Computer-Aided Fault Diagnosis Method and System for Pressure Transmitters

Abstract

The present invention relates to the field of equipment maintenance, and discloses a computer-aided fault diagnosis method and system for a pressure transmitter, including: first obtaining the real-time sensing data of the pressure transmitter, analyzing its dynamic parameter characteristics, constructing a multi-dimensional parameter matrix and extracting trend characteristics, performing a preliminary fault location in combination with historical records, identifying the core fault types and calculating the fault intensity index, then delineating the high-risk fault areas accordingly, calibrating and detecting the output signal to determine the key maintenance areas, then querying the aging data of components within the areas, analyzing the key failure modes, extracting the maintenance monitoring points to calculate the health status index, further generating an evaluation index set, identifying the multi-factor coupling weights, and finally generating a fault monitoring report for the entire life cycle. The present invention can improve the accuracy of fault diagnosis of the pressure transmitter.

Description

Technical Field

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

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

[0003] However, the current traditional fault diagnosis methods for pressure transmitters have obvious limitations. On the one hand, manual inspection depends on the experience and skill level of operation and maintenance personnel. It is not only inefficient but also highly subjective, making it difficult to comprehensively and accurately detect early potential faults. On the other hand, conventional automatic diagnosis methods based on simple threshold judgment cannot adapt to the complex and changeable industrial production environment. The recognition accuracy of the fault types and degrees of pressure transmitters is limited. When encountering complex situations such as sensor aging, circuit parameter drift, and complex working condition interference, traditional diagnosis methods often have difficulty in accurately judging faults in a timely manner, resulting in an extended time for fault troubleshooting and repair. Therefore, a computer - aided fault diagnosis method for pressure transmitters is needed to improve the accuracy of pressure transmitter fault diagnosis. Summary of the Invention

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

[0005] To achieve the above - mentioned purpose, a computer - aided fault diagnosis method for pressure transmitters provided by the present invention includes:

[0006] Obtain the real - time sensing data during the operation of the pressure transmitter, analyze the dynamic parameter characteristics corresponding to the real - time sensing data, based on the dynamic parameter characteristics, construct a multi - dimensional parameter matrix of the pressure transmitter in the operating state, and extract the trend - related characteristics in the multi - dimensional parameter matrix;

[0007] Based on the trend - related characteristics and combined with the historical record data corresponding to the pressure transmitter, conduct an initial fault location for the pressure transmitter to obtain a fault location source point, identify the core fault type corresponding to the fault location source point, and based on the core fault type, calculate the fault intensity index corresponding to the fault location source point;

[0008] Based on the fault intensity index, delimit the high-risk fault areas corresponding to the internal circuit and the sensing unit of the pressure transmitter, calibrate the zero point of the output signal in the high-risk areas 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 areas corresponding to the pressure transmitter based on the response performance curve;

[0009] Query the component aging data in the key maintenance areas, analyze the key failure modes corresponding to the key maintenance areas according to the component aging data, extract the maintenance monitoring points in the key failure modes, and calculate the health status index corresponding to the key maintenance areas based on the maintenance monitoring points;

[0010] Generate an evaluation index set corresponding to the pressure transmitter based on the health status index, query the deterioration trend of the indicators corresponding to the evaluation index set, identify the multi-factor coupling weights corresponding to the indicator deterioration trend, and generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter based on the multi-factor coupling weights.

[0011] Optionally, constructing a multi-dimensional parameter matrix of the pressure transmitter in the operating state based on the dynamic parameter characteristics includes:

[0012] Extract the core variables corresponding to the dynamic parameter characteristics;

[0013] Generate a working condition time series corresponding to the pressure transmitter based on the core variables;

[0014] Perform sliding window segmentation on the working condition time series to obtain window data blocks;

[0015] Perform cross-domain fusion on the data in the window data blocks to obtain fused data blocks;

[0016] Identify the multi-dimensional attributes corresponding to the fused data blocks;

[0017] Construct a multi-dimensional parameter matrix of the pressure transmitter in the operating state based on the multi-dimensional attributes.

[0018] Optionally, fault preliminary location of the pressure transmitter based on the trend-related characteristics combined with the historical record data corresponding to the pressure transmitter to obtain a fault location source point includes:

[0019] Based on the trend-related characteristics combined with the historical record data corresponding to the pressure transmitter, determine the fault type mode corresponding to the pressure transmitter;

[0020] Extract the fault mode matching points in the fault type mode;

[0021] Query the historical high-incidence components corresponding to the pressure transmitter based on the fault mode matching points;

[0022] Conduct correlation analysis on the historical high-incidence components to obtain high-incidence correlated components;

[0023] Conduct initial fault location on the high-incidence correlated components to obtain the fault location source points.

[0024] Optionally, the calculating the fault intensity index corresponding to the fault location source point based on the core fault type includes:

[0025] Calculate the fault intensity index corresponding to the fault location source point using the following formula:

[0026] ;

[0027] where, represents the fault intensity index corresponding to the fault location source point, represents the number of fault factors related to the core fault type, represents the quantity index corresponding to the fault factor, represents the th factor weight corresponding to the i-th fault factor, represents the actual measured value corresponding to the i-th fault factor, represents the th normal reference value corresponding to the fault factor, represents the th maximum allowable fluctuation value corresponding to the fault factor, represents the monitoring time period, represents the th fault influence function of the i-th fault factor at time ;

[0028] Optionally, the delimiting the high-risk fault areas corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the fault intensity index includes:

[0029] Analyze the index composition weights corresponding to the fault intensity index;

[0030] Based on the index composition weights, calculate the contribution intensity values corresponding to the internal circuit and the sensing unit of the pressure transmitter;

[0031] Based on the contribution intensity values, conduct sorting processing on the internal circuit and the sensing unit to obtain a sorting result;

[0032] Mark the components exceeding the preset threshold in the sorting result as high-risk candidate objects;

[0033] Based on the high-risk candidate objects, delimit the high-risk fault area corresponding to the pressure transmitter.

[0034] Optionally, the determining the key maintenance area corresponding to the pressure transmitter based on the response performance curve includes:

[0035] Identify the abnormal fluctuation segments in the response performance curve;

[0036] Mark the key timestamps corresponding to the abnormal fluctuation segments;

[0037] Based on the key timestamps, trace back the operation log corresponding to the pressure transmitter;

[0038] Query the abnormal event records in the operation log;

[0039] Based on the abnormal event records, locate the fault trigger module corresponding to the pressure transmitter;

[0040] Based on the fault trigger module, determine the key maintenance area corresponding to the pressure transmitter

[0041] Optionally, the querying the component aging data in the key maintenance area includes:

[0042] Identify the unique identification codes corresponding to the components in the key maintenance area;

[0043] Based on the unique identification codes, retrieve the basic information files corresponding to the components in the key maintenance area;

[0044] Extract the past maintenance records from the basic information files;

[0045] Based on the past maintenance records, query the component aging data in the key maintenance area.

[0046] Optionally, the calculating the health status index corresponding to the key maintenance area based on the maintenance monitoring points includes:

[0047] Calculate the health status index corresponding to the key maintenance area using the following formula:

[0048] ;

[0049] Where represents the health status index corresponding to the key maintenance area, represents the number corresponding to the maintenance monitoring points, represents the number index corresponding to the maintenance monitoring points, represents the th status coefficient corresponding to the maintenance monitoring point, Indicates the maintenance weight corresponding to the th maintenance monitoring point, indicates the number of key components in the key maintenance area, indicates the quantity index corresponding to the key component, Indicates the th performance degradation index corresponding to the key component, indicates the number of external environmental factors in the key maintenance area, indicates the quantity index of the external environmental factor, Indicates the th influence coefficient corresponding to the external environmental factor, indicates the total maintenance duration, indicates the state influence function of the temperature changing with time on the key maintenance area.

[0050] Optionally, based on the health status index, generating an evaluation index set corresponding to the pressure transmitter, including:

[0051] Dividing the state operation interval corresponding to the health status index;

[0052] Extracting historical fault features in the state operation interval;

[0053] Constructing a state mapping table corresponding to the historical fault features;

[0054] Performing hierarchical allocation on the state mapping table to obtain state hierarchical indicators;

[0055] Generating an evaluation index set corresponding to the pressure transmitter based on the state hierarchical indicators.

[0056] To solve the above problems, the present invention also provides a computer-aided pressure transmitter fault diagnosis system, and the system includes:

[0057] A feature extraction module, configured to obtain real-time sensing data during the operation of the pressure transmitter, analyze dynamic parameter features corresponding to the real-time sensing data, construct a multi-dimensional parameter matrix of the pressure transmitter in the operating state based on the dynamic parameter features, and extract trend-related features in the multi-dimensional parameter matrix;

[0058] An index calculation module, configured to perform initial fault location on the pressure transmitter based on the trend-related features in combination with 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;

[0059] An area determination module, configured to delimit a high-risk failure area corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the failure intensity index, perform signal zero calibration on the output signals in the high-risk area to obtain calibrated output signals, perform real-time performance detection on the calibrated output signals to obtain a response performance curve, and determine a key maintenance area corresponding to the pressure transmitter based on the response performance curve;

[0060] A status index module, configured to query component aging data in the key maintenance area, analyze a key failure mode corresponding to the key maintenance area according to the component aging data, extract maintenance monitoring points in the key failure mode, and calculate a health status index corresponding to the key maintenance area based on the maintenance monitoring points;

[0061] A report generation module, configured to generate an evaluation index set corresponding to the pressure transmitter based on the health status index, query an index deterioration trend corresponding to the evaluation index set, identify a multi-factor coupling weight corresponding to the index deterioration trend, and generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter based on the multi-factor coupling weight.

[0062] Compared with the problems described in the background art, by acquiring the real-time sensing data during the operation of the pressure transmitter and analyzing the dynamic parameter characteristics corresponding to the real-time sensing data, the present invention can promptly detect the subtle changes in the device operation state, providing a key basis for early fault warning. Secondly, the accurate analysis of dynamic parameter characteristics helps to more deeply understand the working characteristics of the device, and then optimize the operation parameters. Based on the trend-related characteristics and the historical record data corresponding to the pressure transmitter, the present invention conducts an initial fault location for the pressure transmitter to obtain the fault location source point, which can comprehensively integrate the current and past device operation information, accurately lock the source where faults are likely to occur, thereby improving the fault troubleshooting efficiency. Further, based on the fault intensity index, the present invention demarcates the high-risk fault areas corresponding to the internal circuit and the sensing unit of the pressure transmitter, can accurately locate the internal problem areas of the pressure transmitter, helps to quickly focus on the key inspection points, improve the maintenance efficiency, and avoid the blindness of comprehensive inspection. At the same time, preventive maintenance can be carried out on the high-risk areas in advance to reduce the probability of sudden faults and ensure the stable and reliable operation of the pressure transmitter. Further, by querying the component aging data in the key maintenance area, the present invention can assist in judging the remaining service life of the components, plan replacements in advance, prevent faults caused by sudden aging and damage of the components, and at the same time, based on the aging data, the aging law of the components can be analyzed, providing strong support for optimizing the device maintenance strategy and improving the overall operation 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 operation status, quickly locate potential problems, provide a scientific basis for maintenance decisions, make reasonable allocation of maintenance resources, improve the maintenance efficiency, reduce the fault risk, and ensure the stable and reliable operation of the pressure transmitter. Therefore, the computer-aided pressure transmitter fault diagnosis method and system provided by the embodiments of the present invention can improve the accuracy of pressure transmitter fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic flowchart of a computer-aided pressure transmitter fault diagnosis method provided by an embodiment of the present invention;

[0064] Figure 2 It is a schematic module diagram of implementing the computer-aided pressure transmitter fault diagnosis system provided by an embodiment of the present invention.

[0065] The realization, functional characteristics and advantages of the present invention will be further described in conjunction with the embodiments with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] 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.

[0067] An embodiment of the present application provides a computer-aided fault diagnosis method for a pressure transmitter. The execution subject of the computer-aided fault diagnosis method for a pressure transmitter includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the computer-aided fault diagnosis method for a pressure transmitter can be executed by software or hardware installed on 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.

[0068] Embodiment 1:

[0069] Referring to Figure 1 As shown, it is a schematic flowchart of a computer-aided fault diagnosis method for a pressure transmitter provided by an embodiment of the present invention. In this embodiment, the computer-aided fault diagnosis method for a pressure transmitter includes:

[0070] S1. Obtain real-time sensing data during the operation of the pressure transmitter, analyze the dynamic parameter characteristics corresponding to the real-time sensing data, based on the dynamic parameter characteristics, construct a multi-dimensional parameter matrix of the pressure transmitter in the operating state, and extract trend-related characteristics in the multi-dimensional parameter matrix.

[0071] By obtaining the real-time sensing data during the operation of the pressure transmitter and analyzing the dynamic parameter characteristics corresponding to the real-time sensing data, the present invention can timely detect the subtle changes in the device operation state, providing a key basis for early fault warning. Secondly, the accurate analysis of dynamic parameter characteristics helps to further understand the device working characteristics, and then optimize the operation parameters.

[0072] 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 regulation; the real-time sensing 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 regulation; the dynamic parameter characteristics can refer to the change rate of the pressure value over a period of time, such as a pressure increase of 0.1 MPa per minute, or the frequency of pressure fluctuations, such as 10 fluctuations per hour, etc., which can show the dynamic changes in the operating state of the device. Optionally, the acquisition of the real-time sensing data during the operation of the pressure transmitter can be achieved through an adaptive sampling algorithm. For example, the sampling interval is dynamically adjusted according to the change frequency of the output signal of the pressure transmitter; when the signal changes violently, the sampling interval is shortened to obtain more accurate data; when the signal is relatively stable, the sampling interval is increased to reduce the pressure of data transmission and storage, so as to efficiently obtain real-time sensing data; the analysis of the dynamic parameter characteristics corresponding to the real-time sensing data can be achieved through parameter analysis tools, such as tools like MATLAB and Python.

[0073] 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 device 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.

[0074] Among them, the multi-dimensional parameter matrix refers to a matrix constructed based on the multi-dimensional attributes of the fusion data block, and each row of the matrix represents a fusion data block, and each column corresponds to a multi-dimensional attribute of the fusion data block.

[0075] As an embodiment of the present invention, the construction of the multi-dimensional parameter matrix of the pressure transmitter in the operating state based on the dynamic parameter characteristics includes: extracting the core variables corresponding to the dynamic parameter characteristics; generating the working condition time series corresponding to the pressure transmitter based on the core variables; performing 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 blocks to obtain fusion data blocks; identifying the corresponding multi-dimensional attributes of the fusion data blocks; and constructing the multi-dimensional parameter matrix of the pressure transmitter in the operating state based on the multi-dimensional attributes.

[0076] Among them, the core variables refer to the variables extracted from the dynamic parameter features, which are the most critical and representative for characterizing the operating state of the pressure transmitter. These variables can prominently reflect the core state information during the operation of the pressure transmitter, such as the change rate of pressure, the extreme value of signal fluctuation amplitude, etc.; the working condition time series refers to a set of data points arranged in chronological order based on the core variables, 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 state of the pressure transmitter over time can be clearly observed; the window data block refers to the result obtained by segmenting the working condition time series with a sliding window. The sliding window is a time window with a fixed size that slides sequentially on the working condition time series, and the data within the window is intercepted each time as a window data block. Each window data block contains the relevant data of the pressure transmitter operation within a specific time period; the fusion data block refers to the data set obtained by cross-domain fusion of the data in the window data block. Cross-domain fusion means organically integrating data from different domains (such as the pressure measurement domain, temperature influence domain, etc.) within the window data block and comprehensively considering the influence of multiple factors on the operating state of the pressure transmitter; the multi-dimensional attributes refer to the multiple different characteristic attributes of the fusion data block. These attributes describe the characteristics of the fusion data block from multiple perspectives, including but not limited to the statistical attributes of the data (such as mean, variance, kurtosis, etc.), frequency attributes (the frequency components and distribution of the signal), correlation attributes (the degree of association between different variables), etc.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] S2. Based on the trend-related features and combined with the historical record data corresponding to the pressure transmitter, conduct a preliminary fault location for the pressure transmitter to obtain a 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.

[0081] Based on the trend-related features and combined with the historical record data corresponding to the pressure transmitter, the present invention conducts a preliminary fault location for the pressure transmitter to obtain a fault location source point, which can comprehensively integrate the current and past equipment operation information, accurately lock the source where faults are likely to occur, and thus improve the efficiency of fault troubleshooting.

[0082] Among them, the historical record data refers to various data sets accumulated during the past operation of the pressure transmitter, which covers the operation 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 part information, etc., as well as previously occurred 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 determined through preliminary fault location of frequently-occurring associated components, which is the key result of fault diagnosis, clarifies the root cause of the fault. Once the fault location source point is determined, it is possible to specifically detect, repair, or replace this component or position, thereby solving the fault problem of the pressure transmitter and restoring the normal operation of the equipment.

[0083] As an embodiment of the present invention, the step of conducting a preliminary fault location for the pressure transmitter based on the trend-related features and combined with the historical record data corresponding to the pressure transmitter to obtain a fault location source point includes:

[0084] Based on the trend-related features and combined with the historical record data corresponding to the pressure transmitter, determine the fault type pattern corresponding to the pressure transmitter; extract the fault mode matching points in the fault type pattern; based on the fault mode matching points, query the historical frequently-occurring components corresponding to the pressure transmitter; conduct an association analysis on the historical frequently-occurring components to obtain frequently-occurring associated components; conduct a preliminary fault location for the frequently-occurring associated components to obtain a fault location source point.

[0085] Among them, the fault type pattern refers to a set of fault manifestation forms with typical characteristics summarized based on trend-related features and the historical record data of the pressure transmitter. It comprehensively reflects how the operating parameters, signal characteristics, etc. of the pressure transmitter change over time when different faults occur, and is an abstract generalization of various faults. For example, continuous abnormal pressure fluctuations can correspond to the sensor fault mode, and signal drift corresponds to the circuit parameter drift fault mode, etc.; the fault mode matching point refers to a set of fault manifestation forms with typical characteristics summarized based on trend-related features and the historical record data of the pressure transmitter. It comprehensively reflects how the operating parameters, signal characteristics, etc. of the pressure transmitter change over time when different faults occur, and is an abstract generalization of various faults. For example, continuous abnormal pressure fluctuations may correspond to the sensor fault mode, and signal drift corresponds to the circuit parameter drift fault mode, etc.; the historical high-incidence component refers to the components that frequently have faults or have a relatively high probability of faults in the historical record data of the pressure transmitter. These components are more likely to have problems than other components due to factors such as their own quality, working environment, and usage frequency. For example, the diaphragm of the pressure sensor, some capacitors and resistors in the signal conditioning circuit, etc.; the high-incidence associated component refers to other components obtained through correlation analysis of the historical high-incidence components and are closely related to the historical high-incidence components in terms of function, physical connection, or signal transmission. When the historical high-incidence component fails, the probability of these associated components being affected and failing increases. For example, a certain 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.

[0086] Further, the determination of the fault type mode corresponding to the pressure transmitter can be achieved through a pattern recognition method. For example, taking trend-related features and historical record data as inputs, comparing them with pre-defined fault type templates to find the most matching fault type; the extraction of the fault mode matching points in the fault type mode can be achieved through a threshold setting method. For example, according to historical data and experience, setting thresholds for key features in the fault type mode, and when the feature value exceeds or is lower than this threshold, taking it 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. For example, counting the number of fault occurrences of each component in the historical record data, sorting them according to the fault occurrence frequency, and finding the components with higher frequencies as historical high-incidence components; the correlation analysis of the historical high-incidence components can be achieved through a network analysis method. For example, regarding each component of the pressure transmitter as a network node and the connection relationship between components as edges, and finding the components closely connected to the historical high-incidence components as high-incidence associated components through the network analysis method; the initial fault location of the high-incidence associated components can be achieved through a fault injection method. For example, performing simulated fault injection on the high-incidence associated components, observing the changes in the operating parameters of the pressure transmitter, and finding the component that causes the fault phenomenon to appear as the fault location source point.

[0087] By identifying the core fault type corresponding to the fault location source point, the present invention helps to quickly understand the key to the fault, thereby formulating a targeted repair strategy to 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 the efficiency of fault resolution.

[0088] Among them, the core fault type refers to the fault category that is representative, universal, and has the most critical and significant impact on the operation of the equipment, summarized from numerous easily occurring fault manifestations and causes when the pressure transmitter fails. It is usually the fault type corresponding to the main and fundamental reasons for whether the pressure transmitter works normally. For example, damage to the pressure sensor, interruption of the signal transmission line, failure of the circuit control module, etc. Optionally, the identification of the core fault type corresponding to the fault location source point can be achieved through a decision tree algorithm. For example, taking various features (such as pressure, temperature, output signal, etc.) of the fault location source point as inputs, constructing a decision tree model, and gradually determining the core fault type through the judgment of these features by the decision tree model.

[0089] Further, 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 influence degree of the fault location source point on the pressure transmitter, help quickly judge the fault priority in the follow-up, reasonably arrange maintenance resources, give priority to dealing with serious faults, and efficiently ensure the stable operation of the equipment.

[0090] Among them, the fault intensity index refers to a comprehensive quantitative index used to measure the severity of the fault corresponding to the fault location source point based on the core fault type.

[0091] As an embodiment of the present invention, calculating the fault intensity index corresponding to the fault location source point based on the core fault type includes:

[0092] Calculating the fault intensity index corresponding to the fault location source point by using the following formula:

[0093] ;

[0094] Wherein, represents the fault intensity index corresponding to the fault location source point, represents the number of fault factors related to the core fault type, represents the quantity index corresponding to the fault factor, represents the th factor weight corresponding to the fault factor, represents the actual measured value corresponding to the i-th fault factor, represents the th normal reference value corresponding to the fault factor, represents the th maximum allowable fluctuation value corresponding to the fault factor, represents the monitoring time period, represents the th fault influence function of the fault factor at time

[0095] ​Specifically, the factor weight refers to the relative importance of the i-th fault factor when calculating the fault intensity index. For example, for a pressure transmitter, if the degradation of the sensor performance has a great impact on its normal operation, then the weight of the fault factor of sensor performance will be relatively high; the actual measured value refers to the real-time value obtained through various monitoring means (such as sensor measurement, data acquisition, etc.) during the operation of the equipment for the i-th fault factor. For instance, if the fault factor is the operating temperature of the equipment, then the actual measured value is the current temperature value of the equipment measured in real time by the temperature sensor; the normal reference value refers to the standard value or value range of the i-th fault factor when the equipment is in a normal and fault-free operating state; the maximum allowable fluctuation value refers to the maximum deviation range allowed for the actual measured value of the i-th fault factor relative to the normal reference value during the normal operation of the equipment, and it is a threshold determined according to factors such as the design performance and working environment of the equipment; the monitoring time period refers to the time length for continuously monitoring the fault factors of the equipment, and this period can be set according to factors such as the characteristics of the equipment, usage frequency, and probability of fault occurrence; the fault impact function refers to a function that describes the degree of influence of the i-th 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.

[0096] S3. Based on the fault intensity index, delimit the high-risk fault area corresponding to the internal circuit and sensing unit of the pressure transmitter, perform signal zero 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 based on the response performance curve, determine the key maintenance area corresponding to the pressure transmitter.

[0097] Based on the fault intensity index, the present invention delimits the high-risk fault area corresponding to the internal circuit and sensing unit of the pressure transmitter, can accurately locate the problem area inside the pressure transmitter, helps to quickly focus on the key points of inspection, improves the maintenance efficiency, and avoids the blindness of comprehensive investigation. At the same time, preventive maintenance can be carried out on the high-risk area in advance, reducing the probability of sudden faults and ensuring the stable and reliable operation of the pressure transmitter.

[0098] Among them, the internal circuit refers to a system composed of various electronic components (such as resistors, capacitors, transistors, integrated circuits, etc.) and circuits, which is responsible for processing the signals transmitted by the sensing unit. It can perform operations such as amplifying, filtering, and converting weak original signals into standard signals (such as 4-20mA current signals, 0-5V voltage signals, etc.) that can be recognized and processed by subsequent devices. At the same time, it also undertakes functions such as power management and self-diagnosis, and is a key part for the pressure transmitter to achieve signal transmission and control; the sensing unit is one of the core components of the pressure transmitter, and its main function is to sense pressure changes. It is usually composed of pressure-sensitive elements (such as piezoresistive, capacitive, piezoelectric, etc. sensitive elements), and can convert the external pressure physical quantity into an electrical signal. For example, when the piezoresistive sensitive element is subjected to pressure, its resistance value will change, thereby generating an electrical signal output that has a certain relationship with the pressure, providing the original data for subsequent signal processing and transmission; the high-fault-risk area refers to the range jointly composed of high-risk candidate objects and the areas around them that have a close electrical connection and signal transmission relationship with them. Defining this area is to be able to focus on checking and processing these areas that are most likely to fail when troubleshooting and maintaining the pressure transmitter.

[0099] As an embodiment of the present invention, defining the high-fault-risk area corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the fault intensity index includes: analyzing the index composition weights corresponding to the fault intensity index; calculating the contribution intensity values corresponding to the internal circuit and the sensing unit of the pressure transmitter based on the index composition weights; performing a sorting process on the internal circuit and the sensing unit based on the contribution intensity values to obtain a sorting result; marking the components that exceed the preset threshold in the sorting result as high-risk candidate objects; and defining the high-fault-risk area corresponding to the pressure transmitter based on the high-risk candidate objects.

[0100] Among them, the exponential composition weight refers to the degree of influence of different factors on the magnitude of the fault intensity index, which is determined by experts based on experience or through data analysis. Its 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 this factor on the fault intensity index. The contribution intensity value refers to the numerical value of the contribution degree of each component to the fault intensity index, calculated according to the exponential composition weight and combined with the actual situation of the fault-related factors corresponding to each component in the internal circuit and sensing unit of the pressure transmitter. The sorting result refers to a list of arranging the components of the internal circuit and sensing unit of the pressure transmitter in descending (or ascending, according to specific settings) order of the contribution intensity value. The preset threshold refers to a standard value set in advance, used to measure whether the contribution intensity value of a component reaches a relatively high level. When the contribution intensity value of a component exceeds this threshold, it is considered to have a relatively high fault risk. The high-risk candidate objects refer to the components of the internal circuit and sensing unit of the pressure transmitter whose contribution intensity values exceed the preset threshold in the sorting result. These components are initially identified as having a relatively high probability of failure under the current conditions and are the objects of key attention and inspection in the follow-up.

[0101] Furthermore, the analysis of the exponential composition weight corresponding to the fault intensity index can be achieved through a hierarchical structure model. For example, the factors affecting the fault intensity are regarded as the criterion layer, such as temperature, humidity, pressure, etc. A judgment matrix is constructed through methods such as expert scoring, and the weights of each factor are calculated. The calculation of the contribution intensity value corresponding to the internal circuit and sensing unit of the pressure transmitter can be achieved through the weighted average method. For example, according to the weights of each factor and the values of each component under the corresponding factors, the contribution intensity value is calculated. The sorting process of the internal circuit and the sensing unit can be achieved through the bubble sorting algorithm. For example, repeatedly visit the column of elements to be sorted, compare adjacent two elements in turn. If the order (such as from large to small, or from A to Z in terms of the first letter) is incorrect, swap them. Finally, the sorting result is obtained. Marking the components exceeding the preset threshold in the sorting result as high-risk candidate objects can be achieved through the threshold comparison method. For example, assuming the preset threshold is 75, in MATLAB, a loop statement can be used to traverse the sorted list of contribution intensity values. When a value greater than 75 is encountered, the corresponding component is marked as a high-risk candidate object. Defining the high-risk fault area corresponding to the pressure transmitter can be achieved through the connected graph algorithm. For example, the internal circuit and sensing unit of the pressure transmitter are regarded as a graph structure, with components as nodes and the electrical connection relationships between components as edges. The area is defined by searching for the nodes connected to the high-risk candidate objects.

[0102] By performing signal zero calibration on the output signal in the high-risk area, the present invention obtains a calibrated output signal. The calibrated output signal is more accurate, which can ensure that the pressure data fed back by the pressure transmitter is true and reliable, providing an accurate basis for subsequent industrial control and monitoring systems based on this data, and avoiding incorrect decisions and abnormal operation of equipment caused by signal deviation.

[0103] Among them, the calibrated output signal refers to a more accurate and reliable signal obtained after zero calibration of the output signal in the high-risk area. It corrects the possible zero offset of the original signal, enabling the signal value to accurately reflect the actual pressure measured by the pressure transmitter, providing a solid foundation for the precise control and accurate data analysis of the subsequent system, ensuring the stable operation of the pressure transmitter and related systems. Optionally, the signal zero calibration of the output signal in the high-risk area can be achieved through signal calibration tools, such as pressure calibrators, multimeters, etc.

[0104] Furthermore, by performing real-time performance detection on the calibrated output signal, the present invention obtains a response performance curve, which can quickly insight into key performance indicators such as the response speed and stability of the signal, facilitating the timely discovery of potential anomalies, providing a strong basis for continuously optimizing the performance of the pressure transmitter and ensuring the accuracy and reliability of measurement data, and improving the overall operation efficiency of the equipment.

[0105] Among them, the response performance curve refers to a curve plotted with time as the horizontal axis and the key performance parameters (such as amplitude, frequency, phase, etc.) of the calibrated output signal as the vertical axis. For example, when the pressure changes, through the response performance curve, it can be clearly seen how the output signal tracks the pressure change over time, including the rate of signal rise or fall, the time required to reach a stable state, and whether there are fluctuations during the process. Optionally, the real-time performance detection of the calibrated output signal can be achieved through time-domain analysis methods. For example, when a step input is given to the pressure transmitter, the time required for the calibrated output signal to rise from the initial value to a certain proportion (such as 90%) of the final value is recorded as the rise time. By depicting the response characteristics of the signal in the time domain through multiple such time parameters, a response performance curve can be constructed.

[0106] Furthermore, based on the response performance curve, the present invention determines the key maintenance area corresponding to the pressure transmitter. The curve can accurately locate the performance abnormal area, avoiding blind overall maintenance, saving manpower and time costs, and can also intervene in the key areas prone to problems in advance, reducing the probability of failure, extending the service life of the equipment, and ensuring its stable operation.

[0107] Among them, the key maintenance area refers to the part that surrounds the fault trigger module and its surrounding parts that are closely associated with it. This not only includes the module itself where the fault directly occurs, but also covers other components or areas that are closely related to this module in terms of electrical connection, signal transmission, physical structure, etc.

[0108] 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 section in the response performance curve; marking the key timestamps corresponding to the abnormal fluctuation section; based on the key timestamps, tracing back the operation log corresponding to the pressure transmitter; querying the abnormal event records in the operation log; based on the abnormal event records, locating the fault trigger module corresponding to the pressure transmitter; and based on the fault trigger module, determining the key maintenance area corresponding to the pressure transmitter.

[0109] Among them, the abnormal fluctuation section refers to the part of the curve in the response performance curve that deviates from the normal fluctuation range, shows obvious irregularity or has a large difference from historical data and theoretical expectations. These fluctuations can be manifested as a sudden increase or decrease in the signal amplitude, an abnormal change in the fluctuation frequency, etc.; the key timestamp refers to the time marking point corresponding to the abnormal fluctuation section, which accurately records the time when the abnormal fluctuation starts, ends, or the time when representative features (such as the occurrence time of the peak value and valley value) appear during the fluctuation process; the operation log refers to a set of information automatically recorded or manually input during the operation of the pressure transmitter, which details the operation parameters of the device at different time points, such as the pressure measurement value, output signal strength, working voltage, current, etc., and also includes the device start and stop times, as well as various events that occur during the operation of the device; the abnormal event record refers to the record content in the operation log specifically for the abnormal situation of the pressure transmitter. These records describe the time when the abnormality occurs (corresponding to the key timestamp), the abnormal phenomenon (such as the interruption of the output signal, the excessive deviation between the measured pressure value and the actual value, etc.), the speculated cause of the occurrence (if any), and the temporary measures taken, etc.; the fault trigger module refers to the specific functional module or component that causes the abnormal fluctuation of the pressure transmitter through in-depth analysis of the abnormal event record. For example, it can be that the pressure sensor module ages due to long-term use, resulting in inaccurate measurement and thus causing an abnormal response performance curve; it can also be that the signal processing circuit module is affected by electromagnetic interference, causing abnormal fluctuations in the output signal. These modules that cause faults are the fault trigger modules.

[0110] Further, the identification of the abnormal fluctuation segment in the response performance curve can be achieved through a threshold-based detection method. For example, a threshold for the normal fluctuation range is set, and when the curve data exceeds this threshold range, it is considered an abnormal fluctuation segment. The marking of the key timestamps corresponding to the abnormal fluctuation segment can be achieved through a traversal method. For example, starting from the starting point of the identified abnormal fluctuation segment, traverse in the order of data and record 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. For example, for the operation log stored in an ordered manner, the binary search algorithm can be used to quickly locate the approximate position where the key timestamp is located, and then obtain the complete operation log record. The querying of the abnormal event records in the operation log can be achieved through a regular expression matching algorithm. For example, use regular expressions to define the pattern of abnormal events and match them in the operation log text to find the abnormal event records that conform to the pattern. The positioning of the fault trigger module corresponding to the pressure transmitter can be achieved through a fault tree analysis method. For example, according to the abnormal event records and the working principle of the pressure transmitter, construct a fault tree, and analyze step by step from the top event (abnormal phenomenon) to find the underlying 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. For example, regard each module of the pressure transmitter as a node of the graph, and the connection relationship between the modules as edges, and start a breadth-first search from the node corresponding to the fault trigger module. The modules and areas corresponding to the nodes within a certain search depth are the key maintenance areas.

[0111] S4. Query the component aging data in the key maintenance area, analyze the key failure modes corresponding to the key maintenance area according to the component aging data, extract the maintenance monitoring points in the key failure modes, and calculate the health status index corresponding to the key maintenance area based on the maintenance monitoring points.

[0112] By querying the component aging data in the key maintenance area, the present invention can assist in judging the remaining service life of components, plan replacements in advance, prevent failures caused by sudden aging and damage of components, and at the same time, based on the aging data, it can also analyze the aging law of components, providing strong support for optimizing the equipment maintenance strategy and improving the overall operation reliability.

[0113] Among them, the component aging data refers to a series of data indicators that can quantitatively represent the aging degree of components, including the cumulative operation duration of components. Generally, the longer the operation duration, the higher the aging degree; the change situation of key performance parameters, such as wear degree, accuracy decline value, electrical performance degradation index, etc.; the frequency of fault occurrence. As the component ages, the number of component faults will increase.

[0114] As an embodiment of the present invention, querying 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 from the basic information file; and based on the past maintenance records, querying the component aging data in the key maintenance area.

[0115] Among them, the unique identification code refers to a unique identification code assigned to each component in the key maintenance area. This code is like the "ID card number" of the component and can be in the form of numbers, letters, or a combination of both, running 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 situation of the components; the past maintenance records refer to the information of various maintenance activities received by the components during use, including the time of each maintenance, a detailed description of the specific content of the maintenance, such as whether cleaning, calibration, replacement of parts, etc. are carried out; recording the reasons for maintenance, such as fault repair, regular maintenance, etc.; and also recording the information of the maintenance personnel and the operation status feedback of the components after maintenance.

[0116] Further, the identification of the unique identification code corresponding to the component in the key maintenance area can be achieved through an image recognition algorithm. For example: using open-source computer vision libraries such as OpenCV, collecting the component QR code image through a camera, preprocessing the image by the algorithm (such as grayscale conversion, 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. For example: in some NoSQL databases based on hash table structures, using the corresponding hash search algorithm to quickly obtain its basic information file according to the component code; the extraction of the past maintenance records from the basic information file can be achieved through a JSON parsing algorithm (if the basic information file is stored in JSON format). For example: parsing the JSON data into a Python dictionary, and then extracting the past maintenance record information according to the key-value pair relationship of the dictionary; the query of the component aging data in the key maintenance area can be achieved through a neural network algorithm. For example: using the TensorFlow library in Python to build a neural network, training with a large amount of historical data to enable the model to accurately predict the aging degree of the component, and finally obtaining the component aging data.

[0117] Based on the component aging data, the present invention analyzes the key failure modes corresponding to the key maintenance areas, extracts the maintenance monitoring points in the key failure modes, anticipates the potential failure risks of the equipment in advance. Extracting the maintenance monitoring points in the key failure modes can make the maintenance work more targeted, concentrate resources on key parts for key monitoring, discover and solve problems in a timely manner, and effectively reduce the equipment failure rate.

[0118] Among them, the key failure mode refers to the most main and influential failure form that causes the equipment or system to lose its specified function, which is summarized based on the component aging data in the key maintenance area of the pressure transmitter. For example, for the sensing unit of the pressure transmitter, the key failure mode may be that the sensor diaphragm ages and ruptures, resulting in inaccurate pressure measurement; or the solder joints of the internal circuit age and become desoldered due to long-term thermal expansion and contraction, causing signal transmission interruption. The maintenance monitoring point refers to the specific position or parameter determined from the key failure mode for real-time monitoring of the equipment operation status and evaluation of the equipment health degree. For example, in the key failure mode of the sensor diaphragm aging and rupturing, the pressure deformation degree of the diaphragm and the tiny current change in the surrounding circuit 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 realized through the FMEA system. For example, by systematically analyzing the possible failure modes of the components and evaluating their influence degrees on the entire key maintenance area and the function of the pressure transmitter, the key failure modes can be determined. The extraction of the maintenance monitoring points in the key failure modes can be realized through the fault feature extraction method. For example, the characteristic parameters that can represent the occurrence and development of the fault are extracted from the historical data of the key failure mode, and the measurement positions or indicators corresponding to these characteristic parameters are used as maintenance monitoring points.

[0119] Based on the maintenance monitoring points, the present invention calculates the health status index corresponding to the key maintenance area, which is convenient for intuitively grasping the equipment health degree and quickly positioning potential risks. Accordingly, the maintenance strategy can be planned in advance, resources can be reasonably allocated, faults can be effectively prevented, and the stable and efficient operation of the equipment can be guaranteed.

[0120] Among them, the health status index refers to a quantitative numerical index for comprehensively evaluating the overall health status of the key maintenance area. The higher the value, the better the health status of the key maintenance area, and the lower the possibility of failure or performance decline; the lower the value, the worse the health status, and closer attention or maintenance measures are needed.

[0121] As an embodiment of the present invention, the calculation of the health status index corresponding to the key maintenance area based on the maintenance monitoring points includes:

[0122] Calculate the health status index corresponding to the key maintenance area using the following formula:

[0123] ;

[0124] where, represents the health status index corresponding to the key maintenance area, represents the quantity corresponding to the maintenance monitoring point, represents the quantity index corresponding to the maintenance monitoring point, represents the th state coefficient corresponding to the maintenance monitoring point, represents the th maintenance weight corresponding to the maintenance monitoring point, represents the quantity of key components within the key maintenance area, represents the quantity index corresponding to the key component, represents the th performance degradation index corresponding to the key component, represents the quantity of external environmental factors in the key maintenance area, represents the quantity index of the external environmental factor, represents the th influence coefficient corresponding to the external environmental factor, represents the total maintenance duration, represents the state influence function of the temperature changing with time on the key maintenance area.

[0125] Specifically, the state coefficient refers to, for each maintenance monitoring point, a coefficient whose value range is usually between 0 and 1, and is used to reflect the actual state of the i-th maintenance monitoring point; the maintenance weight also corresponds to each maintenance monitoring point, and is a weight value ranging from 0 to 1, which reflects the relative importance of the i-th maintenance monitoring point when 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, transmission devices, etc. may be regarded as key components; in electronic equipment, core processors, power modules, etc. belong to key components; the performance degradation index refers to an index used to measure the degradation degree of the performance of the i-th key component relative to the initial state or ideal state, and it can be the change values of various physical quantities or parameters, such as the wear amount of mechanical components, the signal attenuation degree of electronic components, the decrease ratio of the operating efficiency of the equipment, etc.; the external environmental factors refer to various factors in the external environment where the key maintenance area is located that can affect the health status of the equipment or system. For example, a high-temperature environment can accelerate the aging of electronic components, a high-humidity environment may cause metal components to rust and corrode, and vibration may loosen connecting components, etc.; the influence coefficient refers to, for each external environmental factor, a coefficient whose value range is between 0 and 1, which represents the influence degree of the k-th external environmental factor on the health status of the key maintenance area; the total maintenance duration refers to the total time length of monitoring and maintaining the key maintenance area, which reflects the cumulative influence of various factors on the health status of the key maintenance area over a relatively long period; the state influence function refers to a function that changes with time and specifically describes the influence 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, and different temperature change situations will have different degrees of influence on the components in the key maintenance area.

[0126] S5. Based on the health state index, generate an evaluation index set corresponding to the pressure transmitter, query the index deterioration trend corresponding to the evaluation index set, identify the multi-factor coupling weight corresponding to the index deterioration trend, and based on the multi-factor coupling weight, generate a fault monitoring report corresponding to the entire life cycle of the pressure transmitter.

[0127] Based on the health state index of the present invention, an evaluation index set corresponding to the pressure transmitter is generated, which can comprehensively and quantitatively evaluate its operating condition, and can quickly locate potential problems, provide a scientific basis for maintenance decisions, make reasonable allocation of maintenance resources, improve maintenance efficiency, reduce the risk of failures, and ensure the stable and reliable operation of the pressure transmitter.

[0128] Among them, the evaluation index set refers to a comprehensive index set, which includes a series of indexes for evaluating the performance and state of the pressure transmitter generated based on the state hierarchical indexes and other relevant factors. These indexes cover multiple aspects such as the reliability, stability, accuracy, and failure risk of the pressure transmitter.

[0129] As an embodiment of the present invention, generating the evaluation index set corresponding to the pressure transmitter based on the health state index includes: dividing the state operation interval corresponding to the health state index; extracting the historical fault characteristics 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 state hierarchical indexes; and generating the evaluation index set corresponding to the pressure transmitter based on the state hierarchical indexes.

[0130] Among them, the state operation interval refers to different intervals divided according to the numerical range of the health state index. These intervals represent different operation state levels of the pressure transmitter. For example, they can be divided into intervals such as good, normal, warning, and failure; the historical fault characteristics refer to various characteristics and attributes presented when the pressure transmitter has had a fault in the past. It can include specific parameter values at the time of the fault (such as abnormal pressure values, temperature values, current values, etc.), the time law of the fault occurrence, the manifestation form of the fault (such as unstable output signal, too large measurement deviation, etc.), and the change of maintenance monitoring point data related to the fault and other information; the state mapping table refers to a table that corresponds and associates the state operation interval with the 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 characteristic situations can be quickly understood; the state hierarchical index refers to the index obtained after hierarchical processing of the state mapping table. It classifies, grades, and sorts the content 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 and occurrence probability of the fault, so as to obtain state indexes at different levels.

[0131] Further, the division of the state operation intervals corresponding to the health status index can be achieved through statistical analysis methods. For example, by collecting a large amount of health status index data of pressure transmitters, calculating statistics such as the mean and standard deviation of the data, and dividing the intervals based on these statistics; the extraction of historical fault features in the state operation intervals can be achieved through data retrieval methods. For example, in the historical fault database, according to the divided state operation intervals, retrieving the relevant data when faults occurred within the corresponding intervals and extracting features from them to obtain historical fault features; the construction of the state mapping table corresponding to the historical fault features can be achieved through unsupervised classification algorithms. For example, clustering the historical fault features and then mapping the clustering results to the state operation intervals to obtain the state mapping table; the hierarchical allocation of the state mapping table can be achieved through the analytic hierarchy process. For example, by constructing a judgment matrix to determine the relative importance of each historical fault feature under different state operation intervals, and thus performing hierarchical allocation to obtain the state hierarchy index; the generation of the evaluation index set corresponding to the pressure transmitter can be achieved through the index synthesis method. For example, synthesizing the state hierarchy index with other relevant indexes (such as indexes of the accuracy, stability, etc. of the pressure transmitter) to generate the evaluation index set.

[0132] By querying the index deterioration trend corresponding to the evaluation index set and identifying the multi-factor coupling weights corresponding to the index deterioration trend, the present invention can clarify the action degrees of various influencing factors, help maintenance personnel accurately focus on key factors, reasonably allocate resources, formulate more targeted maintenance strategies, and effectively ensure the stable operation of the pressure transmitter.

[0133] Among them, the deterioration trend of the indicators refers to the trend and situation in which the performance of various evaluation indicators of the pressure transmitter gradually deteriorates with the change of factors such as time or the number of uses. For example, the measurement accuracy indicator will gradually decrease with the increase of use time, manifested as an increasing deviation between the measured value and the true value; the stability indicator will show an increase in fluctuations, etc. The multi-factor coupling weight refers to the relative importance of each factor in the deterioration of the indicators under the interaction and mutual influence of multiple factors affecting the deterioration of the pressure transmitter indicators. For example, factors such as environmental temperature, humidity, pressure fluctuation, and the wear of the equipment itself will all affect the performance of the pressure transmitter. By a certain method, the weight value of each factor in the process of causing the deterioration of the indicators is calculated, and this weight value reflects the importance of the factor. Optionally, querying the deterioration trend of the evaluation indicator set corresponding to the pressure transmitter can be realized by the ARIMA algorithm. For example, regarding the time series data as a random process, the change trend of the indicator is determined through the fitting and prediction of historical data; identifying the multi-factor coupling weight corresponding to the deterioration trend of the indicator can be realized by the entropy weight method. For example, using the entropy weight method to analyze the historical data of these factors, calculating the entropy value and weight of each factor, so as to determine the multi-factor coupling weight.

[0134] Furthermore, based on the multi-factor coupling weight, the present invention generates a fault monitoring report corresponding to the entire life cycle of the pressure transmitter, which can accurately locate the high-fault-occurrence links. It intuitively presents the fault risks at different stages according to the influence degree of each factor, and strengthens the monitoring at key nodes, greatly improving the efficiency of fault prevention and ensuring the stable operation of the pressure transmitter.

[0135] Among them, the fault monitoring report refers to a comprehensive and detailed document focusing on the fault-related situations within the entire life cycle of the pressure transmitter. It integrates key information such as multi-factor coupling weights and sorts out the operating states 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 each influencing factor to predict the types, times, and locations of possible faults in the future. Through forms such as data charts and text analysis, it intuitively presents the overall health status of the equipment, providing a strong basis for operation and maintenance decisions. Optionally, generating the fault monitoring report corresponding to the entire life cycle of the pressure transmitter can be realized by visualization tools, such as tools like Tableau and PowerBI.

[0136] Compared with the problems described in the background art, by acquiring the real-time sensing data during the operation of the pressure transmitter and analyzing the dynamic parameter characteristics corresponding to the real-time sensing data, the present invention can promptly detect the subtle changes in the operating state of the device, providing a key basis for early fault warning. Secondly, the accurate analysis of dynamic parameter characteristics helps to better understand the working characteristics of the device, and then optimize the operating parameters. Based on the trend-related characteristics and the historical record data corresponding to the pressure transmitter, the present invention performs an initial fault location on the pressure transmitter to obtain the fault location source point, which can comprehensively integrate the current and past device operating information, accurately lock the source where faults are likely to occur, thereby improving the efficiency of fault troubleshooting. Further, based on the fault intensity index, the present invention delimits the high-risk fault areas corresponding to the internal circuit and the sensing unit of the pressure transmitter, which can accurately locate the problem areas inside the pressure transmitter, help quickly focus on the key inspection points, improve the maintenance efficiency, and avoid the blindness of comprehensive inspection. At the same time, preventive maintenance can be carried out on the high-risk areas in advance to reduce the probability of sudden faults and ensure the stable and reliable operation of the pressure transmitter. Further, by querying the component aging data in the key maintenance areas, the present invention can assist in judging the remaining service life of the components, plan replacements in advance, prevent faults caused by sudden aging and damage of the components, and at the same time, analyze the component aging law based on the aging data, providing strong support for optimizing the device maintenance strategy and improving the overall operation 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 condition, quickly locate potential problems, provide a scientific basis for maintenance decisions, rationally allocate maintenance resources, improve maintenance efficiency, reduce the fault risk, and ensure the stable and reliable operation of the pressure transmitter. Therefore, the computer-aided pressure transmitter fault diagnosis method and system provided by the embodiments of the present invention can improve the accuracy of pressure transmitter fault diagnosis.

[0137] Embodiment 2: As Figure 2 shown, it is a functional module diagram of a computer-aided pressure transmitter fault diagnosis system of the present invention.

[0138] The computer-aided pressure transmitter fault diagnosis system 200 of the present invention can be installed in an electronic device. According to the implemented functions, the computer-aided pressure transmitter fault diagnosis system may 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 modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0139] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0140] The feature extraction module 201 is configured to obtain real-time sensing data during the operation of the pressure transmitter, analyze the dynamic parameter features corresponding to the real-time sensing data, construct a multi-dimensional parameter matrix of the pressure transmitter in the operating state based on the dynamic parameter features, and extract the trend-related features in the multi-dimensional parameter matrix;

[0141] The index calculation module 202 is configured to perform 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, obtain a fault location source point, identify the 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;

[0142] The area determination module 203 is configured to delimit a 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 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 a key maintenance area corresponding to the pressure transmitter based on the response performance curve;

[0143] The state index module 204 is configured to query the component aging data in the key maintenance area, analyze the key failure modes corresponding to the key maintenance area according to the component aging data, extract the maintenance monitoring points in the key failure modes, and calculate a health state index corresponding to the key maintenance area based on the maintenance monitoring points;

[0144] The report generation module 205 is configured to generate an evaluation index set corresponding to the pressure transmitter based on the health state index, query the index degradation trend corresponding to the evaluation index set, identify the multi-factor coupling weight corresponding to the index 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.

[0145] Specifically, each module in the computer-aided pressure transmitter fault diagnosis system 200 in the embodiments of the present invention uses the same technical means as those in the Figure 1 computer-aided pressure transmitter fault diagnosis method described above and can produce the same technical effects, which will not be elaborated here.

[0146] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions 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: Indicates The actual measured value corresponding to each fault factor is 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 sorting 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, represents the health status index corresponding to the key maintenance area, J represents 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.

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