Computer-assisted pressure transmitter fault diagnosis method and system

Abstract

The invention relates to the field of equipment maintenance, and discloses a computer-aided pressure transmitter fault diagnosis method and system, and the method comprises the steps: firstly obtaining real-time sensing data of a pressure transmitter, analyzing the dynamic parameter characteristics of the pressure transmitter, constructing a multi-dimensional parameter matrix, extracting the trend characteristics, carrying out the initial positioning of a fault through the combination of historical records, and carrying out the fault diagnosis; identifying a core fault type and calculating a fault strength index, then delimiting a fault high-risk area, calibrating and detecting an output signal, determining a key maintenance area, querying aging data of parts in the area, analyzing a key failure mode, extracting a maintenance monitoring point to calculate a health state index, and further generating an evaluation index set; and identifying a multi-factor coupling weight, and finally generating a full-life-cycle fault monitoring report. According to the invention, the accuracy of fault diagnosis of the pressure transmitter can be improved.

Description

Technical Field

[0001] The invention relates to a computer-aided pressure transmitter fault diagnosis method and system, belonging to the field of equipment maintenance. Background Art

[0002] In the modern industrial production system, pressure transmitters, as key sensing equipment, are widely used in many fields such as petrochemical, electric power and energy, metallurgical manufacturing, etc. They bear the important task of accurately measuring and transmitting pressure signals. They are one of the core links to ensure the stable operation of industrial processes and ensure product quality and production safety.

[0003] However, the current traditional pressure transmitter fault diagnosis methods have obvious limitations. On the one hand, manual inspections rely on the experience and skill level of the operation and maintenance personnel, which is not only inefficient, but also highly subjective, making it difficult to fully and accurately detect early potential faults; on the other hand, the conventional automatic diagnosis method based on simple threshold judgment cannot adapt to the complex and changeable industrial production environment, and has limited accuracy in identifying the type and degree of pressure transmitter faults. When encountering complex situations such as sensor aging, circuit parameter drift, and complex working conditions, traditional diagnostic methods often find it difficult to accurately judge faults in a timely manner, resulting in extended troubleshooting and repair time. Therefore, a computer-aided pressure transmitter fault diagnosis method is needed to improve the accuracy of pressure transmitter fault diagnosis. Summary of the invention

[0004] The present invention provides a computer-aided pressure transmitter fault diagnosis method and system, the main purpose of which is to improve the accuracy of pressure transmitter fault diagnosis.

[0005] To achieve the above object, the present invention provides a computer-aided pressure transmitter fault diagnosis method, comprising: Acquire real-time sensor data of the pressure transmitter during operation, analyze dynamic parameter features corresponding to the real-time sensor data, construct a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter features, and extract trend-related features in the multi-dimensional parameter matrix; Based on the trend-related features and the historical record data corresponding to the pressure transmitter, the pressure transmitter is initially fault-located to obtain a fault location source point, the core fault type corresponding to the fault location source point is identified, and based on the core fault type, the fault intensity index corresponding to the fault location source point is calculated; Based on the fault intensity index, a high-risk fault area corresponding to the internal circuit and the sensing unit of the pressure transmitter is delineated, a signal zero point calibration is performed on the output signal in the high-risk area to obtain a calibrated output signal, a real-time performance detection is performed on the calibrated output signal to obtain a response performance curve, and based on the response performance curve, a key maintenance area corresponding to the pressure transmitter is determined; Querying the component aging data in the key maintenance area, analyzing the key failure mode corresponding to the key maintenance area according to the component aging data, extracting the maintenance monitoring points in the key failure mode, and calculating the health status index corresponding to the key maintenance area based on the maintenance monitoring points; Based on the health status index, an evaluation index set corresponding to the pressure transmitter is generated, the indicator degradation trend corresponding to the evaluation index set is queried, and the multi-factor coupling weight corresponding to the indicator degradation trend is identified; based on the multi-factor coupling weight, a fault monitoring report corresponding to the entire life cycle of the pressure transmitter is generated.

[0006] Optionally, constructing a multi-dimensional parameter matrix of the pressure transmitter in a running state based on the dynamic parameter characteristics includes: Extracting core variables corresponding to the dynamic parameter features; Based on the core variables, generating a time sequence of working conditions corresponding to the pressure transmitter; Perform sliding window segmentation on the working condition time series to obtain window data blocks; Performing cross-domain fusion on the data in the window data block to obtain a fused data block; identifying corresponding multi-dimensional attributes of the fused data block; Based on the multi-dimensional attributes, a multi-dimensional parameter matrix of the pressure transmitter in the operating state is constructed.

[0007] Optionally, the performing preliminary fault location on the pressure transmitter based on the trend-related features in combination with the historical record data corresponding to the pressure transmitter to obtain the fault location source point includes: Determine a fault type mode corresponding to the pressure transmitter based on the trend-related features combined with historical record data corresponding to the pressure transmitter; Extracting a fault mode matching point in the fault type mode; Based on the fault mode matching point, query the historical high-incidence component corresponding to the pressure transmitter; Performing correlation analysis on the historically high-incidence components to obtain high-incidence correlated components; Perform preliminary fault location on the frequently associated components to obtain the fault location source point.

[0008] Optionally, the calculating, based on the core fault type, a fault intensity index corresponding to the fault location source point includes: The fault intensity index corresponding to the fault location source point is calculated using the following formula: ; in, represents the fault intensity index corresponding to the fault location source point, represents the number of fault factors associated with the core fault type, Indicates the quantity index corresponding to the fault factor, Indicates The factor weight corresponding to each fault factor is: represents the actual measured value corresponding to the i-th fault factor, Indicates The normal reference value corresponding to each fault factor is: Indicates The maximum fluctuation allowable value corresponding to each fault factor is: Indicates the monitoring time period, Indicates The failure factor at time The fault impact function.

[0009] Optionally, the step of delineating a high-risk fault area corresponding to an internal circuit and a sensing unit of the pressure transmitter based on the fault intensity index includes: Analyzing the index component weights corresponding to the fault intensity index; Based on the index component weights, calculating contribution intensity values ​​corresponding to the internal circuit and the sensing unit of the pressure transmitter; Based on the contribution strength value, the internal circuit and the sensor unit are sorted to obtain a sorting result; Marking components in the ranking results that exceed a preset threshold as high-risk candidates; Based on the high-risk candidate objects, a high-risk failure area corresponding to the pressure transmitter is defined.

[0010] Optionally, determining a key maintenance area corresponding to the pressure transmitter based on the response performance curve includes: Identifying an abnormal fluctuation segment in the response performance curve; Mark the key timestamp corresponding to the abnormal fluctuation segment; Based on the key timestamp, trace back the operation log corresponding to the pressure transmitter; Query the abnormal event records in the operation log; Based on the abnormal event record, locate the fault trigger module corresponding to the pressure transmitter; Based on the fault trigger module, determine the key maintenance area corresponding to the pressure transmitter

[0011] Optionally, the querying of the component aging data in the key maintenance area includes: Identify the unique identification code corresponding to the components in the key maintenance area; Based on the unique identification code, retrieve the basic information file corresponding to the component in the key maintenance area; Extracting past maintenance records from the basic information archive; Based on the past maintenance records, the component aging data in the key maintenance area is queried.

[0012] Optionally, the calculating, based on the maintenance monitoring point, a health status index corresponding to the key maintenance area includes: The health status index corresponding to the key maintenance area is calculated using the following formula: ; in, Indicates the health status index corresponding to the key maintenance area, Indicates the number of maintenance monitoring points. represents the quantity index corresponding to the maintenance monitoring point, Indicates The state coefficient corresponding to each maintenance monitoring point is: Indicates The maintenance weight corresponding to each maintenance monitoring point is: Indicates the number of key components in the key maintenance area. Indicates the quantity index corresponding to the key components, Indicates The performance degradation indicators corresponding to the key components are: represents the number of external environmental factors in the key maintenance area, Represents the quantitative index of external environmental factors, Indicates The corresponding influence coefficient of each external environmental factor is Indicates the total maintenance time. It represents the influence function of the temperature changing with time on the status of the key maintenance area.

[0013] Optionally, generating an evaluation index set corresponding to the pressure transmitter based on the health status index includes: Dividing the state operation interval corresponding to the health state index; Extracting historical fault features in the state operation interval; Constructing a state mapping table corresponding to the historical fault characteristics; Performing hierarchical allocation on the state mapping table to obtain a state hierarchical index; Based on the state hierarchy index, an evaluation index set corresponding to the pressure transmitter is generated.

[0014] In order to solve the above problems, the present invention also provides a computer-aided pressure transmitter fault diagnosis system, the system comprising: A feature extraction module is used to obtain real-time sensor data of the pressure transmitter during operation, analyze dynamic parameter features corresponding to the real-time sensor data, construct a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter features, and extract trend-related features in the multi-dimensional parameter matrix; An index calculation module, used to perform preliminary fault location on the pressure transmitter based on the trend-related features combined with the historical record data corresponding to the pressure transmitter, obtain a fault location source point, identify a core fault type corresponding to the fault location source point, and calculate a fault intensity index corresponding to the fault location source point based on the core fault type; an area determination module, for delineating a high-risk area of ​​faults corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the fault intensity index, performing a signal zero point calibration on the output signal in the high-risk area to obtain a calibrated output signal, performing a real-time performance detection on the calibrated output signal to obtain a response performance curve, and determining a key maintenance area corresponding to the pressure transmitter based on the response performance curve; A state index module, used to query the component aging data in the key maintenance area, analyze the key failure mode corresponding to the key maintenance area according to the component aging data, extract the maintenance monitoring points in the key failure mode, and calculate the health state index corresponding to the key maintenance area based on the maintenance monitoring points; A report generation module is used to generate an evaluation index set corresponding to the pressure transmitter based on the health status index, query the indicator degradation trend corresponding to the evaluation index set, and identify the multi-factor coupling weight corresponding to the indicator degradation trend, and generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter based on the multi-factor coupling weight.

[0015] Compared with the problems described in the background technology, the present invention obtains real-time sensor data of the pressure transmitter during operation and analyzes the dynamic parameter characteristics corresponding to the real-time sensor data, so as to timely detect subtle changes in the operating status of the equipment and provide a key basis for early warning of faults. Secondly, accurate analysis of dynamic parameter characteristics helps to gain a deeper understanding of the working characteristics of the equipment and thus optimize the operating parameters. The present invention performs an initial fault location on the pressure transmitter based on the trend-related characteristics combined with the historical record data corresponding to the pressure transmitter to obtain the fault location source point, and can integrate current and past equipment operation information to accurately lock in the source where the fault is likely to occur, thereby improving the efficiency of troubleshooting. Furthermore, the present invention defines the high-risk fault areas corresponding to the internal circuit and sensor unit of the pressure transmitter based on the fault intensity index, and can accurately locate the problem area inside the pressure transmitter, which helps to quickly Quickly focus on inspection points, improve maintenance efficiency, avoid the blindness of comprehensive inspection, and at the same time, preventive maintenance can be carried out in advance for high-risk areas to reduce the probability of sudden failures and ensure the stable and reliable operation of the pressure transmitter. Furthermore, the present invention can assist in judging the remaining service life of components by querying the aging data of components in the key maintenance area, plan replacement in advance, and prevent failures caused by sudden aging and damage of components. At the same time, the aging law of components can be analyzed based on the aging data, which provides strong support for optimizing equipment maintenance strategies and improving overall operational reliability. Finally, based on the health status index, the present invention generates an evaluation index set corresponding to the pressure transmitter, which can comprehensively and quantitatively evaluate its operating status, and can quickly locate potential problems, provide a scientific basis for maintenance decisions, reasonably allocate maintenance resources, improve maintenance efficiency, reduce the risk of failures, and ensure the stable and reliable operation of the pressure transmitter. Therefore, the computer-aided pressure transmitter fault diagnosis method and system provided in the embodiment of the present invention can improve the accuracy of pressure transmitter fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a computer-aided pressure transmitter fault diagnosis method provided by an embodiment of the present invention; Figure 2 A schematic diagram of modules for implementing the computer-aided pressure transmitter fault diagnosis system provided in one embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides a computer-aided pressure transmitter fault diagnosis method. The execution subject of the computer-aided pressure transmitter fault diagnosis method includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the computer-aided pressure transmitter fault diagnosis method can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0020] Embodiment 1: Reference Figure 1 FIG. 1 is a flow chart of a computer-aided pressure transmitter fault diagnosis method according to an embodiment of the present invention. In this embodiment, the computer-aided pressure transmitter fault diagnosis method includes: S1. Acquire real-time sensor data of the pressure transmitter during operation, analyze dynamic parameter characteristics corresponding to the real-time sensor data, construct a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter characteristics, and extract trend-related characteristics in the multi-dimensional parameter matrix.

[0021] The present invention obtains real-time sensor data of the pressure transmitter during operation and analyzes the dynamic parameter characteristics corresponding to the real-time sensor data, so as to timely detect subtle changes in the operating status of the equipment and provide a key basis for early warning of faults. Secondly, accurate analysis of dynamic parameter characteristics helps to gain a deeper understanding of the working characteristics of the equipment and thus optimize the operating parameters.

[0022] Among them, the pressure transmitter refers to a device widely used in industrial production, for example, in petrochemical pipelines, it can convert the fluid pressure in the pipeline into a standard electrical signal, providing key data for production control; the real-time sensor data refers to a device widely used in industrial production, for example, in petrochemical pipelines, it can convert the fluid pressure in the pipeline into a standard electrical signal, providing key data for production control; the dynamic parameter characteristics can refer to the rate of change of the pressure value over a period of time, such as a pressure increase of 0.1MPa per minute, or the frequency of pressure fluctuations, such as 10 fluctuations per hour, etc., which can show the dynamic changes in the operating status of the equipment. Optionally, the acquisition of real-time sensor data during the operation of the pressure transmitter can be achieved through an adaptive sampling algorithm, such as: dynamically adjusting the sampling interval according to the frequency of change of the output signal of the pressure transmitter; when the signal changes drastically, shortening the sampling interval to obtain more accurate data; when the signal is relatively stable, increasing the sampling interval to reduce data transmission and storage pressure, thereby efficiently acquiring real-time sensor data; the analysis of the dynamic parameter characteristics corresponding to the real-time sensor data can be achieved through parameter analysis tools, such as: MATLAB, Python and other tools.

[0023] Furthermore, based on the dynamic parameter characteristics, the present invention constructs a multi-dimensional parameter matrix of the pressure transmitter in the operating state, which can integrate complex and scattered equipment operation information to form a comprehensive and structured data expression, providing a more efficient data processing basis for subsequent fault diagnosis and analysis, and greatly improving the accuracy and efficiency of fault location and judgment.

[0024] The multi-dimensional parameter matrix refers to a matrix constructed based on the multi-dimensional attributes of the fused data block, and each row of the matrix represents a fused data block, and each column corresponds to a multi-dimensional attribute of the fused data block.

[0025] As an embodiment of the present invention, based on the dynamic parameter characteristics, constructing a multidimensional parameter matrix of the pressure transmitter in the operating state, includes: extracting core variables corresponding to the dynamic parameter characteristics; generating a working condition time series sequence corresponding to the pressure transmitter based on the core variables; performing sliding window segmentation on the working condition time series sequence to obtain a window data block; performing cross-domain fusion on the data in the window data block to obtain a fused data block; identifying the corresponding multidimensional attributes of the fused data block; and constructing a multidimensional parameter matrix of the pressure transmitter in the operating state based on the multidimensional attributes.

[0026] Among them, the core variables refer to the variables extracted from the dynamic parameter features, which are the most critical and representative variables for characterizing the operating status of the pressure transmitter. These variables can highlight the core status information of the pressure transmitter when it is working, such as the rate of change of pressure, the extreme value of the fluctuation amplitude of the signal, etc.; the operating condition time series sequence refers to a series of data points generated based on the core variables and arranged in chronological order, which reflects the operating conditions of the pressure transmitter at different times. Each data point corresponds to the value of the core variable at a specific time, and the continuous change of the operating status of the pressure transmitter over time can be clearly observed; the window data block refers to the result obtained after the operating condition time series sequence is segmented by a sliding window. The sliding window is a time window with a fixed size, which slides sequentially on the operating condition time series sequence. Each time the data in the window is intercepted as a window data block, and each window data block contains relevant data of the operation of the pressure transmitter in a specific time period; the fused data block refers to the data set obtained after cross-domain fusion of the data in the window data block. Cross-domain fusion refers to the organic integration of data from different fields (such as pressure measurement domain, temperature influence domain, etc.) in the window data block, and comprehensively considers the impact of multiple factors on the operating status of the pressure transmitter; the multi-dimensional attributes refer to the characteristic attributes of the fused data block in multiple different aspects. These attributes describe the characteristics of the fused data block from multiple angles, including but not limited to the statistical attributes of the data (such as mean, variance, kurtosis, etc.), frequency attributes (frequency components and distribution of signals), correlation attributes (the degree of correlation between different variables), etc.

[0027] Furthermore, the extraction of the core variables corresponding to the dynamic parameter characteristics can be achieved through the principal component analysis method, such as: projecting the high-dimensional dynamic parameter characteristics into the low-dimensional space, and selecting the original variables corresponding to the principal components with high contribution rates as the core variables; the generation of the operating condition time series sequence corresponding to the pressure transmitter can be achieved through an interpolation algorithm, such as: for the missing data points that may exist in the time series, a linear interpolation or spline interpolation algorithm is used to supplement them, so as to obtain the operating condition time series sequence; the sliding window segmentation of the operating condition time series sequence can be achieved through fixed window sliding, such as: setting a time window of a fixed size, sliding point by point on the operating condition time series sequence, and intercepting the data in the window each time, so as to obtain a window data block; the window data block Cross-domain data fusion can be achieved through a weighted fusion algorithm, such as: assigning different weights to the data in each field according to the degree of influence of data in different fields on the operating state of the pressure transmitter, and then performing weighted summation fusion to obtain a fused data block; the identification of the corresponding multi-dimensional attributes of the fused data block can be achieved through Fourier transform, such as: converting the fused data block from the time domain to the frequency domain, analyzing its frequency components and distribution as the frequency attributes in the multi-dimensional attributes; the construction of the multi-dimensional parameter matrix of the pressure transmitter in the operating state can be achieved through a matrix filling method, such as: filling the multi-dimensional attributes of the fused data block into the matrix in the form of rows (representing different fused data blocks) and columns (representing different attributes) to obtain a multi-dimensional parameter matrix).

[0028] By extracting trend-related features in the multi-dimensional parameter matrix, the present invention can easily capture the long-term increase and decrease trends and periodic fluctuations of key indicators such as pressure and signal, thereby predicting potential failure risks in advance, providing key clues for preventive maintenance, and helping industrial production to operate more stably and efficiently.

[0029] Among them, the trend-related features refer to a set of key information extracted from a multi-dimensional parameter matrix that can reflect the trend of the operating status of the pressure transmitter over time. It covers the monotonicity characteristics of the parameters, that is, whether the parameters continue to rise, fall or remain stable, which can be used to judge the development trend of equipment performance; it also includes periodic characteristics, such as whether there are regular periodic fluctuations in the pressure signal, which helps to discover potential problems caused by the equipment operation cycle. Optionally, the extraction of trend-related features in the multi-dimensional parameter matrix can be achieved through the moving average method, such as: smoothing the data by calculating the moving average of the data, eliminating short-term fluctuations, and highlighting long-term trends, thereby obtaining trend-related features.

[0030] S2. Based on the trend-related features and the historical record data corresponding to the pressure transmitter, the fault of the pressure transmitter is initially located to obtain the fault location source point, the core fault type corresponding to the fault location source point is identified, and based on the core fault type, the fault intensity index corresponding to the fault location source point is calculated.

[0031] The present invention performs an initial fault location on the pressure transmitter based on the trend-related features combined with the historical record data corresponding to the pressure transmitter to obtain the fault location source point. It can integrate current and past equipment operation information to accurately lock the source where the fault is likely to occur, thereby improving the efficiency of troubleshooting.

[0032] Among them, the historical record data refers to various data sets accumulated by the pressure transmitter during its past operation, which covers the operating parameters of the equipment at different time points, such as pressure measurement values, temperature, output signal strength, etc., and also includes equipment maintenance records, such as maintenance time, maintenance content, replacement parts information, etc., as well as previous fault records, such as fault occurrence time, fault phenomenon description, fault type and final solution; the fault location source point refers to the starting position or component where the fault occurs in the pressure transmitter, which is finally determined by initial fault location of high-incidence related components. It is the key result of fault diagnosis and clarifies the root cause of the fault. Once the fault location source point is determined, the component or position can be targeted for detailed inspection, repair or replacement, thereby solving the fault problem of the pressure transmitter and restoring normal operation of the equipment.

[0033] As an embodiment of the present invention, the method of performing initial fault location on the pressure transmitter based on the trend-related features in combination with the historical record data corresponding to the pressure transmitter to obtain the fault location source point includes: Based on the trend-related features and the historical record data corresponding to the pressure transmitter, determine the fault type pattern corresponding to the pressure transmitter; extract the fault pattern matching points in the fault type pattern; based on the fault pattern matching points, query the historical high-incidence components corresponding to the pressure transmitter; perform correlation analysis on the historical high-incidence components to obtain high-incidence associated components; perform initial fault location on the high-incidence associated components to obtain the fault location source point.

[0034] Among them, the fault type pattern refers to a set of fault manifestations with typical characteristics summarized based on trend-related features and historical record data of pressure transmitters, which comprehensively reflects how the operating parameters, signal characteristics, etc. of the pressure transmitter change with time when different faults occur. It is an abstract summary of various faults. For example, continuous abnormal fluctuations in pressure can correspond to sensor fault modes, and signal drift corresponds to circuit parameter drift fault modes, etc.; the fault mode matching point refers to a set of fault manifestations with typical characteristics summarized based on trend-related features and historical record data of pressure transmitters, which comprehensively reflects how the operating parameters, signal characteristics, etc. of the pressure transmitter change with time when different faults occur. It is an abstract summary of various faults. For example, continuous abnormal fluctuations in pressure may correspond to sensor fault modes, and signal drift corresponds to circuit parameter drift fault modes, etc. The sensor failure mode, signal drift corresponding circuit parameter drift failure mode, etc.; the historical high-incidence components refer to components that frequently fail or have a relatively high failure probability in the historical record data of the pressure transmitter. These components are more prone to problems than other components due to factors such as their own quality, working environment, and frequency of use, such as the pressure sensor diaphragm, certain capacitors and resistors in the signal conditioning circuit, etc.; the high-incidence associated components refer to other components that are closely related to the historical high-incidence components in function, physical connection or signal transmission after correlation analysis of the historical high-incidence components. When the historical high-incidence components fail, the probability of these associated components being affected and failing increases. For example, a capacitor in the signal conditioning circuit is a historical high-incidence component, and the operational amplifier connected to it is a high-incidence associated component.

[0035] Furthermore, the determination of the fault type pattern corresponding to the pressure transmitter can be achieved through a pattern recognition method, such as: taking trend-related features and historical record data as input, comparing them with a predefined fault type template, and finding the best matching fault type; the extraction of fault mode matching points in the fault type pattern can be achieved through a threshold setting method, such as: setting a threshold for key features in the fault type pattern based on historical data and experience, and when the feature value exceeds or falls below the threshold, it is used as a fault mode matching point; the query of the historical high-incidence components corresponding to the pressure transmitter can be achieved through a statistical analysis method, such as: performing a statistical analysis on each component in the historical record data. The number of failures of the components is counted, and they are sorted according to the frequency of failure, and components with higher frequencies are found as historical high-incidence components; the association analysis of the historical high-incidence components can be achieved through a network analysis method, such as: regarding the various components of the pressure transmitter as network nodes, and the connection relationships between the components as edges, and using a network analysis method to find out the components that are closely connected to the historical high-incidence components as high-incidence associated components; the initial fault location of the high-incidence associated components can be achieved through a fault injection method, such as: performing simulated fault injection on the high-incidence associated components, observing the changes in the operating parameters of the pressure transmitter, and finding out the components that cause the fault phenomenon as the fault location source point.

[0036] The present invention helps to quickly understand the key to the fault by identifying the core fault type corresponding to the fault location source point, so as to formulate a targeted repair strategy and avoid blind troubleshooting. At the same time, clarifying the core fault type can also quickly allocate appropriate maintenance resources based on past experience, greatly improving fault resolution efficiency.

[0037] Among them, the core fault type refers to the representative, common and most critical and significant fault category on the operation of the equipment summarized from many common fault manifestations and causes when the pressure transmitter fails. It is usually the fault type corresponding to the main and fundamental cause of whether the pressure transmitter works normally, such as pressure sensor damage, signal transmission line interruption, circuit control module failure, etc. Optionally, the identification of the core fault type corresponding to the fault location source point can be achieved through a decision tree algorithm, such as: taking various characteristics of the fault location source point (pressure, temperature, output signal, etc.) as input, constructing a decision tree model, and gradually determining the core fault type through the judgment of these characteristics by the decision tree model.

[0038] Furthermore, based on the core fault type, the present invention calculates the fault intensity index corresponding to the fault location source point, which can intuitively reflect the impact of the fault location source point on the pressure transmitter, help to quickly determine the fault priority, reasonably arrange maintenance resources, give priority to handling serious faults, and efficiently ensure stable operation of the equipment.

[0039] The fault intensity index refers to a comprehensive quantitative indicator used to measure the fault severity corresponding to the fault location source point based on the core fault type.

[0040] As an embodiment of the present invention, the calculating, based on the core fault type, the fault intensity index corresponding to the fault location source point includes: The fault intensity index corresponding to the fault location source point is calculated using the following formula: ; in, represents the fault intensity index corresponding to the fault location source point, represents the number of fault factors associated with the core fault type, Indicates the quantity index corresponding to the fault factor, Indicates The factor weight corresponding to each fault factor is: represents the actual measured value corresponding to the i-th fault factor, Indicates The normal reference value corresponding to each fault factor is: Indicates The maximum fluctuation allowable value corresponding to each fault factor is: Indicates the monitoring time period, Indicates The failure factor at time The fault impact function.

[0041] In detail, the factor weight refers to the relative importance of the i-th fault factor in calculating the fault intensity index. For example, for a pressure transmitter, if the performance degradation of the sensor has a great impact on its normal operation, then the weight of the sensor performance fault factor will be relatively high; the actual measurement value refers to the real-time value obtained by various monitoring means (such as sensor measurement, data acquisition, etc.) for the i-th fault factor during the operation of the equipment. For example, if the fault factor is the operating temperature of the equipment, then the actual measurement value is the current temperature value of the equipment measured in real time by the temperature sensor; the normal reference value refers to the value of the i-th fault factor when the equipment is in normal and fault-free operation. The standard value or value range under the state; the maximum allowable fluctuation value refers to the maximum deviation range allowed by the actual measurement value of the ith fault factor relative to the normal reference value when the equipment is operating normally, which is a threshold determined based on factors such as the design performance and working environment of the equipment; the monitoring time period refers to the length of time for continuous monitoring of the equipment fault factor, and this period can be set based on factors such as the characteristics of the equipment, the frequency of use, and the probability of failure; the fault influence function refers to a function that describes the degree of influence of the ith fault factor on the operation of the equipment or system at time t, which reflects the influence of the fault factor on the performance, function, etc. of the equipment over time.

[0042] S3. Based on the fault intensity index, define the high-risk fault area corresponding to the internal circuit and the sensor unit of the pressure transmitter, perform signal zero point calibration on the output signal in the high-risk area to obtain a calibrated output signal, perform real-time performance detection on the calibrated output signal to obtain a response performance curve, and determine the key maintenance area corresponding to the pressure transmitter based on the response performance curve.

[0043] Based on the fault intensity index, the present invention delineates the high-risk fault areas corresponding to the internal circuit and the sensor unit of the pressure transmitter, and can accurately locate the problem areas inside the pressure transmitter, which helps to quickly focus on inspection points, improve maintenance efficiency, and avoid the blindness of comprehensive inspections. At the same time, preventive maintenance can be performed on high-risk areas in advance to reduce the probability of sudden failures and ensure stable and reliable operation of the pressure transmitter.

[0044] The internal circuit refers to a system composed of various electronic components (such as resistors, capacitors, transistors, integrated circuits, etc.) and lines, which is responsible for processing the signal from the sensor unit. It can amplify, filter, convert and other operations on the weak original signal, and turn it into a standard signal (such as 4-20mA current signal, 0-5V voltage signal, etc.) that can be recognized and processed by subsequent equipment. It also undertakes power management, self-diagnosis and other functions. It is a key part of the pressure transmitter to achieve signal transmission and control; the sensor unit refers to one of the core components of the pressure transmitter. Its main function is to sense pressure changes. It is usually composed of a pressure sensitive element ( The pressure transmitter is composed of a plurality of pressure sensors (such as piezoresistive, capacitive, piezoelectric and other sensitive elements), which can convert the external pressure physical quantity into an electrical signal. For example, when a piezoresistive sensitive element is subjected to pressure, its resistance value will change, thereby generating an electrical signal output that is related to the pressure, and providing raw data for subsequent signal processing and transmission; the high-risk fault area refers to a range composed of high-risk candidate objects and their surrounding areas that have close electrical connections and signal transmission relationships with them. The purpose of demarcating this area is to concentrate on inspecting and processing these areas that are most prone to faults when troubleshooting and maintaining the pressure transmitter.

[0045] As an embodiment of the present invention, the method of delineating a high-risk fault area corresponding to the internal circuit and the sensor unit of the pressure transmitter based on the fault intensity index includes: analyzing the index composition weight corresponding to the fault intensity index; calculating the contribution intensity value corresponding to the internal circuit and the sensor unit of the pressure transmitter based on the index composition weight; sorting the internal circuit and the sensor unit based on the contribution intensity value to obtain a sorting result; marking the components in the sorting result that exceed a preset threshold as high-risk candidate objects; and delineating the high-risk fault area corresponding to the pressure transmitter based on the high-risk candidate objects.

[0046] The index composition weight refers to the degree of influence of different factors on the size of the fault intensity index, which is determined by experts based on experience or through data analysis. The value range is usually between 0 and 1, and the sum of all weights is 1. The larger the weight, the more critical the influence of the factor on the fault intensity index. The contribution intensity value refers to the contribution degree of each component to the fault intensity index calculated according to the index composition weight and the actual situation of the fault-related factors corresponding to each component in the internal circuit and sensor unit of the pressure transmitter. The sorting result refers to a list in which the components of the internal circuit and sensor unit of the pressure transmitter are arranged from large to small (or from small to large, according to the specific setting) according to the contribution intensity value. The preset threshold refers to a standard value set in advance, which is used to measure whether the contribution intensity value of the component reaches a high level. When the contribution intensity value of the component exceeds the threshold, it is considered to have a high risk of failure. The high-risk candidate object refers to the components of the internal circuit and sensor unit of the pressure transmitter whose contribution intensity value exceeds the preset threshold in the sorting result. These components are preliminarily identified as having a high probability of failure under the current conditions and are the objects of subsequent focus and inspection.

[0047] Furthermore, the analysis of the index component weights corresponding to the fault intensity index can be implemented through a hierarchical model, such as: taking the factors affecting the fault intensity as the criterion layer, such as temperature, humidity, pressure, etc., constructing a judgment matrix through expert scoring, etc., and calculating the weight of each factor; the calculation of the contribution intensity value corresponding to the internal circuit and the sensor unit of the pressure transmitter can be implemented through a weighted average method, such as: calculating the contribution intensity value according to the weight of each factor and the value of each component under the corresponding factor; the sorting of the internal circuit and the sensor unit can be implemented through a bubble sort algorithm, such as: repeatedly visiting the element column to be sorted, comparing two adjacent elements in turn, and if the order (such as from large to small, first letters from A to Z) are swapped, and finally a sorting result is obtained; the marking of components exceeding a preset threshold in the sorting result as high-risk candidates can be achieved through a threshold comparison method, such as: assuming that the preset threshold is 75, a loop statement can be used in MATLAB to traverse the sorted contribution intensity value list, and when a value greater than 75 is encountered, the corresponding component is marked as a high-risk candidate; the delineation of the high-risk fault area corresponding to the pressure transmitter can be achieved through a connectivity graph algorithm, such as: regarding the internal circuit and sensor unit of the pressure transmitter as a graph structure, the components as nodes, and the electrical connections and other relationships between the components as edges, and delineating the area by searching for nodes connected to the high-risk candidate.

[0048] The present invention obtains a calibrated output signal by performing signal zero point calibration on the output signal in the high-risk area. The calibrated output signal is more accurate, which can ensure that the pressure data fed back by the pressure transmitter is true and reliable, and provide an accurate basis for subsequent industrial control and monitoring systems based on the data, thereby avoiding erroneous decisions and abnormal operation of equipment due to signal deviation.

[0049] Among them, the calibrated output signal refers to a more accurate and reliable signal obtained after the zero-point calibration of the output signal in the high-risk area, which corrects the possible zero-point offset of the original signal, so that the signal value can accurately reflect the actual pressure measured by the pressure transmitter, and provide a solid foundation for the precise control of the subsequent system and accurate data analysis, and ensure the stable operation of the pressure transmitter and related systems. Optionally, the signal zero-point calibration of the output signal in the high-risk area can be achieved through signal calibration tools, such as: pressure calibrator, multimeter and other tools.

[0050] Furthermore, the present invention obtains a response performance curve by performing real-time performance detection on the calibration output signal, which can quickly provide insights into key performance indicators such as signal response speed and stability, facilitate timely discovery of potential anomalies, and provide a strong basis for continuously optimizing the performance of the pressure transmitter and ensuring the accuracy and reliability of measurement data, thereby improving the overall operating efficiency of the equipment.

[0051] Among them, the response performance curve refers to a curve drawn with time as the horizontal axis and the key performance parameters of the calibration output signal (such as amplitude, frequency, phase, etc.) as the vertical axis. For example, when the pressure changes, the response performance curve can clearly show how the output signal tracks the pressure change over time, including the rate at which the signal rises or falls, the time required to reach a stable state, and whether there are fluctuations in the process. Optionally, the real-time performance detection of the calibration output signal can be achieved through time domain analysis. For example, when a step input is given to the pressure transmitter, the time required for the calibration output signal to rise from the initial value to a certain proportion (such as 90%) of the final value is recorded, which is the rise time. The response characteristics of the signal in the time domain are depicted through multiple such time parameters, and then the response performance curve is constructed.

[0052] Furthermore, the present invention determines the key maintenance area corresponding to the pressure transmitter based on the response performance curve. The curve can accurately locate the performance abnormality area, avoid blind comprehensive maintenance, save manpower and time costs, and intervene in key areas prone to problems in advance, reduce the probability of failure, extend the service life of the equipment, and ensure its stable operation.

[0053] Among them, the key maintenance area refers to the parts closely related to the fault trigger module and its periphery, which not only includes the module itself where the fault directly occurs, but also covers other components or areas that are closely related to the module in terms of electrical connection, signal transmission, physical structure, etc.

[0054] As an embodiment of the present invention, determining the key maintenance area corresponding to the pressure transmitter based on the response performance curve includes: identifying the abnormal fluctuation segment in the response performance curve; marking the key timestamp corresponding to the abnormal fluctuation segment; based on the key timestamp, backtracking the operation log corresponding to the pressure transmitter; querying the abnormal event record in the operation log; locating the fault trigger module corresponding to the pressure transmitter based on the abnormal event record; and determining the key maintenance area corresponding to the pressure transmitter based on the fault trigger module.

[0055] Among them, the abnormal fluctuation segment refers to the curve portion in the response performance curve that deviates from the normal fluctuation range, shows obvious irregularity or differs greatly from historical data and theoretical expectations. These fluctuations can be manifested as a sudden increase or decrease in signal amplitude, abnormal changes in fluctuation frequency, etc.; the key timestamp refers to the time mark point corresponding to the abnormal fluctuation segment, which accurately records the time when the abnormal fluctuation starts, ends or has representative characteristics (such as the time when the peak and valley values ​​appear) during the fluctuation process; the operation log refers to a series of information sets automatically recorded or manually entered by the pressure transmitter during operation, which records in detail the operating parameters of the equipment at different time points, such as pressure measurement values, output signal strength, working voltage, current, etc., and also includes the equipment start and stop time, as well as the time when the equipment is running. Various events that occurred; the abnormal event record refers to the record content in the operation log specifically for abnormal conditions of the pressure transmitter. These records describe the time when the abnormality occurred (corresponding to the key timestamp), the abnormal phenomenon (such as output signal interruption, the measured pressure value deviates too much from the actual value, etc.), the cause speculation (if any) and the temporary measures taken; the fault trigger module refers to the specific functional module or component that causes abnormal fluctuations in the pressure transmitter through in-depth analysis of the abnormal event records. For example, the pressure sensor module may be aged due to long-term use, resulting in inaccurate measurement and thus abnormal response performance curve; the signal processing circuit module may be subject to electromagnetic interference, causing abnormal fluctuations in the output signal. These modules that cause faults are fault trigger modules.

[0056] Furthermore, the identification of the abnormal fluctuation segment in the response performance curve can be achieved through a threshold-based detection method, such as: setting a threshold of a normal fluctuation range, and when the curve data exceeds this threshold range, it is considered to be an abnormal fluctuation segment; the marking of the key timestamp corresponding to the abnormal fluctuation segment can be achieved through a traversal method, such as: starting from the starting point of the identified abnormal fluctuation segment, traversing in sequence according to the data order, and recording the time corresponding to each abnormal point as the key timestamp; the backtracking of the operation log corresponding to the pressure transmitter can be achieved through a binary search algorithm, such as: for the ordered stored operation log, the binary search algorithm can be used to quickly locate the approximate location of the key timestamp, and then obtain the complete operation log record; the query of the abnormal event record in the operation log can be achieved through a normal The regular expression matching algorithm is implemented, such as: using regular expressions to define the pattern of abnormal events, matching in the operation log text, and finding abnormal event records that meet the pattern; the positioning of the fault trigger module corresponding to the pressure transmitter can be achieved through the fault tree analysis method, such as: according to the abnormal event records and the working principle of the pressure transmitter, a fault tree is constructed, and analysis is gradually carried out from the top event (abnormal phenomenon) to find the bottom module that causes the fault, that is, the fault trigger module; the determination of the key maintenance area corresponding to the pressure transmitter can be achieved through a graph search algorithm, such as: considering each module of the pressure transmitter as a node of the graph, and the connection relationship between the modules as an edge, starting from the node corresponding to the fault trigger module, a breadth-first search is performed, and the modules and areas corresponding to the nodes within a certain search depth are the key maintenance areas.

[0057] S4. Query the component aging data in the key maintenance area, analyze the key failure mode corresponding to the key maintenance area according to the component aging data, extract the maintenance monitoring points in the key failure mode, and calculate the health status index corresponding to the key maintenance area based on the maintenance monitoring points.

[0058] The present invention can assist in determining the remaining service life of components by querying the component aging data in the key maintenance area, plan replacement in advance, and prevent failures caused by sudden aging and damage of components. At the same time, the aging laws of components can also be analyzed based on the aging data, providing strong support for optimizing equipment maintenance strategies and improving overall operational reliability.

[0059] Among them, the component aging data refers to a series of data indicators that can quantitatively represent the degree of component aging, including the cumulative operating time of the component. The longer the operating time, the higher the degree of aging; changes in key performance parameters, such as wear degree, accuracy degradation value, electrical performance degradation index, etc.; failure frequency. With aging, the number of component failures will increase.

[0060] As an embodiment of the present invention, the querying of the component aging data in the key maintenance area includes: identifying the unique identification code corresponding to the component in the key maintenance area; based on the unique identification code, retrieving the basic information file corresponding to the component in the key maintenance area; extracting the past maintenance records in the basic information file; based on the past maintenance records, querying the component aging data in the key maintenance area.

[0061] Among them, the unique identification code refers to the unique identification code assigned to each component in the key maintenance area. The code is like the "identity card number" of the component. It can be in the form of numbers, letters or a combination of the two, and runs through the entire life cycle of the component from production, installation to use and maintenance; the basic information file refers to a comprehensive information collection about the components in the key maintenance area, covering the detailed technical parameters of the components, such as model, specification, material, rated working conditions, etc. These parameters determine the performance and applicable scenarios of the components; it also includes the manufacturer, production date, procurement information, etc. of the components, which helps to understand the source and supply chain of the components; the past maintenance record refers to the record of various maintenance activities that the components have undergone during use, including the time of each maintenance, and a detailed description of the specific content of the maintenance, such as whether cleaning, calibration, replacement of parts and other operations have been performed; record the reasons for maintenance, such as fault repair, regular maintenance, etc.; it also records the maintenance personnel information and feedback on the operating status of the components after maintenance.

[0062] Furthermore, the identification of the unique identification code corresponding to the component in the key maintenance area can be achieved through an image recognition algorithm, such as: using open source computer vision libraries such as OpenCV, collecting the component QR code image through a camera, the algorithm pre-processing the image (such as grayscale, noise reduction, binarization, etc.), and then performing feature extraction and decoding to identify the unique identification code in the QR code; the retrieval of the basic information file corresponding to the component in the key maintenance area can be achieved through a hash search algorithm, such as: in some NoSQL databases based on a hash table structure, using the corresponding hash search algorithm to quickly obtain its basic information file according to the component code; the extraction of past maintenance records in the basic information file can be achieved through a JSON-based parsing algorithm (if the basic information file is stored in JSON format), such as: parsing JSON data into a Python dictionary, and then extracting past maintenance record information based on the key-value pair relationship of the dictionary; the query of component aging data in the key maintenance area can be achieved through a neural network algorithm, such as: using Python's TensorFlow library to build a neural network, training a large amount of historical data, so that the model can accurately predict the aging degree of the component, and finally obtain the component aging data.

[0063] The present invention analyzes the key failure modes corresponding to the key maintenance areas according to the component aging data, and extracts the maintenance monitoring points in the key failure modes, so as to predict the potential failure risks of equipment in advance. Extracting the maintenance monitoring points in the key failure modes can make the maintenance work more targeted, concentrate resources to focus on monitoring key parts, discover and solve problems in time, and effectively reduce the equipment failure rate.

[0064] Among them, the critical failure mode refers to the most important and influential failure form that causes the equipment or system to lose the specified function in the key maintenance area of ​​the pressure transmitter, which is summarized based on the component aging data. For example, for the sensing unit of the pressure transmitter, the critical failure mode may be the aging and rupture of the sensor diaphragm, resulting in inaccurate pressure measurement; or the solder joints of the internal circuit are aging and desoldering due to long-term thermal expansion and contraction, resulting in signal transmission interruption; the maintenance monitoring point refers to a specific position or parameter determined from the critical failure mode, which is used to monitor the operating status of the equipment in real time and evaluate the health of the equipment. For example, in the critical failure mode of aging and rupture of the sensor diaphragm, the pressure deformation degree of the diaphragm, the micro-circuit of the surrounding circuit, and the degree of deformation of the pressure on the diaphragm may be the cause of the failure. Small current changes can be used as maintenance monitoring points; for the failure mode of solder joint desoldering, the temperature and resistance value at the solder joint can be used as maintenance monitoring points. Optionally, the analysis of the key failure modes corresponding to the key maintenance areas can be achieved through a FMEA system, such as by systematically analyzing the possible failure modes of components and evaluating their impact on the entire key maintenance area and the function of the pressure transmitter, thereby determining the key failure modes; the extraction of maintenance monitoring points in the key failure modes can be achieved through a fault feature extraction method, such as extracting characteristic parameters that can characterize the occurrence and development of failures from historical data of key failure modes, and using the measurement positions or indicators corresponding to these characteristic parameters as maintenance monitoring points.

[0065] The present invention calculates the health status index corresponding to the key maintenance area based on the maintenance monitoring points, which facilitates intuitive grasp of the health of the equipment and rapid location of potential risks. Based on this, maintenance strategies can be planned in advance, resources can be reasonably allocated, failures can be effectively prevented, and stable and efficient operation of the equipment can be guaranteed.

[0066] Among them, the health status index refers to a quantitative numerical indicator used to comprehensively evaluate the overall health status of key maintenance areas. The higher the value, the better the health status of the key maintenance area and the lower the possibility of failure or performance degradation; the lower the value, the poorer the health status and the need for closer attention or maintenance measures.

[0067] As an embodiment of the present invention, the calculating of the health status index corresponding to the key maintenance area based on the maintenance monitoring point includes: The health status index corresponding to the key maintenance area is calculated using the following formula: ; in, Indicates the health status index corresponding to the key maintenance area, Indicates the number of maintenance monitoring points. represents the quantity index corresponding to the maintenance monitoring point, Indicates The state coefficient corresponding to each maintenance monitoring point is: Indicates The maintenance weight corresponding to each maintenance monitoring point is: Indicates the number of key components in the key maintenance area. Indicates the quantity index corresponding to the key components, Indicates The performance degradation indicators corresponding to the key components are: represents the number of external environmental factors in the key maintenance area, Represents the quantitative index of external environmental factors, Indicates The corresponding influence coefficient of each external environmental factor is Indicates the total maintenance time. It represents the influence function of the temperature changing with time on the status of the key maintenance area.

[0068] In detail, the state coefficient refers to a coefficient with a value range of 0-1 for each maintenance monitoring point, which is used to reflect the actual state of the maintenance monitoring point; the maintenance weight refers to a weight value with a value range of 0-1 corresponding to each maintenance monitoring point, which reflects the relative importance of the maintenance monitoring point in evaluating the health status of the key maintenance area; the key components refer to the components that play a key role in the normal operation and performance of the system in the key maintenance area. For example, in mechanical equipment, engines, transmissions, etc. may be regarded as key components; in electronic equipment, core processors, power modules, etc. are key components; the performance degradation index refers to an indicator used to measure the degree of degradation of the performance of the key component relative to the initial state or ideal state, which can be the change values ​​of multiple physical quantities or parameters, such as the amount of wear of mechanical parts, the degree of signal attenuation of electronic parts, the proportion of equipment operating efficiency reduction, etc.; the external environment Factors refer to various factors in the external environment where the key maintenance area is located that may affect the health status of the equipment or system, for example, a high temperature environment may accelerate the aging of electronic components, a high humidity environment may cause metal parts to rust and corrode, and vibration may loosen connecting parts, etc.; the influence coefficient refers to a coefficient with a value range of 0-1 for each external environmental factor, which represents the degree of influence of the kth external environmental factor on the health status of the key maintenance area; the total maintenance time refers to the total length of time for monitoring and maintaining the key maintenance area, which reflects the cumulative impact of various factors on the health status of the key maintenance area over a long period of time; the state influence function refers to a function that changes with time, which specifically describes the impact of temperature factors on the state of the key maintenance area. Temperature is an environmental factor that has a greater impact on the performance of equipment and systems. Different temperature changes will have different degrees of impact on the components in the key maintenance area.

[0069] S5. Based on the health status index, generate an evaluation index set corresponding to the pressure transmitter, query the indicator degradation trend corresponding to the evaluation index set, and identify the multi-factor coupling weight corresponding to the indicator degradation trend, and based on the multi-factor coupling weight, generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter.

[0070] Based on the health status index, the present invention generates an evaluation index set corresponding to the pressure transmitter, which can comprehensively and quantitatively evaluate its operating status and quickly locate potential problems, providing a scientific basis for maintenance decisions, rationally allocating maintenance resources, improving maintenance efficiency, reducing failure risks, and ensuring stable and reliable operation of the pressure transmitter.

[0071] Among them, the evaluation index set refers to a comprehensive set of indicators, which includes a series of indicators for evaluating the performance and status of pressure transmitters generated based on state hierarchy indicators and other related factors. These indicators cover multiple aspects of the pressure transmitter, such as reliability, stability, accuracy, and failure risk.

[0072] As an embodiment of the present invention, the generation of an evaluation index set corresponding to the pressure transmitter based on the health status index includes: dividing the state operation interval corresponding to the health status index; extracting historical fault features in the state operation interval; constructing a state mapping table corresponding to the historical fault features; hierarchically assigning the state mapping table to obtain state hierarchy indicators; and generating an evaluation index set corresponding to the pressure transmitter based on the state hierarchy indicators.

[0073] The state operation interval refers to different intervals divided according to the numerical range of the health status index. These intervals represent different operating status levels of the pressure transmitter, for example, they can be divided into good, normal, warning, fault and other intervals; the historical fault characteristics refer to various characteristics and attributes presented when the pressure transmitter failed in the past, which may include specific parameter values ​​when the fault occurs (such as abnormal pressure values, temperature values, current values, etc.), time rules of fault occurrence, manifestations of faults (such as unstable output signals, large deviations of measured values, etc.), and maintenance monitoring point data changes related to the fault; the state mapping table refers to a table that corresponds and associates state operation intervals with historical fault characteristics. In this table, each state operation interval corresponds to one or more groups of historical fault characteristics. Through this mapping relationship, when it is known that the pressure transmitter is in a certain state operation interval, the possible fault characteristics can be quickly understood; the state hierarchical index refers to an index obtained after hierarchical processing of the state mapping table. It classifies, grades and sorts the contents in the state mapping table according to certain logic and rules. For example, the historical fault characteristics are hierarchically divided according to factors such as the severity of the fault and the probability of occurrence, so as to obtain state indicators of different levels.

[0074] Further, the division of the state operation interval corresponding to the health state index can be achieved by a statistical analysis method, such as: collecting a large amount of health state index data of the pressure transmitter, calculating the mean, standard deviation and other statistics of the data, and dividing the interval according to these statistics; the extraction of the historical fault features in the state operation interval can be achieved by a data retrieval method, such as: in the historical fault database, according to the divided state operation interval, retrieve the relevant data when the fault occurs in the corresponding interval, extract features from it, and obtain the historical fault features; the construction of the state mapping table corresponding to the historical fault features can be achieved by an unsupervised classification algorithm, such as: clustering the historical fault features, and then mapping the clustering results with the state operation interval to obtain the state mapping table; the hierarchical allocation of the state mapping table can be achieved by a hierarchical analysis method, such as: by constructing a judgment matrix, determining the relative importance of each historical fault feature in different state operation intervals, and then performing hierarchical allocation to obtain a state hierarchical index; the generation of the evaluation index set corresponding to the pressure transmitter can be achieved by an index synthesis method, such as: synthesizing the state hierarchical index with other related indicators (such as the accuracy and stability of the pressure transmitter) to generate an evaluation index set.

[0075] By querying the indicator degradation trend corresponding to the evaluation indicator set and identifying the multi-factor coupling weight corresponding to the indicator degradation trend, the present invention can clarify the degree of influence of each influencing factor, help maintenance personnel to accurately focus on key factors, reasonably allocate resources, and formulate more targeted maintenance strategies, so as to effectively ensure the stable operation of the pressure transmitter.

[0076] Among them, the index degradation trend refers to the trend and situation of the performance of various evaluation indicators of the pressure transmitter gradually deteriorating as the time or the number of times of use changes. For example, the measurement accuracy index will gradually decrease with the increase of the use time, which is manifested as the deviation between the measured value and the true value is getting larger and larger; the stability index will fluctuate more and more; the multi-factor coupling weight refers to the relative importance of each factor in the degradation of the index when multiple factors affecting the degradation of the pressure transmitter interact and influence each other. For example, factors such as ambient temperature, humidity, pressure fluctuations and the wear of the equipment itself will affect the pressure transmitter. The performance of the index is affected, and the weight value of each factor in the process of causing the index degradation is calculated by a certain method. This weight value reflects the importance of the factor. Optionally, the query of the index degradation trend corresponding to the evaluation index set can be implemented by the ARIMA algorithm, such as: considering the time series data as a random process, and determining the change trend of the index by fitting and predicting the historical data; the identification of the multi-factor coupling weight corresponding to the index degradation trend can be implemented by the entropy weight method, such as: using the entropy weight method to analyze the historical data of these factors, calculating the entropy value and weight of each factor, and thus determining the multi-factor coupling weight.

[0077] Furthermore, based on the multi-factor coupling weights, the present invention generates a fault monitoring report corresponding to the entire life cycle of the pressure transmitter, which can accurately locate the high-prone links of faults. It intuitively presents the fault risks at different stages according to the degree of influence of each factor, and strengthens monitoring at key nodes, greatly improving the efficiency of fault prevention and ensuring the stable operation of the pressure transmitter.

[0078] Among them, the fault monitoring report refers to a comprehensive and detailed document that focuses on the fault-related situations in the entire life cycle of the pressure transmitter. It integrates key information such as multi-factor coupling weights to sort out the operating status of the equipment at different stages. The report not only covers the fault history records of each component of the pressure transmitter, but also combines the weights of various influencing factors to predict the type, time and location of possible future faults. Through data charts, text analysis and other forms, it intuitively presents the overall health status of the equipment, providing a strong basis for operation and maintenance decisions. Optionally, the generation of the fault monitoring report corresponding to the entire life cycle of the pressure transmitter can be achieved through visualization tools, such as: Tableau, PowerBI and other tools.

[0079] Compared with the problems described in the background technology, the present invention obtains real-time sensor data of the pressure transmitter during operation and analyzes the dynamic parameter characteristics corresponding to the real-time sensor data, so as to timely detect subtle changes in the operating status of the equipment and provide a key basis for early warning of faults. Secondly, accurate analysis of dynamic parameter characteristics helps to gain a deeper understanding of the working characteristics of the equipment and thus optimize the operating parameters. The present invention performs an initial fault location on the pressure transmitter based on the trend-related characteristics combined with the historical record data corresponding to the pressure transmitter to obtain the fault location source point, and can integrate current and past equipment operation information to accurately lock in the source where the fault is likely to occur, thereby improving the efficiency of troubleshooting. Furthermore, the present invention defines the high-risk fault areas corresponding to the internal circuit and sensor unit of the pressure transmitter based on the fault intensity index, and can accurately locate the problem area inside the pressure transmitter, which helps to quickly Quickly focus on inspection points, improve maintenance efficiency, avoid the blindness of comprehensive inspection, and at the same time, preventive maintenance can be carried out in advance for high-risk areas to reduce the probability of sudden failures and ensure the stable and reliable operation of the pressure transmitter. Furthermore, the present invention can assist in judging the remaining service life of components by querying the aging data of components in the key maintenance area, plan replacement in advance, and prevent failures caused by sudden aging and damage of components. At the same time, the aging law of components can be analyzed based on the aging data, which provides strong support for optimizing equipment maintenance strategies and improving overall operational reliability. Finally, based on the health status index, the present invention generates an evaluation index set corresponding to the pressure transmitter, which can comprehensively and quantitatively evaluate its operating status, and can quickly locate potential problems, provide a scientific basis for maintenance decisions, reasonably allocate maintenance resources, improve maintenance efficiency, reduce the risk of failures, and ensure the stable and reliable operation of the pressure transmitter. Therefore, the computer-aided pressure transmitter fault diagnosis method and system provided in the embodiment of the present invention can improve the accuracy of pressure transmitter fault diagnosis.

[0080] Example 2: Figure 2 FIG. 1 is a functional module diagram of a computer-aided pressure transmitter fault diagnosis system according to the present invention.

[0081] The computer-aided pressure transmitter fault diagnosis system 200 of the present invention can be installed in an electronic device. According to the functions to be implemented, the computer-aided pressure transmitter fault diagnosis system can include a feature extraction module 201, an index calculation module 202, a region determination module 203, a state index module 204 and a report generation module 205. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0082] In the embodiment of the present invention, the functions of each module / unit are as follows: The feature extraction module 201 is used to obtain real-time sensor data of the pressure transmitter during operation, analyze dynamic parameter features corresponding to the real-time sensor data, construct a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter features, and extract trend-related features in the multi-dimensional parameter matrix; The index calculation module 202 is used to perform preliminary fault location on the pressure transmitter based on the trend-related features combined with the historical record data corresponding to the pressure transmitter, obtain the fault location source point, identify the core fault type corresponding to the fault location source point, and calculate the fault intensity index corresponding to the fault location source point based on the core fault type; The area determination module 203 is used to delineate the high-risk fault area corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the fault intensity index, perform signal zero point calibration on the output signal in the high-risk area to obtain a calibrated output signal, perform real-time performance detection on the calibrated output signal to obtain a response performance curve, and determine the key maintenance area corresponding to the pressure transmitter based on the response performance curve; The state index module 204 is used to query the component aging data in the key maintenance area, analyze the key failure mode corresponding to the key maintenance area according to the component aging data, extract the maintenance monitoring points in the key failure mode, and calculate the health state index corresponding to the key maintenance area based on the maintenance monitoring points; The report generation module 205 is used to generate an evaluation index set corresponding to the pressure transmitter based on the health status index, query the indicator degradation trend corresponding to the evaluation index set, and identify the multi-factor coupling weight corresponding to the indicator degradation trend, and generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter based on the multi-factor coupling weight.

[0083] In detail, the modules in the computer-aided pressure transmitter fault diagnosis system 200 in the embodiment of the present invention are used in the same manner as described above. Figure 1 The computer-aided pressure transmitter fault diagnosis method described in the invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0084] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A computer-aided pressure transmitter fault diagnosis method, characterized in that: The method comprises: Acquire real-time sensor data of the pressure transmitter during operation, analyze dynamic parameter features corresponding to the real-time sensor data, construct a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter features, and extract trend-related features in the multi-dimensional parameter matrix; Based on the trend-related features and the historical record data corresponding to the pressure transmitter, the pressure transmitter is initially fault-located to obtain a fault location source point, the core fault type corresponding to the fault location source point is identified, and based on the core fault type, the fault intensity index corresponding to the fault location source point is calculated; Based on the fault intensity index, a high-risk fault area corresponding to the internal circuit and the sensing unit of the pressure transmitter is delineated, a signal zero point calibration is performed on the output signal in the high-risk area to obtain a calibrated output signal, a real-time performance detection is performed on the calibrated output signal to obtain a response performance curve, and based on the response performance curve, a key maintenance area corresponding to the pressure transmitter is determined; Querying the component aging data in the key maintenance area, analyzing the key failure mode corresponding to the key maintenance area according to the component aging data, extracting the maintenance monitoring points in the key failure mode, and calculating the health status index corresponding to the key maintenance area based on the maintenance monitoring points; Based on the health status index, an evaluation index set corresponding to the pressure transmitter is generated, the indicator degradation trend corresponding to the evaluation index set is queried, and the multi-factor coupling weight corresponding to the indicator degradation trend is identified; based on the multi-factor coupling weight, a fault monitoring report corresponding to the entire life cycle of the pressure transmitter is generated.

2. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: The step of constructing a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter characteristics includes: Extracting core variables corresponding to the dynamic parameter features; Based on the core variables, generating a time sequence of working conditions corresponding to the pressure transmitter; Perform sliding window segmentation on the working condition time series to obtain window data blocks; Performing cross-domain fusion on the data in the window data block to obtain a fused data block; identifying corresponding multi-dimensional attributes of the fused data block; Based on the multi-dimensional attributes, a multi-dimensional parameter matrix of the pressure transmitter in the operating state is constructed.

3. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: The method of performing an initial fault location on the pressure transmitter based on the trend-related features in combination with the historical record data corresponding to the pressure transmitter to obtain a fault location source point includes: Determine a fault type mode corresponding to the pressure transmitter based on the trend-related features combined with historical record data corresponding to the pressure transmitter; Extracting a fault mode matching point in the fault type mode; Based on the fault mode matching point, query the historical high-incidence component corresponding to the pressure transmitter; Performing correlation analysis on the historically high-incidence components to obtain high-incidence correlated components; Perform preliminary fault location on the frequently associated components to obtain the fault location source point.

4. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: The calculating, based on the core fault type, a fault intensity index corresponding to the fault location source point includes: The fault intensity index corresponding to the fault location source point is calculated using the following formula: ；‘ in, represents the fault intensity index corresponding to the fault location source point, represents the number of fault factors associated with the core fault type, Indicates the quantity index corresponding to the fault factor, Indicates The factor weight corresponding to each fault factor is: represents the actual measured value corresponding to the i-th fault factor, Indicates The normal reference value corresponding to each fault factor is: Indicates The maximum fluctuation allowable value corresponding to each fault factor is: Indicates the monitoring time period, Indicates The failure factor at time The fault impact function.

5. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: Delineating the high-risk fault area corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the fault intensity index includes: Analyzing the index component weights corresponding to the fault intensity index; Based on the index component weights, calculating contribution intensity values ​​corresponding to the internal circuit and the sensing unit of the pressure transmitter; Based on the contribution strength value, the internal circuit and the sensor unit are sorted to obtain a sorting result; Marking components in the ranking results that exceed a preset threshold as high-risk candidates; Based on the high-risk candidate objects, a high-risk failure area corresponding to the pressure transmitter is defined.

6. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: Determining the key maintenance area corresponding to the pressure transmitter based on the response performance curve includes: Identifying an abnormal fluctuation segment in the response performance curve; Marking the key timestamp corresponding to the abnormal fluctuation segment; Based on the key timestamp, trace back the operation log corresponding to the pressure transmitter; Query the abnormal event records in the operation log; Based on the abnormal event record, locate the fault trigger module corresponding to the pressure transmitter; Based on the fault trigger module, a key maintenance area corresponding to the pressure transmitter is determined.

7. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: The querying of the component aging data in the key maintenance area includes: Identify the unique identification code corresponding to the components in the key maintenance area; Based on the unique identification code, retrieve the basic information file corresponding to the component in the key maintenance area; Extracting past maintenance records from the basic information archive; Based on the past maintenance records, the component aging data in the key maintenance area is queried.

8. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: The calculating, based on the maintenance monitoring point, the health status index corresponding to the key maintenance area includes: The health status index corresponding to the key maintenance area is calculated using the following formula: ； in, Indicates the health status index corresponding to the key maintenance area, Indicates the number of maintenance monitoring points. represents the quantity index corresponding to the maintenance monitoring point, Indicates The state coefficient corresponding to each maintenance monitoring point is: Indicates The maintenance weight corresponding to each maintenance monitoring point is: Indicates the number of key components in the key maintenance area. Indicates the quantity index corresponding to the key components, Indicates The performance degradation indicators corresponding to the key components are: represents the number of external environmental factors in the key maintenance area, Represents the quantitative index of external environmental factors, Indicates The corresponding influence coefficient of each external environmental factor is Indicates the total maintenance time. It represents the influence function of the temperature changing with time on the status of the key maintenance area.

9. The computer-aided pressure transmitter fault diagnosis method according to claim 1, characterized in that: The step of generating an evaluation index set corresponding to the pressure transmitter based on the health status index includes: Dividing the state operation interval corresponding to the health state index; Extracting historical fault features in the state operation interval; Constructing a state mapping table corresponding to the historical fault characteristics; Performing hierarchical allocation on the state mapping table to obtain a state hierarchical index; Based on the state hierarchy index, an evaluation index set corresponding to the pressure transmitter is generated.

10. A computer-aided pressure transmitter fault diagnosis system, characterized in that: The system comprises: A feature extraction module is used to obtain real-time sensor data of the pressure transmitter during operation, analyze dynamic parameter features corresponding to the real-time sensor data, construct a multi-dimensional parameter matrix of the pressure transmitter in operation based on the dynamic parameter features, and extract trend-related features in the multi-dimensional parameter matrix; An index calculation module, used to perform preliminary fault location on the pressure transmitter based on the trend-related features combined with the historical record data corresponding to the pressure transmitter, obtain a fault location source point, identify a core fault type corresponding to the fault location source point, and calculate a fault intensity index corresponding to the fault location source point based on the core fault type; an area determination module, for delineating a high-risk area of ​​faults corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the fault intensity index, performing a signal zero point calibration on the output signal in the high-risk area to obtain a calibrated output signal, performing a real-time performance detection on the calibrated output signal to obtain a response performance curve, and determining a key maintenance area corresponding to the pressure transmitter based on the response performance curve; A state index module, used to query the component aging data in the key maintenance area, analyze the key failure mode corresponding to the key maintenance area according to the component aging data, extract the maintenance monitoring points in the key failure mode, and calculate the health state index corresponding to the key maintenance area based on the maintenance monitoring points; A report generation module is used to generate an evaluation index set corresponding to the pressure transmitter based on the health status index, query the indicator degradation trend corresponding to the evaluation index set, and identify the multi-factor coupling weight corresponding to the indicator degradation trend, and generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter based on the multi-factor coupling weight.

Images