (e.g., 15 PSIG) for the actuator pressure health parameter at an operating value (e.g., 40% open) for the valve position health parameter during an operating time period. Trend analyzer 240 can select a baseline value (e.g., 22 PSIG) for the actuator pressure health parameter at an operating value (e.g., 40% open) for the valve position health parameter during a baseline time period. The trend analyzer 240 can compare a first value and a second value of the actuator pressure health parameter and determine whether the difference meets a threshold (e.g., the difference is greater than 5 PSI).

[0050] In some examples, trend analyzer 240 performs regression analysis on the relationship between two or more health parameters. For example, trend analyzer 240 can select a first health parameter and a second health parameter, where the second health parameter is a function of the first health parameter. For example, trend analyzer 240 can select a valve position health parameter and an actuator pressure health parameter for a valve. Trend analyzer 240 can select corresponding operating data and baseline data for the valve position health parameter and the actuator pressure health parameter for the valve assembly 108 over a time period. Trend analyzer 240 can determine the range of values ​​for the second health parameter based on the first health parameter. For example, trend analyzer 240 can plot the actuator pressure health parameter based on the valve position health parameter.

[0051] In some examples, trend analyzer 240 determines the slope, y-intercept, and / or boundary values ​​of the relationship between two or more health parameters. For example, trend analyzer 240 may determine the slope of a line, where the line includes values ​​of actuator pressure health parameters based on valve position health parameters. Trend analyzer 240 may determine the slope of the line as the spring rate of valve assembly 108. For example, the spring rate of valve assembly 108 may be the force required to compress the spring of valve assembly 108 a specified distance. Trend analyzer 240 may determine the y-intercept of the line as an estimate of the seat load of valve assembly 108. For example, the seat load estimate may be an estimate of the remaining pressure of the spring of valve assembly 108 when all actuator pressure is removed. Trend analyzer 240 may calculate health parameters based on boundary values ​​of the line (e.g., values ​​where valve 112 is 0% closed or 100% closed). For example, trend analyzer 240 may determine the available force estimate health parameter by determining the actuator pressure at a closed valve position (e.g., valve 112 is 100% closed). For example, the available force estimate could be an estimate of the available force to open valve 112 from the closed position.

[0052] In some cases, trend analyzer 240 determines the value of a parameter by analyzing the relationship between two or more health parameters. For example, trend analyzer 240 can determine the actuator pressure health parameter as a function of the valve position health parameter. Trend analyzer 240 can determine a theoretical estimate (e.g., a bench set estimate) of the actuator pressure health parameter as a function of the valve position health parameter. For example, as valve 112 travels from fully closed to fully open and / or from fully open to fully closed, trend analyzer 240 can determine the average value of the actuator pressure health parameter. For example, as valve 112 travels from open to closed, trend analyzer 240 can determine a first value (e.g., 20 PSIG) of the actuator pressure health parameter at a first value of the valve position health parameter (e.g., 60% open). As valve 112 travels from closed to open, trend analyzer 240 can determine a second value (e.g., 16 PSIG) of the actuator pressure health parameter at the first value of the valve position health parameter (e.g., 60% open). Trend analyzer 240 can use a first value (e.g., 20 PSIG) and a second value (e.g., 16 PSIG) to determine the average value of the actuator pressure health parameter at a first value of the valve position health parameter (e.g., 60% open) (e.g., ((20 PSIG + 16 PSIG) ÷ 2) = 18 PSIG). Trend analyzer 240 can perform similar calculations to determine multiple average values ​​for a range of valve position health parameters (e.g., 0% open to 100% open). In some examples, trend analyzer 240 stores the calculated information (e.g., the average value of the actuator pressure health parameter) in database 210. In some cases, trend analyzer 240 can generate graphs or plots based on the calculated information. For example, trend analyzer 240 can generate a plot depicting the average value of the actuator pressure health parameter as a function of the valve position health parameter.

[0053] In some examples, as valve 112 travels from fully closed to fully open and from fully open to fully closed, trend analyzer 240 calculates the difference between the values ​​of the actuator pressure health parameter. For example, as valve 112 travels from fully closed to fully open, trend analyzer 240 may determine a first value (e.g., 20 PSIG) of the actuator pressure health parameter at the valve position (e.g., 40% open). As valve 112 travels from fully open to fully closed, trend analyzer 240 may determine a second value (e.g., 12 PSIG) of the actuator pressure health parameter at the valve position (e.g., 40% open). Trend analyzer 240 may determine that the difference between the first and second values ​​(e.g., (20 PSIG - 12 PSIG) = 8 PSIG) is either twice the friction estimate of valve assembly 108 or twice the friction estimate of valve assembly 108. By halving the friction estimate by a factor of two (e.g., (8 PSIG ÷ 2) = 4 PSIG), trend analyzer 240 can determine the friction estimate of valve assembly 108 (e.g., 4 PSIG). In some examples, trend analyzer 240 can analyze the friction estimate of valve assembly 108 over a period of time. For example, trend analyzer 240 can determine the friction estimate of valve assembly 108 for each full stroke of valve 112 and compare it with the friction estimate for the life of valve 112.

[0054] In some cases, trend analyzer 240 determines a trend state. The trend state can be a status indicator of valve health. For example, the trend state can be the percentage of available valve health (e.g., the valve is 100% healthy, the valve is 50% healthy, etc.). Trend states can include deteriorating states, fault states, warning states, etc. In some examples, trend analyzer 240 determines the trend state based on whether the operating value of a health parameter is close to a threshold. For example, trend analyzer 240 can determine that the value of a dead-time health parameter is approaching a threshold (e.g., the value of the dead-time health parameter is greater than 500 milliseconds). Trend analyzer 240 can update the trend state to include a warning state based on its determination that the value of the dead-time health parameter is approaching a threshold. In some examples, trend analyzer 240 updates the trend state based on the rate of change of these values. For example, trend analyzer 240 can determine that the value of the dead-time health parameter is approaching a threshold at a first rate (e.g., the value of the dead-time health parameter increases by 5 milliseconds per full-stroke cycle), where the first rate indicates an impending fault. When the trend analyzer 240 determines that the value of the dead time health parameter is increasing at a first rate, the trend analyzer 240 can update the trend status to include the fault status.

[0055] In some examples, trend analyzer 240 determines the trend state based on whether the difference between two operating values ​​of a health parameter is close to a threshold. For example, trend analyzer 240 may determine that the difference between a first operating value and a second operating value of a dead-time health parameter is approaching a threshold (e.g., the difference is greater than 100 milliseconds). Trend analyzer 240 may update the trend state to include a deteriorating state, indicating that the condition of valve assembly 108 associated with the difference is deteriorating (e.g., the structure of valve assembly 108 may have deteriorated to the point of failure).

[0056] In some examples, trend analyzer 240 determines the trend state based on whether the difference between the operating value and the baseline value of a health parameter is close to a threshold. For example, trend analyzer 240 may determine that the difference between the operating value and the baseline value of the dead-time health parameter is approaching a threshold (e.g., the difference is greater than 100 milliseconds). Trend analyzer 240 may update the trend state to include a fault state, indicating that the condition of valve assembly 108 associated with the difference is failing (e.g., valve assembly 108 may be experiencing an impending fault). In some examples, trend analyzer 240 determines an estimated timeline for valve failure. For example, trend analyzer 240 may determine an estimated amount of time until valve assembly 108 experiences a fault based on the trend state. In some examples, trend analyzer 240 stores the trend state in database 210. In some cases, trend analyzer 240 retrieves the trend state from database 210.

[0057] Alternatively or concurrently, trend analyzer 240 may select, compare, and analyze trends of one or more health parameters of the first valve with respect to health information obtained from the second valve (e.g., the same valve as the first valve, an ideal valve, etc.). For example, trend analyzer 240 for the first valve may compare one or more health parameters of the first valve with one or more health parameters of the second valve. Trend analyzer 240 for the first valve may select, compare, and analyze one or more health parameters as a function of additional health parameters (e.g., based on the baseline and / or operating values ​​of the first valve) with respect to health information obtained from the second valve (e.g., based on the baseline and / or operating values ​​of the second valve). In some examples, trend analyzer 240 may compare a regression analysis of the relationship between two or more health parameters of the first valve with a regression analysis of the relationship between two or more health parameters of the second valve. Trend analyzer 240 may obtain information from database 210 and / or from the second valve via network 280. Trend analyzer 240 may update information related to the health information of the first valve (e.g., trend status, threshold, etc.) based on the health information corresponding to the second valve.

[0058] In such Figure 2In the example shown, VHM device 100 includes an outlier identifier 250 to determine whether a calculated health parameter value (e.g., a data point) or an acquired health parameter value (e.g., a data point) is an outlier. In some examples, the outlier identifier 250 calculates at least one mean and standard deviation value for the health parameter of interest. The outlier identifier 250 can determine the difference between the mean and the value of the health parameter over a time period. When the difference meets a threshold (e.g., the difference exceeds one or more standard deviation values), the outlier identifier 250 can determine that the value of the health parameter is an outlier. In some cases, the outlier identifier 250 removes the identified outliers from baseline health information or operational health information of the health parameter. In some examples, the outlier identifier 250 stores the outliers in database 210. In some cases, the outlier identifier 250 retrieves data points, mean values, standard deviation values, etc., from database 210, for example.

[0059] In such Figure 2 In the example shown, VHM device 100 includes a fault mode identifier 260 to identify potential faults or diagnose existing faults in valve assembly 108. In some examples, fault mode identifier 260 determines whether a change in health parameters over a time period can be attributed to or attributable to mechanical degradation or structural conditions of valve assembly 108. For example, fault mode identifier 260 may determine that a decrease in a t63-time health parameter (e.g., the amount of time it takes for the valve to travel 63% of its full stroke) or a stroke-time health parameter (e.g., the amount of time it takes for the valve to travel 98% of its full stroke, the amount of time it takes for the valve to travel 100% of its full stroke, etc.) can be attributed to a failure of the actuator spring in valve assembly 108. In some examples, a damaged actuator spring at one end of the valve stroke behaves like a more stiff actuator spring. For example, when valve 112 moves toward the damaged end (i.e., toward the damaged actuator spring), the values ​​of the t63 time health parameter and / or the stroke time health parameter may increase because the damaged actuator spring provides greater resistance to the movement of valve 112 than normal. When valve 112 moves away from the damaged end, the values ​​of the t63 time health parameter and / or the stroke time health parameter may decrease as the damaged actuator spring assists the movement of valve 112. In some examples, fault mode identifier 260 stores the identified fault modes in database 210. In some cases, fault mode identifier 260 retrieves health information (e.g., baseline health information, operational health information, etc.) from database 210.

[0060] In such Figure 2In the example shown, VHM device 100 includes an alarm generator 270 for generating alarms based on changes in one or more health parameters. In some examples, alarm generator 270 generates an alarm when the difference between a first operating value and a second operating value of the health parameter meets a threshold. In some cases, alarm generator 270 generates an alarm when the difference between the operating value of the health parameter and a baseline value meets a threshold. In some examples, alarm generator 270 uses a predetermined threshold that may depend on a default threshold or user input. In some examples, alarm generator 270 utilizes a calculated threshold. For example, alarm generator 270 may base the calculated threshold on one or more standard deviation values. In some examples, alarm generator 270 stores the threshold and / or the generated alarms in database 210. In some cases, alarm generator 270 obtains the threshold and / or the generated alarms from database 210.

[0061] In some examples, alarm generator 270 can identify the condition of valve assembly 108 when it determines that the difference between a first operating value and a second operating value of a health parameter meets a threshold. For example, alarm generator 270 can identify the condition of valve assembly 108 as structural degradation (e.g., cracks in valve seals, cracks in gas supply connection seals, etc.), structural performance degradation (e.g., pressure loss, mechanical obstruction, etc.), structural failure (e.g., actuator 110 cannot move, valve 112 can no longer maintain pressure, etc.). In response to identifying the structural condition, exemplary alarm generator 270 can generate alarms, such as issuing an alarm sound, propagating an alarm message throughout the process control network, generating a fault log and / or report, displaying the alarm on a monitor, etc.

[0062] In some examples, alarm generator 270 generates thresholds based on current and / or past health information of valve assembly 108 (e.g., adjusting existing thresholds, creating new thresholds, etc.). For example, alarm generator 270 may modify an existing threshold (e.g., a default threshold) for the dead-time health parameter of valve assembly 108 based on a recently calculated dead-time health parameter of valve assembly 108. Alternatively, alarm generator 270 may generate thresholds based on current and / or past health information obtained from a second valve. The second valve may be operatively coupled to fluid process system 116 via process piping. The second valve may be operatively coupled to a second fluid process system separate from fluid process system 116, etc. For example, alarm generator 270 may modify the dead-time health parameter of valve assembly 108 based on acquired dead-time health parameters for similar valves in a fluid process system external to fluid process system 116.

[0063] In such Figure 2In the example shown, network 280 is a bus and / or computer network. For example, network 280 could be an internal controller bus, a process control network, a direct wired connection to an interface of field device 104, etc. In some examples, network 280 is a network capable of communicatively coupling to the Internet. However, network 280 can be implemented using any suitable wired and / or wireless network(s), including, for example, one or more data buses, one or more local area networks (LANs), one or more wireless LANs, one or more cellular networks, one or more fiber optic networks, one or more satellite networks, one or more private networks, one or more public networks, etc. Network 280 enables the exemplary VHM device 100 to communicate with field device 104. As used herein, the phrase "in communication" includes variations thereof, including direct and / or indirect communication via one or more intermediate components, and does not require direct physical (e.g., wired) communication and / or continuous communication, but rather includes selective communication at regular or irregular intervals and one-off events.

[0064] Despite Figure 2 The implementation is shown in the figure. Figure 1 An exemplary manner of the valve health monitor (VHM) device 100, but in Figure 2 One or more of the components, processes, and / or devices shown may be combined, divided, rearranged, omitted, eliminated, and / or implemented in any other way. Furthermore, exemplary collection engines 200, exemplary databases 210, exemplary parameter calculators 220, exemplary difference calculators 230, exemplary trend analyzers 240, exemplary outlier detectors 250, exemplary fault mode detectors 260, exemplary alarm generators 270, and / or more generally... Figure 2 The exemplary VHM device 100 can be implemented by hardware, software, firmware, and / or any combination of hardware, software, and / or firmware. Thus, for example, it can include an exemplary collection engine 200, an exemplary database 210, an exemplary parameter calculator 220, an exemplary difference calculator 230, an exemplary trend analyzer 240, an exemplary outlier identifyr 250, an exemplary fault mode identifyr 260, an exemplary alarm generator 270, and / or more generally... Figure 2Any of the exemplary VHM device 100 may be implemented by one or more analog or digital circuits, logic circuits, (multiple) programmable processors, (multiple) application-specific integrated circuits (ASICs), (multiple) programmable logic devices ((multiple) PLDs) and / or (multiple) field-programmable logic devices ((multiple) FPLDs). When reading any of the device or system claims of this patent to cover purely software and / or firmware implementations, the exemplary collection engine 200, exemplary database 210, exemplary parameter calculator 220, exemplary difference calculator 230, exemplary trend analyzer 240, exemplary outlier identifyr 250, exemplary fault mode identifyr 260, exemplary alarm generator 270 and / or more generally... Figure 2 At least one of the exemplary VHM devices 100 is hereby explicitly defined as including a tangible computer-readable storage device or storage disk (such as a memory storing software and / or firmware, a digital versatile disc (DVD), an optical disc (CD), a Blu-ray disc, etc.). Furthermore, Figure 2 Exemplary VHM devices may include, in addition to Figure 2 Other than or alternative to those shown Figure 2 One or more of the elements, processes and / or devices shown, and / or may include more than one of any or all of the elements, processes and devices shown.

[0065] exist Figure 3-12 The diagram shows the representation used for implementation. Figure 2 A flowchart of an exemplary method of an exemplary VHM device 100 is provided. In these examples, the method can be implemented using machine-readable instructions, which include instructions generated by a processor (such as, in conjunction with, below). Figure 16 The program executed by the processor 1612 shown in the exemplary processor platform 1600 discussed herein may be embodied in software stored on a tangible computer-readable storage medium (such as a CD-ROM, floppy disk, hard disk drive, digital versatile disc (DVD), Blu-ray disc, or memory associated with the processor 1612), but the entire program and / or portions thereof may alternatively be executed by a device other than the processor 1612 and / or embodied in firmware or dedicated hardware. Furthermore, although references... Figure 3-12 The flowchart shown describes an exemplary procedure, but many other methods for implementing the exemplary VHM device 100 may be used instead. For example, the execution order of the boxes may be changed, and / or some boxes in the described boxes may be changed, eliminated, or combined.

[0066] As mentioned above, Figure 3-12The exemplary method can be implemented using coded instructions (e.g., computer and / or machine-readable instructions) stored on a tangible computer-readable storage medium (e.g., hard disk drive, flash memory, read-only memory (ROM), compact disc (CD), digital versatile disc (DVD), cache, random access memory (RAM), and / or any other storage device or disk in which information is stored for any duration (e.g., extended time period, permanent, for short instances, for temporary buffering, and / or for caching information)). As used herein, the term tangible computer-readable storage medium is explicitly defined to include any type of computer-readable storage device and / or disk and excludes propagation signals and transmission media. As used herein, "tangible computer-readable storage medium" and "tangible machine-readable storage medium" are used interchangeably. Additionally or alternatively, Figure 3-12 The exemplary process can be implemented using coded instructions (e.g., computer and / or machine-readable instructions) stored on non-transitory computer and / or machine-readable media (such as hard disk drives, flash memory, read-only memory, compact disks, digital multifunction disks, caches, random access memory, and / or any other storage device or disk in which information is stored for any duration (e.g., extended time periods, permanently, for short instances, for temporary buffering, and / or for caching information)). As used herein, the term non-transitory computer-readable media is explicitly defined to include any type of computer-readable storage device and / or disk and excludes propagation signals and transmission media. As used herein, when the phrase “at least” is used as a transitional term in the preamble of a claim, it is open-ended in the same way as the term “comprising” is open-ended. All other variations of “comprising” and “comprising” are explicitly defined as open-ended terms. All other variations of “including” and “containing” are also defined as open-ended terms. Conversely, the terms “constituting” and / or other forms of composition are defined as closed-ended terms.

[0067] Figure 3 It means that it can be generated by Figure 2The flowchart illustrates an exemplary method 300 for acquiring and processing valve health information executed by an exemplary VHM device 100. Example method 300 begins at block 302 when the VHM device 100 acquires data acquisition trigger information. For example, when valve assembly 108 initiates full-stroke valve operation, the collection engine 200 can acquire data acquisition trigger information from field device 104. At block 304, the VHM device 100 determines whether the data acquisition trigger information includes a start data acquisition command. For example, the collection engine 200 can determine whether the data acquisition trigger information includes a start data acquisition command. If, at block 304, the VHM device 100 determines that the data acquisition trigger information does not include a start data acquisition command, control returns to block 302 to acquire additional data acquisition trigger information. If, at block 304, the VHM device 100 determines that the data acquisition trigger information does include a start data acquisition command, then at block 306, the VHM device 100 acquires and processes the health information. For example, the collection engine 200 can obtain operational health information from the field device 104 for the valve assembly 108 and determine whether the obtained operational health information includes one or more health parameters that require further calculation and / or processing.

[0068] At box 308, VHM device 100 calculates multiple health parameters. For example, parameter calculator 220 may convert (e.g., perform conversion using conversion calculations, convert to different units of measurement, etc.), scale (e.g., scale using a scaling factor), transform (e.g., transform using a transformation curve), and / or otherwise process the health parameters obtained from operational health information into a format that can be used by the exemplary VHM device 100. At box 310, VHM device 100 calculates multiple health parameter differences. For example, difference calculator 230 may determine the difference between a first operational value of a dead-time health parameter obtained during a first time period and a second operational value of a dead-time health parameter obtained during a second time period.

[0069] At box 312, VHM device 100 determines whether at least one health parameter difference meets a threshold. For example, difference calculator 230 can determine whether the difference between a first operating value and a second operating value of the dead-time health parameter meets a threshold (e.g., the difference is greater than 100 milliseconds). If at box 312, VHM device 100 determines that at least one health parameter difference does not meet the threshold, control returns to box 302 to obtain additional data acquisition trigger information. If at box 312, VHM device 100 determines that at least one health parameter difference does meet the threshold, then at box 314, VHM device 100 identifies multiple fault modes. For example, fault mode identifier 260 can determine the difference between the first operating value and the second operating value of the dead-time health parameter based on... Figure 1Obstructions to valve 112. At box 316, VHM device 100 generates an alarm. For example, alarm generator 270 can generate an alarm based on identified fault modes(s).

[0070] exist Figure 4 The diagram shows the combination of data acquisition trigger information ( Figure 3 Additional details for box 302). Figure 4 It means that it can be generated by Figure 2 The flowchart illustrates an exemplary method 400 in which the VHM device 100 executes to obtain data acquisition trigger information. The exemplary method 400 begins at block 402 when the VHM device 100 obtains the valve position. For example, the collection engine 200... Figure 1 The field device 104 for valve 112 obtains the value of the valve position health parameter. At block 404, the VHM device 100 determines whether the valve position is fully open or fully closed. For example, the collection engine 200 can determine that the valve position is fully open based on the value of the valve position health parameter (e.g., a value indicating that the position of valve 112 is 100% open). If at block 404, the VHM device 100 determines that the valve position is not fully open or fully closed (e.g., the valve position is 25% open), control returns to block 402 to obtain the additional valve position. If at block 404, the VHM device 100 determines that the valve position is fully open or fully closed (e.g., valve 112 is 100% open, valve 112 is 100% closed, etc.), then at block 406, the valve receives a command to move in the opposite direction. For example, the field device 104 can receive a command from a communicatively coupled process control system to guide valve 112 from fully open to fully closed. At box 408, VHM device 100 receives data acquisition trigger information. For example, collection engine 200 may receive data acquisition trigger information from field device 104 in response to field device 104 receiving a command to move pilot valve 112 in a relative direction. Data acquisition trigger information may include a start data acquisition command, a stop data acquisition command, etc.

[0071] exist Figure 5 The diagram illustrates the combination of acquiring and processing health information. Figure 3 Additional details for box 306. Figure 5 It means that it can be generated by Figure 2 The flowchart illustrates an exemplary method 500 in which the VHM device 100 executes to obtain and process health information. The exemplary method 500 begins at block 502 when the VHM device 100 obtains health information. For example, the collection engine 200 can... Figure 1Operational health information is obtained from the field device 104 of valve assembly 108. At box 504, the VHM device processes the health information. For example, the collection engine 200 may categorize the operational health information into one or more health parameters based on one or more data delimiters (e.g., hash marker "#", space, comma, etc.). At box 506, the VHM device 100 selects the health parameter of interest to be processed. For example, the collection engine 200 may select the stroke time health parameter of valve assembly 108 to be processed.

[0072] At block 508, VHM device 100 determines whether the health parameter is a calculated parameter. For example, collection engine 200 may determine that the gain value health parameter (e.g., a value calculated by dividing the percentage change in valve position by the percentage change in command signal) requires further calculation. If at block 508, VHM device 100 determines that the health parameter is not a calculated parameter, control proceeds to block 512 to store the information. If at block 508, VHM device 100 determines that the health parameter is a calculated parameter, then at block 510, VHM device 100 sets a calculated parameter flag. For example, when collection engine 200 determines that the health parameter is a calculated parameter, collection engine 200 may set the calculated parameter flag. At block 512, VHM device 100 stores the information. For example, collection engine 200 may store the calculated parameter flag in database 210. At block 514, VHM device 100 determines whether another health parameter of interest exists. For example, collection engine 200 may determine whether another health parameter of interest exists. If at box 514, VHM device 100 determines that there is another health parameter of interest to be processed, then control returns to box 506 to select the other health parameter of interest to be processed; otherwise, exemplary method 500 ends.

[0073] exist Figure 6 The diagram shows the combined calculation of (multiple) health parameters. Figure 3 Additional details for box 308. Figure 6 It means that it can be generated by Figure 2 The flowchart illustrates an exemplary method 600 in which the VHM device 100 performs calculations of multiple health parameters. The exemplary method 600 begins at block 602 when the VHM device 100 selects health information of interest to process. For example, the collection engine 200 may select operational health information of the valve assembly 108 for a time period of interest to process. At block 604, the VHM device 100 selects the health parameters of interest to process. For example, the collection engine 200 may select the dead-time health parameter of the valve assembly 108 to process.

[0074] At block 606, VHM device 100 determines whether a calculation parameter flag has been set for a health parameter. For example, collection engine 200 can determine that a calculation parameter flag has been set for the dead time health parameter of valve assembly 108. If at block 606, VHM device 100 determines that no calculation parameter flag has been set for a health parameter, control continues to block 610 to store information. For example, collection engine 200 can store the value of the actuator pressure health parameter in database 210. If at block 606, VHM device 100 determines that a calculation parameter flag has been set for a health parameter, then at block 608, VHM device 100 calculates the health parameter. For example, parameter calculator 220 can calculate the dead time health parameter based on the selected operational health information of valve assembly 108.

[0075] At box 610, VHM device 100 stores information. For example, parameter calculator 220 can store calculated values ​​of dead-time health parameters in database 210. At box 612, VHM device 100 determines whether another health parameter for a process of interest exists. For example, collection engine 200 can determine whether another health parameter for a process of interest exists. If at box 612, VHM device 100 determines that another health parameter for a process of interest exists, control returns to box 604 to select the other health parameter for the process of interest. If at box 612, VHM device 100 determines that no other health parameter for a process of interest exists (e.g., database 210 returns an empty index, etc.), then at box 614, VHM device 100 determines whether additional health information for a process of interest exists. For example, collection engine 200 can determine whether additional health information for a process of interest exists. If at box 614, VHM device 100 determines that additional health information for a process of interest exists, control returns to box 602 to select the additional health information for the process of interest; otherwise, exemplary method 600 ends.

[0076] Figure 7 It means that it can be generated by Figure 2 The flowchart illustrates an exemplary method 700 in which the VHM device 100 executes to calculate a dead-time health parameter associated with a valve. The exemplary method 700 begins at block 702 when the VHM device 100 selects acquired health information associated with the dead-time health parameter. For example, the collection engine 200 may select acquired operational health information associated with the dead-time health parameter from database 210. The acquired operational health information associated with the dead-time health parameter may include valve command information, valve position information, and timestamp information. At block 704, the VHM device 100 determines a first valve command value. For example, the collection engine 200 may obtain a first valve command value.

[0077] At block 706, VHM device 100 determines a subsequent valve command value. For example, the collection engine 200 may obtain a subsequent valve command value. At block 708, VHM device 100 calculates the difference between the first valve command value and the subsequent valve command value. For example, parameter calculator 220 may calculate the difference between the first valve command value (e.g., 4.0 mA) and the subsequent valve command value (e.g., 20.0 mA). At block 710, VHM device 100 determines whether the valve command difference meets a threshold. For example, parameter calculator 220 may determine whether the difference meets a threshold (e.g., the difference is greater than 0.5 mA). If at block 710, VHM device 100 determines that the valve command difference does not meet the threshold, control returns to block 706 to determine another subsequent valve command value. If at block 710, VHM device 100 determines that the valve command difference does meet the threshold, then at block 712, VHM device 100 determines the first valve position at the subsequent valve command value. For example, parameter calculator 220 can determine that the first value of the valve position health parameter is 0% open at a subsequent valve command value of 20 mA.

[0078] At box 714, VHM device 100 determines a first timestamp at a first valve position. For example, parameter calculator 220 may determine a first timestamp associated with a first value of the valve position health parameter (e.g., 0% open). At box 716, VHM device 100 determines a subsequent valve position. For example, parameter calculator 220 may determine a second value of the valve position health parameter (e.g., 1% open). At box 718, VHM device 100 calculates the difference between the first valve position and the subsequent valve position. For example, parameter calculator 220 may calculate the difference between the first value of the valve position health parameter (e.g., 0% open) and the second value of the valve position health parameter (e.g., 1% open).

[0079] At box 720, VHM device 100 determines whether the valve position difference meets a threshold. For example, parameter calculator 220 can determine whether the valve position difference (e.g., a 1% valve position difference) meets a threshold (e.g., a difference greater than 2%). If at box 720, VHM device 100 determines that the valve position difference does not meet the threshold, control returns to box 716 to determine another subsequent valve position. If at box 720, VHM device 100 determines that the valve position difference does meet the threshold, then at box 722, VHM device 100 determines a timestamp at the subsequent valve position. For example, parameter calculator 220 can determine a second timestamp corresponding to a subsequent value of the valve position health parameter. At box 724, VHM device 100 calculates the difference between the first timestamp and the subsequent timestamp. For example, parameter calculator 220 can calculate the timestamp difference between the first timestamp and the second timestamp. At box 726, VHM device 100 stores the timestamp difference as a dead-time health parameter. For example, the parameter calculator 220 can store the timestamp difference as a dead-zone time health parameter in the database 210.

[0080] exist Figure 8 The diagram shows the combined calculation of the difference between (multiple) health parameters. Figure 3 Additional details for box 310. Figure 8 It means that it can be generated by Figure 2 The flowchart illustrates an exemplary method 800 in which the VHM device 100 performs an operation to calculate the difference between an operational value and a baseline value for one or more health parameters. The exemplary method 800 begins at block 802 when the VHM device 100 selects a health parameter of interest for processing. For example, the collection engine 200 may select an actuator pressure health parameter from the database 210 for processing. At block 804, the VHM device 100 obtains the operational value of the health parameter. For example, the difference calculator 230 may obtain the operational value of the actuator pressure health parameter from the database 210. In some examples, the operational value may be the most recently obtained and processed operational value by the VHM device 100. For example, the operational value may be the first operational value obtained and / or processed by the collection engine 200 and / or stored in a first-in-first-out (FIFO) buffer queue in the database 210. At block 806, the VHM device 100 obtains the baseline value of the health parameter. For example, the difference calculator 230 may obtain the baseline value of the actuator pressure health parameter from the database 210.

[0081] At box 808, VHM device 100 calculates the difference between the operating value and the baseline value. For example, difference calculator 230 can calculate the difference between the operating value of the actuator pressure health parameter and the baseline value of the actuator pressure health parameter. At box 810, VHM device 100 determines whether the difference meets a threshold. For example, difference calculator 230 can determine whether the difference meets a threshold (e.g., difference greater than 10 PSI, difference greater than 500 milliseconds, etc.). If at box 810, VHM device 100 determines that the difference does not meet a threshold, control continues to box 814 to determine if there is another health parameter of interest to process. If at box 810, VHM device 100 determines that the difference does meet a threshold, at box 812, VHM device 100 processes potential outliers. For example, outlier identifier 250 can process potential outliers. At box 814, VHM device 100 determines whether there is another health parameter of interest. For example, collection engine 200 can determine whether there is another health parameter of interest to process. If at box 814, VHM device 100 determines that there is another health parameter of interest to be processed, then control returns to box 802 to select the other health parameter of interest to be processed; otherwise, exemplary method 800 ends.

[0082] exist Figure 9 The diagram illustrates the combination of processing potential outliers ( Figure 8 Additional details for box 812). Figure 9 It means that it can be generated by Figure 2 The flowchart illustrates an exemplary method 900 by which the VHM device 100 performs a process to handle potential outliers of one or more health parameters. The exemplary method 900 begins at block 902 when the VHM device 100 selects a health parameter of interest for processing. For example, the collection engine 200 may select a dead-time health parameter to process. At block 904, the VHM device 100 selects operational information for the health parameter to process. For example, the collection engine 200 may select operational health information for the dead-time health parameter to process. The operational information may include the value of the dead-time health parameter during an exemplary time period (e.g., one hour, one day, one month, etc.) that includes one or more outliers. At block 906, the VHM device 100 calculates the mean and standard deviation values. For example, the outlier identifyr 250 may calculate the mean and standard deviation values ​​based on the operational health information of the dead-time health parameter.

[0083] At box 908, VHM device 100 selects data points of interest for processing. For example, collection engine 200 can select data points within the operational health information of the dead-time health parameter to be processed. At box 910, VHM device 100 calculates the difference between the data points and the average value. For example, outlier detector 250 can calculate the difference between the dead-time health parameter data points and the average value of the operational information of the dead-time health parameter.

[0084] At block 912, VHM device 100 determines whether the difference meets a threshold. For example, outlier identifier 250 can determine whether the difference meets a threshold. In some examples, user input determines the threshold. In some cases, the threshold is one or more standard deviation values. Alternatively or additionally, the threshold can be generated (e.g., adjusted, created, modified, etc.) based on current and / or past health information of valve assembly 108. Alternatively, the threshold can be generated based on current and / or past health information obtained from another valve assembly. If at block 912, VHM device 100 determines that if the difference does not meet the threshold (e.g., the difference is less than one standard deviation value), control continues to block 918 to determine if there is another data point of interest for processing. If at block 912, VHM device 100 determines that if the difference does meet the threshold (e.g., the difference is greater than one standard deviation value), then at block 914, VHM device 100 identifies the data point as an outlier. For example, outlier identifier 250 can identify the data point as an outlier.

[0085] At box 916, VHM device 100 removes a data point from the operational health information of a health parameter. For example, outlier identifier 250 can remove a data point from the operational health information of a dead-time health parameter. In some examples, outliers are stored in database 210 for further analysis and / or alarm generation. At box 918, VHM device 100 determines whether another data point of interest exists. For example, collection engine 200 can determine whether another data point of interest exists. If at box 918, VHM device 100 determines that another data point of interest exists, control returns to box 908 to select the other data point of interest. If at box 918, VHM device 100 determines that no other data point of interest exists (e.g., database 210 returns an empty index, etc.), then at box 920, VHM device 100 determines whether another health parameter of interest exists. For example, collection engine 200 can determine whether another health parameter of interest exists. If at box 918, VHM device 100 determines that there is another health parameter of interest to be processed, then control returns to box 902 to select the other health parameter of interest to be processed; otherwise, exemplary method 900 ends.

[0086] Figure 10 It means that it can be generated by Figure 2 A flowchart illustrates an exemplary method 1000 in which the VHM device 100 performs an action to generate baseline health parameters associated with a valve. For example, the VHM device 100 can be used for... Figure 1 Valve assembly 108 generates baseline values ​​for health parameters. Exemplary method 1000 begins at block 1002 when field device 104 commands the valve to move to the closed position. Alternatively, VHM device 100 may command the valve to move to the closed position, or a user may manually move the valve to the closed position. For example, collection engine 200 may send a command to a process control system communicatively coupled to field device 104 to guide valve 112 to a closed position (e.g., approximately 100% closed). At block 1004, VHM device 100 acquires and processes health information. For example, collection engine 200 may acquire and process baseline health information from field device 104 for valve assembly 108. In some examples, VHM device 100 acquires and processes baseline health information according to exemplary method 500. At block 1006, VHM device 100 calculates health parameters. For example, parameter calculator 220 may calculate one or more health parameters based on the acquired baseline health information. In some cases, VHM device 100 calculates health parameters according to exemplary method 600.

[0087] At box 1008, VHM device 100 obtains the valve position. For example, collection engine 200 may obtain the value of the valve position health parameter of valve assembly 108 (e.g., valve 112 is 25% open). At box 1010, VHM device 100 determines whether the valve has moved to the closed position. For example, collection engine 200 may determine whether valve 112 has moved to the closed position (e.g., where valve 112 is approximately 100% closed). If at box 1010, VHM device 100 determines that the valve has not moved to the closed position, control returns to box 1008 to obtain another valve position. If at box 1010, VHM device 100 determines that the valve has indeed moved to the closed position, then at box 1012, field device 104 commands the valve to move to the open position. Alternatively, VHM device 100 may command the valve to move to the open position, or the user may manually move the valve to the open position. For example, the collection engine 200 can send commands to the process control system communicatively coupled to the field device 104 to guide the valve 112 to move to an open position (e.g., where the valve 112 is approximately 100% open).

[0088] At box 1014, VHM device 100 acquires and processes health information. For example, collection engine 200 may acquire and process baseline health information from field device 104 for valve assembly 108. In some examples, VHM device 100 acquires and processes baseline health information according to exemplary method 500. At box 1016, VHM device 100 calculates health parameters. For example, parameter calculator 220 may calculate one or more health parameters based on the acquired baseline health information. In some cases, VHM device 100 calculates health parameters according to exemplary method 600.

[0089] At box 1018, VHM device 100 obtains the valve position. For example, collection engine 200 may obtain the value of the valve position health parameter for valve assembly 108 (e.g., valve 112 is 25% closed). At box 1020, VHM device 100 determines whether the valve has moved to the open position. For example, collection engine 200 may determine whether valve 112 has moved to the open position (e.g., where valve 112 is approximately 100% open). If at box 1020, VHM device 100 determines that the valve has not moved to the open position, control returns to box 1018 to obtain another valve position. If at box 1020, VHM device 100 determines that the valve has indeed moved to the open position, then at box 1022, VHM device 100 calculates the difference between the health parameter at the open and closed positions. For example, difference calculator 230 may calculate the difference between the baseline value of the dead time health parameter at the open position and the baseline value of the dead time health parameter at the closed position.

[0090] At box 1024, VHM device 100 determines whether all differences satisfy their respective thresholds. For example, difference calculator 230 may determine whether the difference between the dead-time health parameter value at the open position and the dead-time health parameter value at the closed position satisfies a threshold (e.g., the difference is greater than 10 milliseconds). If at box 1024, VHM device 100 determines that not all differences satisfy their respective thresholds, control returns to box 1002 to command the valve to move to the closed position. If at box 1024, VHM device 100 determines that all differences satisfy their respective thresholds, then at box 1026, the VHM device generates a baseline value for the health parameter. For example, difference calculator 230 may store the calculated baseline value of the health parameter at the open position in database 210. In another example, difference calculator 230 may store the calculated baseline value of the health parameter at the closed position in the database. Alternatively, exemplary method 1000 may be performed for a single health parameter.

[0091] Figure 11 It means that it can be generated by Figure 2The flowchart illustrates an exemplary method 1100 in which the VHM device 100 performs an analysis of trends in the operating values ​​of health parameters associated with a valve. The exemplary method 1100 begins at block 1102 when the VHM device selects a health parameter of interest for processing. For example, the collection engine 200 can select... Figure 1 The actuator pressure health parameters associated with valve assembly 108 are processed. At block 1104, VHM device 100 selects an operating value from the queue. For example, collection engine 200 may retrieve the operating value of the actuator pressure health parameter from database 210. In some examples, the operating value may be the most recently acquired and / or processed operating value by VHM device 100. For example, the operating value may be the first operating value in a first-in-first-out (FIFO) buffer queue acquired and / or processed by collection engine 200. At block 1106, VHM device 100 calculates the difference between the operating value and a trend value. For example, trend analyzer 240 may calculate the difference between the operating value of the actuator pressure health parameter and the trend value of the actuator pressure health parameter.

[0092] At box 1108, VHM device 100 determines whether the difference meets a threshold. For example, trend analyzer 240 can determine whether the difference meets a threshold (e.g., the difference is greater than 10 PSI). If at box 1108, VHM device 100 determines that the difference does not meet the threshold, control continues to box 1112 to update the trend value. If at box 1108, VHM device 100 determines that the difference meets the threshold, then at box 1110, VHM device 100 updates the trend state. For example, trend analyzer 240 can update the trend state of the actuator pressure health parameter.

[0093] At box 1112, VHM device 100 updates the trend value. For example, trend analyzer 240 may update the trend value of the actuator pressure health parameter. In some examples, trend analyzer 240 replaces the previous trend value with the operating value selected at box 1104. In some cases, trend analyzer 240 recalculates the moving-window average to include the operating value selected at box 1104. At box 1114, VHM device 100 determines whether there is another health parameter of interest to process. For example, collection engine 200 may determine whether there is another health parameter of interest to process. If at box 1114, VHM device 100 determines that there is another health parameter of interest, control returns to box 1102 to select the other health parameter of interest to process; otherwise, exemplary method 1100 ends.

[0094] Figure 12 It means that it can be generated by Figure 2The flowchart illustrates an exemplary method 1200 in which the VHM device 100 executes to analyze trends in the values ​​of two or more health parameters associated with a valve. The exemplary method 1200 begins at block 1202 when the VHM device 100 selects a first health parameter of interest for processing. For example, the collection engine 200 can select with… Figure 1 The valve position health parameter associated with valve assembly 108. At block 1204, VHM device 100 selects a second health parameter of interest to process. For example, collection engine 200 can select a valve position health parameter associated with... Figure 1 The actuator pressure health parameters associated with valve assembly 108. At block 1206, VHM device 100 selects an operating value for a first health parameter. For example, trend analyzer 240 can select an operating value for a valve position health parameter from database 210. At block 1208, VHM device 100 selects an operating value for a second health parameter corresponding to the operating value of the first health parameter. For example, trend analyzer 240 can select an operating value for an actuator pressure health parameter (e.g., 12PSIG) corresponding to the operating value of the valve position health parameter (e.g., valve 112 is in a 40% open position).

[0095] At box 1210, VHM device 100 selects a baseline value for a first health parameter corresponding to the operating value of the first health parameter. For example, trend analyzer 240 can select a baseline value for a valve position health parameter (e.g., valve 112 is 40% open) corresponding to the operating value of the valve position health parameter (e.g., valve 112 is 40% open). At box 1212, VHM device 100 selects a baseline value for a second health parameter corresponding to the baseline value of the first health parameter. For example, trend analyzer 240 can select a baseline value for an actuator pressure health parameter (e.g., 14PSIG) corresponding to the baseline value of the valve position health parameter (e.g., valve 112 is 40% open).

[0096] At box 1214, VHM device 100 calculates the difference between the operating value of the second health parameter and the baseline value. For example, trend analyzer 240 may calculate the difference between the operating value of the actuator pressure health parameter (e.g., 12 PSIG) and the baseline value of the actuator pressure health parameter (e.g., 14 PSIG). At box 1216, VHM device 100 determines whether the difference meets a threshold. For example, trend analyzer 240 may determine whether the difference (e.g., 12 PSIG - 10 PSIG = 2 PSIG) meets a threshold (e.g., the difference is greater than 5 PSIG). If at box 1216, VHM device 100 determines that the difference does not meet the threshold, control continues to box 1220 to determine whether there is another second health parameter of interest to be processed. If at box 1216, VHM device 100 determines that the difference does meet the threshold, at box 1218, VHM device 100 updates the trend state. For example, trend analyzer 240 may update the trend state.

[0097] At box 1220, VHM device 100 determines whether a second health parameter for processing of interest exists. For example, collection engine 200 may determine that another health parameter exists that can be analyzed relative to the valve position health parameter. If at box 1220, VHM device 100 determines that another second health parameter for processing of interest exists, control returns to box 1204 to select the other second health parameter for processing of interest. If at box 1220, VHM device 100 determines that no other second health parameter for processing of interest exists, then at box 1222, VHM device 100 determines whether a first health parameter for processing of interest exists. For example, collection engine 200 may determine that another health parameter exists that can be used as a base reference, relative to which the additional health parameter can be analyzed. If at box 1222, VHM device 100 determines that another first health parameter for processing of interest exists, control returns to box 1202 to select the other first health parameter for processing of interest; otherwise, exemplary method 1200 ends.

[0098] Figure 13 It is a graph depicting valve health information (e.g., baseline health information) during the baseline process. For example, Figure 13 The graph can depict the curves obtained during the baseline process. Figure 1 Baseline health information of valve assembly 108. Figure 13The graph depicts curve 1300 of actuator pressure 1302 according to valve position 1304. Actuator pressure 1302 is measured in pounds per square inch (PSIG). Valve position 1304 is measured as a percentage. Valve position axis 1306 ranges from -20% to 120%, where 0% refers to valve position 1304 being 0% open or fully closed, and 100% refers to valve position 1304 being 100% open or fully open.

[0099] In some examples, VHM device 100 displays plot 1300 based on baseline health information. For example, parameter calculator 220 can display plot 1300 to calculate baseline values ​​for the health parameters of valve assembly 108. In some cases, parameter calculator 220 is used for baseline processes. Figure 1 Each complete full-stroke operation of valve assembly 108 (e.g., valve 112 traveling from fully closed to fully open and back to fully closed) generates plot 1300. Parameter calculator 220 can calculate health parameters such as seat load estimate 1308, bench equipment estimate 1310 (e.g., theoretical actuator pressure estimate), double friction estimate 1312, friction estimate, spring stiffness, available force estimate 1314, etc. VHM device 100 can store the calculated health parameters in database 210. For example, parameter calculator 220 can store baseline values ​​for seat load estimate 1308, bench equipment estimate 1310, double friction estimate 1312, friction estimate, spring stiffness, available force estimate 1314, etc., in database 210.

[0100] exist Figure 13 In the example shown, parameter calculator 220 calculates the baseline value of seat load estimate 1308 by calculating the difference between the actuator pressure 1302 at 1% valve position 1304 and the actuator pressure 1302 at 0% valve position 1304. If all actuator pressure 1302 is removed from valve 112, the baseline value of seat load estimate 1308 can be the amount of pressure from the spring of valve 112. For example, parameter calculator 220 can determine the baseline value of seat load estimate as approximately 5 PSIG based on plot 1300 (e.g., (4 PSIG at 1% valve position) – (-1 PSIG at 0% valve position) = 5 PSIG).

[0101] exist Figure 13In the example shown, VHM device 100 calculates a baseline value for bench equipment estimate 1310 based on actuator pressure 1302 at 0% valve position 1304 and actuator pressure 1302 at 100% valve position 1304. The exemplary VHM device 100 then infers a line for bench equipment estimate 1310 including actuator pressure 1302 at 0% valve position 1304 and actuator pressure 1302 at 100% valve position 1304. For example, parameter calculator 220 can determine actuator pressure 1302 at 0% valve position 1304 as approximately 7 PSIG based on plot 1300. Parameter calculator 220 can determine actuator pressure 1302 at 100% valve position 1304 as approximately 28 PSIG based on plot 1300. The parameter calculator 220 can deduce the line between (1) the actuator pressure 1302 of 7 PSIG at 0% valve position 1304 and (2) the actuator pressure 1302 of 28 PSIG at 100% valve position 1304 to determine the line for the bench device estimate 1310.

[0102] exist Figure 13 In the example shown, VHM device 100 calculates a baseline value for twice the friction estimate 1312 by dividing the actuator pressure 1302 at valve position 1304 on line 1316 by the actuator pressure 1302 at the same valve position 1304 on line 1318. For example, parameter calculator 220 can calculate a baseline value for twice the friction estimate 1312 at 40% valve position 1304 by dividing the actuator pressure 1302 at 40% valve position 1304 for line 1316 (e.g., approximately 20 PSIG) by the actuator pressure 1302 at 40% valve position 1304 (e.g., approximately 15 PSIG). For example, parameter calculator 220 can calculate a baseline value for twice the friction estimate 1312 at 40% valve position 1304 as approximately 1.33 (e.g., 20 PSIG ÷ 15 PSIG ≈ 1.33) based on graph 1300. In some cases, parameter calculator 220 can calculate the baseline value of friction estimate by halving the baseline value of twice the friction estimate 1312. For example, parameter calculator 220 can calculate the baseline value of friction estimate at 40% valve position 1304 as approximately 0.67 based on plot 1300 (e.g., (20 PSIG ÷ 15 PSIG) ÷ 2 ≈ 0.67).

[0103] exist Figure 13 In the example shown, VHM device 100 calculates the baseline value of the spring stiffness by calculating the slope of the line estimated by the working group for line 1310. For example, parameter calculator 220 can calculate the slope of the baseline value of line 1310 estimated by the working group to determine... Figure 1The baseline value of the spring stiffness of valve 112. For example, parameter calculator 220 can determine the actuator pressure 1302 at 60% valve position 1304 as approximately 21 PSIG. Parameter calculator 220 can determine the actuator pressure 1302 at 20% valve position 1304 as approximately 12 PSIG. Parameter calculator 220 can calculate the baseline spring stiffness value as approximately 0.225 based on plot 1300 (e.g., (21 PSIG - 12 PSIG) ÷ (60% - 20%) ≈ 0.225).

[0104] In such Figure 13 In the example shown, VHM device 100 calculates a baseline value for available force estimate 1314 by calculating the difference between actuator pressure 1302 at 100% valve position 1304 and actuator pressure 1302 at 99% valve position 1304. The baseline value for available force estimate 1314 can be the amount of available pressure from the spring of valve 112 used to begin closing valve 112 (e.g., the amount of force required to move valve 112 from a 100% open valve position to a 99% open valve position). For example, parameter calculator 220 can determine the baseline value for available force estimate 1314 as approximately 3 PSIG based on plot 1300 (e.g., (37 PSIG at 100% valve position) – (34 PSIG at 99% valve position) = 3 PSIG).

[0105] Figure 14 It is a graph depicting the valve's health information during operation. For example, Figure 14 The graph can depict the results obtained during normal operation. Figure 1 Operational health information of valve assembly 108. Figure 14 The graph depicts a plot 1400 of the actuator pressure 1402 according to valve position 1404. Actuator pressure 1402 is measured in pounds per square inch (PSIG). Valve position 1404 is measured as a percentage. Valve position axis 1406 ranges from -20% to 120%, where 0% refers to valve position 1404 being 0% open or fully closed, and 100% refers to valve position 1404 being 100% open or fully open.

[0106] In some examples, the VHM device 100 displays graph 1400 based on operational health information. For example, parameter calculator 220 may display graph 1400 to calculate operational values ​​of health parameters for valve assembly 108. In some cases, parameter calculator 220 is used for parameters during operation. Figure 1Each complete full-stroke operation of valve assembly 108 (e.g., valve 112 traveling from fully closed to fully open and from fully open back to fully closed) generates plot 1400. Parameter calculator 220 can calculate health parameters such as seat load estimate 1408, work group estimate 1410 (e.g., theoretical actuator pressure estimate), double friction estimate 1412, friction estimate, spring stiffness, available force estimate 1414, etc. VHM device 100 can store the calculated health parameters in database 210. For example, parameter calculator 220 can store operational values ​​such as seat load estimate 1408, work group estimate 1410, double friction estimate 1412, friction estimate, spring stiffness, available force estimate 1414, etc., in database 210.

[0107] exist Figure 14 In the example shown, parameter calculator 220 calculates the operating value of seat load estimate 1408 by calculating the difference between the actuator pressure 1402 at 1% valve position 1404 and the actuator pressure 1402 at 0% valve position 1404. If all actuator pressure 1402 is removed from valve 112, the operating value of seat load estimate 1408 can be the amount of pressure from the spring of valve 112. For example, parameter calculator 220 can determine the operating value of seat load estimate as approximately 3 PSIG based on plot 1400 (e.g., (1 PSIG at 1% valve position) – (-2 PSIG at 0% valve position) = 3 PSIG).

[0108] exist Figure 14 In the example shown, VHM device 100 calculates the operating value of workgroup estimate 1410 based on actuator pressure 1402 at 0% valve position 1404 and actuator pressure 1402 at 100% valve position 1404. The exemplary VHM device 100 then infers a line for the workgroup estimate 1410, which includes actuator pressure 1402 at 0% valve position 1404 and actuator pressure 1402 at 100% valve position 1404. For example, parameter calculator 220 can determine the actuator pressure 1402 at 0% valve position 1404 as approximately 3 PSIG based on plot 1400. Parameter calculator 220 can determine the actuator pressure 1402 at 100% valve position 1404 as approximately 27 PSIG based on plot 1400. The parameter calculator 220 can deduce the line between (1) the actuator pressure 1402 of 3 PSIG at 0% valve position 1404 and (2) the actuator pressure 1402 of 27 PSIG at 100% valve position 1404 to determine the line for the working group estimate 1410.

[0109] exist Figure 14In the example shown, VHM device 100 calculates the operating value of double friction estimate 1412 by dividing the actuator pressure 1402 at valve position 1404 on line 1416 by the actuator pressure 1402 at the same valve position 1404 on line 1418. For example, parameter calculator 220 can calculate the operating value of double friction estimate 1412 at 40% of valve position 1404 by dividing the actuator pressure 1402 at 40% of valve position 1404 on line 1416 (e.g., approximately 15 PSIG) by the actuator pressure 1402 at line 1418 (e.g., approximately 11 PSIG). For example, parameter calculator 220 can calculate the operating value of double friction estimate 1412 at 40% of valve position 1404 as approximately 1.36 (e.g., 15 PSIG ÷ 11 PSIG ≈ 1.36) based on plot 1400. In some cases, the parameter calculator 220 can calculate the operating value of the friction estimate by halving the baseline value of twice the friction estimate 1412. For example, the parameter calculator 220 can calculate the operating value of the friction estimate at 40% valve position 1404 as approximately 0.68 based on plot 1400 (e.g., (15 PSIG ÷ 11 PSIG) ÷ 2 ≈ 0.68).

[0110] exist Figure 14 In the example shown, VHM device 100 calculates the operating value of the spring stiffness by calculating the slope of the line estimated by the bench equipment 1410. For example, parameter calculator 220 can calculate the slope of the operating value of the line estimated by the work group 1410 to determine... Figure 1 The operating value of the spring stiffness of valve 112. For example, parameter calculator 220 can determine the actuator pressure 1402 at 60% valve position 1404 as approximately 17 PSIG. Parameter calculator 220 can determine the actuator pressure 1402 at 20% valve position 1404 as approximately 8 PSIG. Parameter calculator 220 can calculate the operating value of the spring stiffness based on plot 1400 as approximately 0.225 (e.g., (17 PSIG - 7 PSIG) ÷ (60% - 20%) ≈ 0.250).

[0111] exist Figure 14In the example shown, VHM device 100 calculates the operating value of available force estimate 1414 by calculating the difference between actuator pressure 1402 at 100% valve position 1404 and actuator pressure 1402 at 99% valve position 1404. The operating value of available force estimate 1414 can be the amount of available pressure from the spring of valve 112 used to begin closing valve 112 (e.g., the amount of force required to move valve 112 from a 100% open valve position to a 99% open valve position). For example, parameter calculator 220 can determine the operating value of available force estimate 1414 as approximately 2 PSIG based on plot 1400 (e.g., (32 PSIG at 100% valve position) – (30 PSIG at 99% valve position) = 2 PSIG).

[0112] Figure 15 This is an example form 1500 that depicts exemplary health information. For example, form 1500 may be depicted in... Figure 13 Baseline process and Figure 14 Health information obtained during the operation process. Table 1500 shows exemplary health information that can be obtained and / or processed by the VHM device 100. For example, the VHM device 100 can obtain health information from... Figure 1 The field device 104 for valve assembly 108 acquires and / or processes the exemplary health information shown in Table 1500. Table 1500 depicts exemplary health information for health parameters, such as seat load estimate 1502, benchtop device estimate at 0% valve position 1504, friction estimate at 40% valve position 1506, spring stiffness 1508, and available force estimate 1510. Although five health parameters are listed in Table 1500, additionally or alternatively, fewer or more than five health parameters may exist that are acquired and / or processed by VHM device 100.

[0113] exist Figure 15 In the example shown, Table 1500 depicts a baseline process column 1512, an operating process column 1514, an absolute value difference column 1516, and an alarm threshold column 1518. The baseline process column 1512 details exemplary values ​​of health parameters obtained during the baseline process. For example, the baseline process column 1512 may be based on… Figure 13 The plot 1300 details exemplary values. The operation procedure column 1514 details exemplary values ​​of health parameters obtained during the operation procedure. For example, the operation procedure column 1514 may be based on... Figure 14The diagram 1400 details exemplary values. The absolute value difference column 1516 details exemplary values ​​in which values ​​are calculated by determining the absolute value difference between the baseline process column 1512 and the operation process column 1514. Alternatively, the VHM device 100 may determine a relative value difference between the baseline process column 1512 and the operation process column 1514, wherein the relative value difference may produce a negative value.

[0114] In such Figure 15 In the example shown, Table 1500 includes an alarm threshold column 1518 to detail exemplary values ​​of health parameter thresholds indicating the conditions used to generate an alarm. For example, alarm generator 270 may generate an alarm if the value in the absolute difference column 1516 is greater than the value in the alarm threshold column 1518. Alternatively, alarm generator 270 may generate an alarm if the value in the procedure column 1514 is greater than or less than a permissible value. In some examples, alarm generator 270 employs a predefined threshold that may depend on user input. In some cases, exemplary alarm generator 270 utilizes a calculated threshold. For example, alarm generator 270 may base a calculated threshold on one or more standard deviation values. For example, the value in alarm threshold column 1518 may be determined by... Figure 1 The values ​​obtained during the baseline process of valve assembly 108 are the result of the associated average and / or standard deviation values. In another example, the values ​​in alarm threshold column 1518 can be the result of user input.

[0115] exist Figure 15 In the example shown, Table 1500 depicts exemplary health information that can be obtained and / or processed by VHM device 100. VHM device 100 can use the health information in Table 1500 to determine whether to generate an alarm. In the example shown, the seat load estimate 1502 is 5 PSIG during the baseline process and 3 PSIG during the operation process. The absolute difference between the baseline process value and the operation process value of seat load estimate 1502 is 2 PSIG (e.g., 5 PSIG - 3 PSIG = 2 PSIG). In the example shown, the alarm threshold for seat load estimate 1502 is 1 PSIG. An alarm can be generated in response to determining that the absolute difference meets the alarm threshold (e.g., the absolute difference of 2 PSIG is greater than the alarm threshold of 1 PSIG). For example, alarm generator 270 can generate an alarm when the absolute difference meets the alarm threshold. Alarm generator 270 can generate alarms, such as issuing alarm sounds, propagating alarm messages throughout the process control network, generating fault logs and / or reports, and displaying alarms on displays.

[0116] In such Figure 15In the example shown, the friction estimate 1506 at 40% valve position is 0.67 during the baseline process and 0.68 during the operating process. The absolute difference between the baseline process value and the operating process value of the friction estimate 1506 at 40% valve position is 0.01 (e.g., 0.68 - 0.67 = 0.01). In the example shown, the alarm threshold for the friction estimate 1506 at 40% valve position is 0.1. An alarm may not be generated in response to determining that the absolute difference does not meet the alarm threshold (e.g., an absolute difference of 0.01 is less than the alarm threshold of 0.1). For example, alarm generator 270 may not generate an alarm when the absolute difference does not meet the alarm threshold.

[0117] Figure 16 It is capable of executing instructions to achieve Figure 3-12 Methods and Figure 2 A block diagram of an exemplary processor platform 1600 for a device. The processor platform 1600 may be, for example, a programmable logic controller, a server, a personal computer, a mobile device (e.g., a cellular phone, a smartphone, or an iPad). TM Tablet computers, personal digital assistants (PDAs), internet devices, or any other type of computing device.

[0118] The processor platform 1600 shown in the example includes a processor 1612. The processor 1612 shown in the example is hardware. For example, the processor 1612 may be implemented by one or more integrated circuits, logic circuits, microprocessors, or controllers from any desired family or manufacturer.

[0119] The processor 1612 of the illustrated example includes local memory 1613 (e.g., cache). The processor 1612 of the illustrated example executes instructions to implement an exemplary valve health monitor device 100, which includes an exemplary collection engine 200, an exemplary parameter calculator 220, an exemplary difference calculator 230, an exemplary trend analyzer 240, an exemplary anomaly identifier 250, an exemplary fault mode identifier 260, and an exemplary alarm generator 270. The processor 1612 of the illustrated example communicates via bus 1618 with main memory including volatile memory 1614 and non-volatile memory 1616. The volatile memory 1614 may be implemented using synchronous dynamic random access memory (SDRAM), dynamic random access memory (DRAM), RAMBUS dynamic random access memory (RDRAM), and / or any other type of random access storage device. The non-volatile memory 1616 may be implemented using flash memory and / or any other desired type of storage device. Access to main memory 1614 and 1616 is controlled by the memory controller.

[0120] The processor platform 1600 shown in the example also includes interface circuitry 1620. Interface circuitry 1620 can be implemented using any type of interface standard, such as an Ethernet interface, a Universal Serial Bus (USB) interface, and / or a PCI Express interface.

[0121] In the example shown, one or more input devices 1622 are connected to interface circuitry 1620. The input devices 1622 allow users to input data and commands into processor 1612. The input devices can be implemented, for example, audio sensors, microphones, cameras (still or video), keyboards, buttons, mice, touchscreens, trackpads, tracking balls, isopoint devices, and / or speech recognition systems.

[0122] One or more output devices 1624 are also connected to the interface circuitry 1620 of the illustrated example. The output devices 1624 may be implemented, for example, by display devices (e.g., light-emitting diodes (LEDs), organic light-emitting diodes (OLEDs), liquid crystal displays, cathode ray tube displays (CRTs), touchscreens, haptic output devices, printers, and / or speakers). Therefore, the interface circuitry 1620 of the illustrated example typically includes a graphics driver card, a graphics driver chip, or a graphics driver processor.

[0123] The interface circuit 1620 of the example shown also includes communication devices (such as transmitters, receivers, transceivers, modems, and / or network interface cards) to facilitate data exchange with external machines (such as any type of computing device) via a network 1626 (e.g., Ethernet connection, digital subscriber line (DSL), telephone line, coaxial cable, cellular telephone system, etc.).

[0124] The processor platform 1600 shown in the example also includes one or more mass storage devices 1628 for storing software and / or data. Examples of such mass storage devices 1628 include floppy disk drives, hard disk drives, optical disk drives, Blu-ray disc drives, RAID systems, magnetic storage media, and digital multifunction disc (DVD) drives. The exemplary mass storage device 1628 implements the exemplary database 210.

[0125] Figure 3-12 The encoded command 1632 can be stored in a mass storage device 1628, a volatile memory 1614, a non-volatile memory 1616, and / or a removable tangible computer-readable storage medium (such as a CD or DVD).

[0126] As can be understood from the foregoing, the valve health monitoring device and method disclosed above provide predictive health monitoring of valves to monitor their condition. Therefore, the valve's operational lifecycle can be optimized by operating the valve until its condition has been identified, thus preventing premature replacement. Furthermore, the identification of the valve's condition generates warnings to personnel, allowing for preventative maintenance and / or replacement of the valve before potential failures that could cause undesirable downtime in the process control environment.

[0127] While certain exemplary methods, apparatuses, and articles of manufacture have been disclosed herein, the scope of this patent is not limited thereto. Rather, this patent covers all methods, apparatuses, and articles of manufacture that fall fully within the scope of the claims of this patent.

Claims

1. An apparatus for monitoring the health status of a valve assembly, the valve assembly including an actuator and a valve, the apparatus comprising: A collection engine that, in response to the valve assembly initiating full-stroke valve operation, acquires operational health information from the valve assembly, the full-stroke valve operation including movement of the valve between a fully open position and a fully closed position, the operational health information including multiple health parameters of the valve assembly, including a stroke setpoint, the valve position, the actuator pressure, and a drive signal; A parameter calculator that calculates an operational value for one of the plurality of health parameters of the valve assembly; as well as A difference calculator calculates the difference between the operating value of the health parameter of the valve assembly and the baseline value of the health parameter of the valve assembly.

2. The apparatus of claim 1 further includes an alarm generator, which identifies the condition of the valve and generates an alarm when the difference meets a threshold, the alarm including the condition, the condition including a degradation of the valve.

3. The apparatus according to claim 1, wherein, The collection engine also obtains baseline health information from the valve, and the parameter calculator calculates the baseline value of the health parameter.

4. The apparatus of claim 1 further includes a trend analyzer that updates the trend state and trend value based on the difference.

5. A method for monitoring the health status of a valve assembly, the valve assembly including an actuator and a valve, the method comprising: In response to the valve assembly initiating full-stroke valve operation, operational health information is obtained from the valve assembly, the full-stroke valve operation including movement of the valve between a fully open position and a fully closed position, the operational health information including multiple health parameters of the valve assembly, including a stroke setpoint, the position of the valve, the pressure of the actuator, and the drive signal; Calculate the operational value of one of the plurality of health parameters of the valve assembly; as well as Calculate the difference between the operating value of the health parameter of the valve assembly and the baseline value of the health parameter of the valve assembly.

6. The method according to claim 5, further comprising: When the difference meets a threshold, the condition of the valve is identified and an alarm is generated, the alarm including the condition, which includes the degradation of the valve.

7. The method according to claim 5, further comprising: Baseline health information is obtained from the valve, and the baseline value of the health parameter is calculated.

8. The method according to claim 5, further comprising: The trend state and trend value are updated based on the difference.

9. The method according to claim 8, wherein, The trend status includes a deterioration status, a failure status, or a warning status.

10. A tangible computer-readable storage medium comprising instructions that, when executed, cause a machine to perform at least the following operations: In response to the valve assembly initiating full-stroke valve operation, operational health information is obtained from the valve assembly, the full-stroke valve operation including movement of the valve between a fully open position and a fully closed position, the operational health information including multiple health parameters of the valve assembly, including a stroke setpoint, the position of the valve, the pressure of the actuator, and the drive signal; Calculate the operational value of one of the plurality of health parameters of the valve assembly; as well as Calculate the difference between the operating value of the health parameter of the valve assembly and the baseline value of the health parameter of the valve assembly.

11. The tangible computer-readable storage medium of claim 10, further comprising, when executed, instructions that cause the machine to perform at least the following operations: when the difference satisfies a threshold, identify the condition of the valve and generate an alarm, the alarm including the condition, the condition including a degrade of the valve.

12. The tangible computer-readable storage medium of claim 10, further comprising, when executed, instructions that cause the machine to perform at least the following operations: obtain baseline health information from the valve and calculate the baseline value of the health parameter.

13. The tangible computer-readable storage medium of claim 10, further comprising, when executed, instructions that cause the machine to perform at least the following operations: updating the trend state and trend value based on the difference.

14. The tangible computer-readable storage medium according to claim 13, wherein, The trend status includes a deterioration status, a failure status, or a warning status.

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