Automatic instrument fault prediction system and method based on big data analysis

Abstract

The invention discloses an automatic instrument fault prediction system and method based on big data analysis, and belongs to the technical field of fault detection. The system comprises the following modules: an intelligent data processing and normalizing module which collects multi-source heterogeneous data of an instrument and an environment sensor in real time and performs data cleaning, standardization and quality evaluation; the working condition environment characteristic analysis module is used for identifying the current working condition state and quantitatively evaluating the influence degree of environmental factors on instrument operation; the multi-monitoring-parameter coupling analysis module is used for calculating and analyzing the mutual influence relationship among the monitoring parameters of the instrument and evaluating the coupling strength and influence links among the monitoring parameters in real time; the dynamic threshold calculation module is used for dynamically calculating and adjusting an early warning threshold system of each monitoring parameter; the fault prediction decision module is used for comprehensively evaluating various monitoring parameters and calculating a fault risk probability; and the early warning output and feedback module optimizes early warning output through an intelligent filtering mechanism, and collects early warning effect feedback for continuous optimization.

Description

Technical Field

[0001] This application relates to the technical field of fault detection, and more specifically, to an automated instrument fault prediction system and method based on big data analysis. Background Art

[0002] With the continuous improvement of industrial automation, automated instruments have become key equipment in modern industrial production processes. These instruments are widely used in various production systems to monitor and control various parameters in the production process in real time, such as temperature, pressure, flow rate, liquid level, etc. The normal operation of the instruments is directly related to production safety, product quality, and energy efficiency. However, during long-term operation, automated instruments may malfunction due to environmental changes, aging, wear, etc. Traditional fault diagnosis methods often rely on manual detection or simple rule-based judgments, and cannot accurately identify early signs of faults in real time, resulting in lagged fault detection and causing serious consequences such as production downtime, equipment damage, and resource waste.

[0003] To solve this problem, in recent years, fault prediction technologies based on big data and artificial intelligence have received extensive attention. By collecting and analyzing a large amount of instrument operation data in real time and using data mining and machine learning technologies, early prediction of instrument faults can be achieved, potential risks can be detected in advance, and corresponding preventive measures can be taken, thereby improving the reliability and operation efficiency of the equipment. Currently, many fault prediction systems rely on a single monitoring parameter or use static thresholds for early warning. These methods are difficult to cope with changing working conditions and complex environmental factors, and it is difficult to comprehensively consider the coupling relationship between different monitoring parameters, resulting in low prediction accuracy.

[0004] Therefore, how to use big data analysis technology to capture the fault precursors of instruments in real time and accurately, combine the coupling relationship between multi-source heterogeneous data, environmental factors, and monitoring parameters, dynamically calculate the early warning threshold, and perform accurate fault prediction through intelligent algorithms has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To overcome a series of defects existing in the prior art, the purpose of this application is to provide an automated instrument fault prediction system based on big data analysis for the above problems, including the following modules:

[0006] An intelligent data processing and standardization module that collects multi-source heterogeneous data from instrument and environmental sensors in real time and performs data cleaning, standardization, and quality assessment through a hierarchical architecture;

[0007] A working condition and environment feature analysis module that constructs a unified working condition - environment joint feature space, identifies the current working condition state, and quantitatively evaluates the influence degree of environmental factors on instrument operation;

[0008] Multi - monitoring parameter coupling analysis module, which calculates and analyzes the mutual influence relationships among various monitoring parameters of the instrument, and evaluates the coupling strength and influence link between monitoring parameters in real time;

[0009] Dynamic threshold calculation module, which dynamically calculates and adjusts the early - warning threshold system of each monitoring parameter by using an adaptive weight allocation mechanism;

[0010] Fault prediction and decision - making module, which comprehensively evaluates each monitoring parameter, calculates the fault risk probability, and generates an interpretable prediction conclusion;

[0011] Early - warning output and feedback module, which generates hierarchical early - warning information, optimizes the early - warning output through an intelligent filtering mechanism, and at the same time collects early - warning effect feedback for continuous optimization.

[0012] Furthermore, the intelligent data processing and normalization module includes a data acquisition unit, a data cleaning unit, a data standardization unit, a data quality assessment unit, a time - series data construction unit, and a data storage unit. Among them, the data acquisition unit is responsible for real - time acquisition of multi - source heterogeneous data from the instrument and environmental sensors; the data cleaning unit is used for preliminary processing of the collected raw data; the data standardization unit is used to convert the cleaned data into a unified format and unit; the data quality assessment unit is used for quality assessment of the standardized data; the time - series data construction unit is used to organize the standardized and quality - assessed data in time series to construct a unified time - series data format; the data storage unit is used to store the processed high - quality data into a database or data warehouse.

[0013] Furthermore, the working condition and environment feature analysis module includes a working condition feature extraction unit, an environment feature extraction unit, a joint feature space construction unit, a dimensionality reduction analysis unit, and an influence assessment unit. Among them, the working condition feature extraction unit is responsible for extracting key working condition features from multi - source heterogeneous data; the environment feature extraction unit is used to extract feature data related to environmental factors; the joint feature space construction unit is responsible for integrating the working condition features and environmental features into a unified joint feature space, and at the same time calculating the initial correlation between features; the dimensionality reduction analysis unit is used to reduce the dimension of the feature space, highlight key features and reduce the computational complexity; the classification model recognition unit is used to judge whether the equipment is in a normal working state or an abnormal state; the influence assessment unit is used to quantitatively evaluate the influence degree of environmental factors on the operation of the instrument and identify the potential influence of environmental variables on the equipment performance or operation state.

[0014] Further, the feature dimension extracted by the joint feature space construction unit is 10 - 30; the dimensionality reduction analysis unit uses the principal component analysis method to reduce the feature dimension to 10 - 20, and the cumulative variance contribution rate is not less than 85%; the impact evaluation unit uses the 0 - 1 standardization method to quantify the impact degree of environmental factors and makes dynamic adjustments according to the actual application scenario.

[0015] Further, the multi - monitoring - parameter coupling analysis module includes a monitoring - parameter correlation calculation unit, an impact propagation modeling unit, a key monitoring - parameter identification unit, a coupling strength evaluation unit, and an impact link analysis unit. Among them, the monitoring - parameter correlation calculation unit is responsible for calculating the correlation between the monitoring parameters of the instrument to quantify the mutual influence relationship between the monitoring parameters; the impact propagation modeling unit is responsible for revealing the complex coupling relationship between the monitoring parameters; the key monitoring - parameter identification unit is used to identify the key monitoring parameters that have the greatest impact on the equipment operation and locate the monitoring indicators that need to be focused on; the coupling strength evaluation unit quantifies the impact degree between different monitoring parameters by evaluating the coupling strength between the monitoring parameters in real - time; the impact link analysis unit is used to analyze the key links in the monitoring - parameter impact propagation map, identify the main impact paths between the monitoring parameters, and clarify the action mechanism of the monitoring - parameter coupling.

[0016] Further, the coupling strength evaluation unit calculates the coupling strength between the monitoring parameters in real - time. The coupling strength threshold is set as follows: strong coupling is greater than 0.7, medium coupling is 0.4 - 0.7, weak coupling is 0.2 - 0.4, and very weak coupling is less than 0.2. And the minimum coupling strength threshold is set to 0.1 for noise filtering; the impact link analysis unit sets the maximum depth of impact propagation to 4 layers, the number of nodes per layer does not exceed 15, and it supports a dynamic node - number adjustment mechanism based on the working conditions.

[0017] Further, the dynamic threshold calculation module includes a weight assignment unit, a threshold calculation unit, a multi - level threshold construction unit, and a threshold optimization unit. Among them, the weight assignment unit is responsible for dynamically adjusting the weights of the monitoring parameters; the threshold calculation unit dynamically calculates the warning thresholds of the monitoring parameters to adapt to the changes under different working conditions and environmental conditions; the multi - level threshold construction unit establishes a multi - level warning threshold system to meet the warning requirements of different scenarios; the smoothing transition unit is used to avoid false alarms or missed alarms caused by threshold mutations; the threshold optimization unit is used to dynamically optimize the threshold calculation results to improve accuracy and adaptability.

[0018] Furthermore, the fault prediction and decision-making module includes a monitoring parameter comprehensive evaluation unit, a historical fault matching unit, a risk probability calculation unit, a prediction conclusion generation unit, and a decision-making recommendation unit, where: the monitoring parameter comprehensive evaluation unit is used to comprehensively evaluate various monitoring parameters and quantify the abnormality degree and deviation of the monitoring parameters; the historical fault matching unit is responsible for matching the similarity between the current monitoring parameter abnormality and historical faults and identifying potential fault types; the risk probability calculation unit is used to calculate the fault risk probability under the current monitoring parameter abnormality; the prediction conclusion generation unit is used to generate an interpretable fault prediction conclusion, clarify the fault type, risk level, and potential impact; the decision-making recommendation unit is responsible for providing targeted operation and maintenance decision-making recommendations to support fault prevention and handling.

[0019] Furthermore, the early warning output and feedback module includes an early warning information generation unit, an early warning filtering unit, an early warning distribution unit, a feedback collection unit, and an effect evaluation unit, where: the early warning information generation unit is responsible for generating early warning information of corresponding levels, setting the minimum early warning time interval to 3 minutes, and filtering instantaneous early warnings with a duration of less than 30 seconds; the early warning filtering unit is responsible for optimizing the output of early warning information to reduce false alarms and redundant information; the early warning distribution unit is used to real-time publish the optimized graded early warning information through multiple channels; the feedback collection unit is responsible for collecting the responses and processing feedback of users to the early warning information, and recording the early warning effect and actual fault conditions; the effect evaluation unit is used to evaluate the accuracy and effectiveness of the early warning information, identify the deficiencies and improvement spaces of the early warning mechanism, and the assessment criteria for the early warning accuracy rate and missed alarm rate are: the early warning accuracy rate for critical faults is not less than 95% and the missed alarm rate is less than 0.1%, the early warning accuracy rate for major faults is not less than 90% and the missed alarm rate is less than 0.5%, the early warning accuracy rate for general faults is not less than 85% and the missed alarm rate is less than 1%, the early warning accuracy rate for minor faults is not less than 80% and the missed alarm rate is less than 2%; the maximum response time delay from the occurrence of an abnormality to the early warning output does not exceed 5 seconds.

[0020] The purpose of this application also lies in providing an automated instrument fault prediction method based on big data analysis, including the following steps:

[0021] Adopt a distributed network architecture to collect instrument operation data and environmental sensing data in real time and perform preprocessing to ensure the high quality and accuracy of the data;

[0022] Accurately identify the working conditions of the instrument, analyze the influence of environmental variables on the instrument performance through a multi-layer perceptron network, and construct an environmental factor influence scoring system;

[0023] Use the attention mechanism to identify key monitoring parameters, and combine dynamic time-varying Granger causality test to evaluate the coupling relationship between parameters;

[0024] Dynamically calculate the warning threshold based on the working condition characteristics and environmental impact factors, and use the fuzzy evaluation method to achieve the smooth transition of the threshold;

[0025] Extract fault features through the residual network, accurately identify the fault type by combining the attention mechanism, and calculate the probability of the fault occurrence using the Bayesian network;

[0026] Optimize the warning rules through reinforcement learning, establish a quantitative evaluation system for the prediction effect, and design a feedback closed-loop mechanism for continuous optimization.

[0027] Compared with the prior art, the present application has the following beneficial effects:

[0028] The present application collects and processes multi-source data in real time, analyzes the coupling relationship between the working conditions, environmental characteristics, and monitoring parameters, dynamically adjusts the warning threshold, and finally realizes the prediction and hierarchical warning of the fault risk. At the same time, combined with the feedback mechanism, the prediction effect is continuously optimized, thereby greatly improving the accuracy and reliability of the fault prediction. Description of the Drawings

[0029] Figure 1 It is a schematic structural diagram of an automatic instrument fault prediction system based on big data analysis disclosed in an embodiment of the present application.

[0030] Figure 2 It is a schematic flow diagram of an automatic instrument fault prediction method based on big data analysis disclosed in an embodiment of the present application. Detailed Embodiments

[0031] To make the purpose, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0032] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary only and are intended to explain the present invention and should not be construed as limiting the present invention.

[0034] As Figure 1 shown, an automatic instrument fault prediction system based on big data analysis includes the following modules:

[0035] Intelligent data processing and normalization module, which collects multi-source heterogeneous data from instruments and environmental sensors in real time, and performs data cleaning, standardization, and quality assessment through a hierarchical architecture;

[0036] Operating condition environment feature analysis module, which constructs a unified operating condition - environment joint feature space, identifies the current operating condition state, and quantitatively evaluates the influence degree of environmental factors on the operation of the instrument;

[0037] Multi-monitoring parameter coupling analysis module, which calculates and analyzes the mutual influence relationship between various monitoring parameters of the instrument, and evaluates the coupling strength and influence link between monitoring parameters in real time;

[0038] Dynamic threshold calculation module, which dynamically calculates and adjusts the early warning threshold system of each monitoring parameter by using an adaptive weight allocation mechanism;

[0039] Fault prediction decision module, which comprehensively evaluates each monitoring parameter, calculates the fault risk probability, and generates an interpretable prediction conclusion;

[0040] Early warning output and feedback module, which generates hierarchical early warning information, optimizes the early warning output through an intelligent filtering mechanism, and simultaneously collects early warning effect feedback for continuous optimization.

[0041] In this embodiment, by integrating modules such as intelligent data processing, operating condition environment analysis, multi-parameter coupling analysis, dynamic threshold calculation, fault prediction, and early warning output, a complete closed-loop solution for the fault prediction of automation instruments is constructed. Its innovations are reflected in the following aspects: by collecting and processing multi-source heterogeneous data in real time, the accuracy and consistency of the data are significantly improved; by establishing an operating condition - environment joint feature space and a dynamic threshold system, the adaptability to complex operating conditions and changing environments is improved; through the dynamic analysis of parameter coupling relationships and risk assessment, accurate prediction of fault trends is achieved; through a hierarchical early warning and feedback optimization mechanism, the false alarm rate is reduced, and the reliability and practicability of the system are enhanced. Overall, the system effectively improves the health management level of instrument equipment, provides users with timely, efficient, and accurate fault prediction and decision-making support, helps reduce operation and maintenance costs, and ensures production safety.

[0042] Furthermore, the intelligent data processing and normalization module includes the following components:

[0043] Data acquisition unit, which is responsible for collecting multi-source heterogeneous data from instruments and environmental sensors in real time to ensure the comprehensiveness and real-time nature of the data;

[0044] Data cleaning unit, which performs preliminary processing on the collected raw data, removes noise, outliers, and duplicate data, and improves the accuracy of the data;

[0045] A data standardization unit that converts the cleaned data into a unified format and unit to ensure the consistency and comparability of the data;

[0046] A data quality assessment unit that assesses the quality of the standardized data, identifies and marks potential data quality problems to ensure the reliability of the data;

[0047] A time-series data construction unit that organizes the standardized and quality-assessed data in a time series, constructs a unified time-series data format for subsequent analysis and processing;

[0048] A data storage unit that stores the processed high-quality data in a database or data warehouse to ensure the security and accessibility of the data.

[0049] In this embodiment, the implementation of the intelligent data processing and normalization module significantly improves the processing ability of complex and multi-source heterogeneous data, ensuring the integrity, accuracy, and consistency of the data. Through the real-time acquisition function of the data acquisition unit, comprehensive coverage and timely update of instrument and environmental sensor data are ensured; the data cleaning unit effectively filters out noise and abnormal data, significantly reducing the interference of data errors on subsequent analysis; the data standardization unit lays a foundation for multi-dimensional data fusion across devices and environments through the conversion of unified formats and units; the data quality assessment unit accurately identifies potential quality problems, improves the reliability of the data, and provides reliable input for subsequent analysis; the time-series data construction unit structures high-quality data into a time series, facilitating feature extraction and trend analysis for subsequent modules; and the data storage unit provides guarantees for the long-term availability and traceability of the data through a secure and reliable storage mechanism.

[0050] Furthermore, the working condition environment feature analysis module includes the following components:

[0051] A working condition feature extraction unit that extracts key working condition features from multi-source heterogeneous data to describe the current working condition;

[0052] An environmental feature extraction unit that extracts feature data related to environmental factors to build a joint analysis basis for environmental features and working condition features;

[0053] A joint feature space construction unit that integrates working condition features and environmental features into a unified joint feature space and calculates the initial correlation between features simultaneously;

[0054] A dimensionality reduction analysis unit that reduces the dimensionality of the feature space through the principal component analysis algorithm, highlighting key features and reducing computational complexity;

[0055] A classification model identification unit that, based on the dimensionality-reduced data, applies a classification algorithm to identify the current working condition and determine whether the device is in a normal working state or an abnormal state.

[0056] The impact assessment unit quantifies the impact degree of environmental factors on the instrument operation, identifies the potential impact of environmental variables on the equipment performance or operating status, and assists in optimizing the operating conditions.

[0057] In this embodiment, the implementation of the working condition and environment characteristic analysis module effectively improves the adaptability to complex working conditions and variable environments and the accurate perception level. The collaborative work of the working condition feature extraction unit and the environment feature extraction unit ensures the comprehensive capture and accurate description of the key operating status and environmental factors; the joint feature space construction unit integrates the working condition and environment features, provides a unified analysis framework, and at the same time enhances the interpretability of the relationship between features; the dimensionality reduction analysis unit reduces the feature dimension and redundancy through algorithms such as principal component analysis, not only significantly improves the calculation efficiency, but also highlights the importance of the core features; the classification model recognition unit quickly and accurately identifies the equipment status based on the dimensionality-reduced feature data, providing strong support for fault prediction; the impact assessment unit deeply reveals the potential coupling relationship between environmental variables and equipment performance by quantitatively analyzing the impact of environmental factors on equipment operation, providing a guiding basis for operation parameter optimization and environmental regulation. Overall, through the complete process from data extraction to status recognition, this module realizes the comprehensive analysis of working conditions and environments, helps to improve the accuracy and reliability of fault prediction, and at the same time provides important decision-making support for operation optimization.

[0058] Specifically, the feature dimension extracted by the joint feature space construction unit is 10 - 30; the dimensionality reduction analysis unit uses the principal component analysis method to reduce the feature dimension to 10 - 20, and the cumulative variance contribution rate is not less than 85%; the impact assessment unit uses the 0 - 1 standardization method to quantify the impact degree of environmental factors and makes dynamic adjustments according to the actual application scenario.

[0059] In this embodiment, the joint feature space construction unit comprehensively covers the multi-dimensional information of working conditions and environments by extracting 10 - 30 key features, laying a solid foundation for in-depth analysis; the dimensionality reduction analysis unit reasonably reduces the feature dimension to 10 - 20 through principal component analysis and ensures that the cumulative variance contribution rate is not less than 85%, effectively reducing the calculation burden while maximizing the representativeness and relevance of feature information; the impact assessment unit uses the 0 - 1 standardization method to quantify the impact degree of environmental factors on equipment operation and makes dynamic adjustments in combination with the actual application scenario, realizing the organic combination of environmental adaptability and system flexibility.

[0060] Furthermore, the multi-monitoring parameter coupling analysis module includes the following components:

[0061] The monitoring parameter correlation calculation unit calculates the correlation between the monitoring parameters of the instrument based on the working condition state, and quantifies the mutual influence relationship between the monitoring parameters;

[0062] The influence propagation modeling unit constructs an influence propagation map of the monitoring parameters, describes the influence path and propagation law between the monitoring parameters, and reveals the complex coupling relationship between the monitoring parameters;

[0063] The key monitoring parameter identification unit identifies the key monitoring parameters that have the greatest impact on the equipment operation through graph analysis algorithms, and locates the monitoring indicators that need to be focused on;

[0064] The coupling strength evaluation unit evaluates the coupling strength between the monitoring parameters in real time, and quantifies the influence degree between different monitoring parameters;

[0065] The influence link analysis unit analyzes the key links in the influence propagation map of the monitoring parameters, identifies the main influence paths between the monitoring parameters, and clarifies the action mechanism of the monitoring parameter coupling.

[0066] In this embodiment, the implementation of the multi-monitoring parameter coupling analysis module significantly enhances the understanding and modeling ability of the complex parameter relationships in the instrument operation state. The monitoring parameter correlation calculation unit comprehensively reveals the mutual influence between the parameters by quantifying the correlation between the monitoring parameters, laying a foundation for subsequent in-depth analysis; the influence propagation map constructed by the influence propagation modeling unit intuitively describes the complex coupling relationship between the monitoring parameters, providing a reliable basis for identifying key paths and laws; the key monitoring parameter identification unit accurately locates the key parameters that have the greatest impact on the equipment operation through graph analysis algorithms, helping to optimize the monitoring resource allocation and improve the system monitoring efficiency; the coupling strength evaluation unit realizes the real-time quantification of the influence degree between the parameters, enhancing the perception ability of dynamic changes; the influence link analysis unit further clarifies the action mechanism between the parameters by analyzing the key influence links, providing a scientific basis for predicting the fault mode and optimizing the operation strategy. Generally speaking, this module provides in-depth support for fault prediction and operation optimization through comprehensive parameter correlation analysis and accurate key point identification.

[0067] Specifically, the coupling strength between the monitoring parameters is evaluated in real time through the following formula: where C xy∣W is the coupling strength between the monitoring parameters x and y under the specific working condition W; and are the monitoring values of the monitoring parameters x and y under the working condition W respectively; and are the predicted values of the monitoring parameters x and y under the working condition W respectively.

[0068] Specifically, the coupling strength evaluation unit calculates the coupling strength between monitoring parameters in real time. The coupling strength threshold is set as follows: strong coupling is greater than 0.7, medium coupling is 0.4 - 0.7, weak coupling is 0.2 - 0.4, and very weak coupling is less than 0.2. A minimum coupling strength threshold of 0.1 is set for noise filtering. The influence link analysis unit sets the maximum depth of influence propagation to 4 layers, with the number of nodes per layer not exceeding 15, and supports a dynamic node number adjustment mechanism based on working conditions.

[0069] In this embodiment, the setting of the coupling strength threshold enables the clear classification of the coupling relationships between monitoring parameters: strong coupling, greater than 0.7; medium coupling, 0.4 - 0.7; weak coupling, 0.2 - 0.4; very weak coupling, less than 0.2. At the same time, a minimum coupling strength threshold of 0.1 is set, effectively filtering noise and ensuring the accuracy and reliability of data analysis. By calculating the coupling strength in real time, changes between parameters can be quickly identified and responded to, providing an accurate basis for fault prediction and equipment status evaluation. The maximum depth of the influence link analysis unit is set to 4 layers, with the number of nodes per layer not exceeding 15, ensuring a balance between the depth of the analysis process and the calculation efficiency. In addition, supporting a dynamic node number adjustment mechanism based on working conditions further improves the adaptability and analysis accuracy of the system under different working conditions. Overall, the application of the above measures not only optimizes the accuracy and flexibility of coupling analysis but also improves the real-time response ability and scalability of the system in practical applications.

[0070] Furthermore, the dynamic threshold calculation module includes the following components:

[0071] The weight assignment unit adopts an adaptive weight assignment mechanism to dynamically adjust the weights of each monitoring parameter according to working conditions and environmental factors.

[0072] The threshold calculation unit dynamically calculates the warning thresholds of each monitoring parameter based on the weight assignment results to adapt to changes under different working conditions and environmental conditions.

[0073] The multi-level threshold construction unit establishes a multi-level warning threshold system according to the importance and coupling relationships of monitoring parameters to meet the warning requirements of different scenarios.

[0074] The smooth transition unit realizes the smooth transition of thresholds under different working conditions and environments through time series analysis, avoiding false alarms or missed alarms caused by threshold mutations.

[0075] The threshold optimization unit dynamically optimizes the threshold calculation results according to historical data and real-time feedback to improve accuracy and adaptability.

[0076] In this embodiment, the implementation of the dynamic threshold calculation module significantly improves the adaptability and accuracy of the system under complex working conditions and environments. The weight allocation unit, through an adaptive mechanism, dynamically adjusts the weights of various monitoring parameters according to changes in working conditions and environmental factors, ensuring that the priorities of monitoring parameters under different conditions are reasonably allocated; the threshold calculation unit dynamically calculates the warning thresholds of various monitoring parameters based on the weights adjusted in real time, enabling the system to accurately adapt to changes in working conditions and environments and enhancing the flexibility of the warning system; the multi-level threshold construction unit establishes a multi-level warning system that meets different application scenarios according to the importance and coupling relationship of monitoring parameters, making the warning mechanism more comprehensive and refined; the smooth transition unit uses time series analysis to effectively avoid sudden changes in thresholds during changes in working conditions, thereby reducing the occurrence of false alarms and missed alarms and improving the stability of warnings; the threshold optimization unit combines historical data with real-time feedback to dynamically adjust the threshold calculation results and continuously improve the accuracy and adaptability of the warning mechanism. Overall, through a multi-level and multi-dimensional dynamic threshold adjustment mechanism, this module significantly enhances the system's response ability to changing environments and working conditions, ensuring the accuracy and timeliness of fault warnings.

[0077] Specifically, the warning thresholds of various monitoring parameters are dynamically calculated through the following formula: Among them, θ j (t) is the dynamic warning threshold of the jth monitoring parameter at time t; p j is the standard reference threshold of the jth monitoring parameter; m is the number of influencing factors; w jk (t) is the weight coefficient between the jth monitoring parameter and the kth environmental / working condition factor, indicating the degree of influence of this environmental factor on the threshold of the jth monitoring parameter at time t; C k (t) is the change amount of the kth environmental / working condition factor at time t, indicating the influence of environmental or working condition factors on monitoring parameters at a specific time.

[0078] Furthermore, the fault prediction and decision-making module includes the following components:

[0079] The monitoring parameter comprehensive evaluation unit comprehensively evaluates each monitoring parameter based on the dynamic threshold, quantifying the abnormality degree and deviation of the monitoring parameters;

[0080] The historical fault matching unit combines the historical fault mode library to match the similarity between the current monitoring parameter abnormality and historical faults, identifying potential fault types;

[0081] The risk probability calculation unit calculates the fault risk probability under the current monitoring parameter abnormality, quantifying the possibility of fault occurrence;

[0082] The prediction conclusion generation unit generates an interpretable fault prediction conclusion based on the monitoring parameter evaluation result and the risk probability, clarifying the fault type, risk level, and potential impact;

[0083] The decision-making suggestion unit provides targeted operation and maintenance decision-making suggestions based on the prediction conclusion to support fault prevention and handling.

[0084] In this embodiment, the implementation of the fault prediction decision module further improves the system's prediction ability for equipment faults and the decision support effect. The monitoring parameter comprehensive evaluation unit comprehensively evaluates each monitoring parameter based on the dynamic threshold, quantifies the degree of abnormality and deviation, and ensures accurate monitoring of the equipment operation status; the historical fault matching unit can quickly identify the similarity between the current monitoring parameter abnormality and the historical fault mode by matching with the historical fault mode library, thereby helping to judge the potential fault type and providing a historical basis for fault prediction; the risk probability calculation unit quantifies the probability of fault occurrence by analyzing the current abnormal state, providing a risk assessment basis for early warning decision-making; the prediction conclusion generation unit generates an interpretable fault prediction conclusion based on the comprehensive evaluation and risk probability, clearly indicating the fault type, risk level, and potential impact, providing a scientific basis for subsequent operation and maintenance decision-making; the decision-making suggestion unit combines the prediction conclusion to provide targeted operation and maintenance decision-making suggestions, helping operation and maintenance personnel take timely measures for fault prevention and handling, and minimizing the impact of faults on production. Overall, this module realizes the closed-loop management from monitoring data to fault prediction, effectively improves the early warning ability of equipment faults, and enhances the safety and stability of equipment operation.

[0085] Furthermore, the early warning output and feedback module includes the following components:

[0086] The early warning information generation unit generates early warning information of corresponding levels according to the threshold classification system provided by the dynamic threshold calculation module in combination with the fault prediction result;

[0087] The early warning filtering unit optimizes the output of early warning information through an intelligent filtering mechanism, reduces false alarms and redundant information, and improves the accuracy of early warning;

[0088] The early warning distribution unit real-time publishes the optimized classified early warning information through multiple channels to ensure the timeliness and accessibility of early warning;

[0089] The feedback collection unit collects the responses and handling feedback of users to the early warning information, and records the early warning effect and actual fault situation;

[0090] The effect evaluation unit evaluates the accuracy and effectiveness of the early warning information based on the feedback data, and identifies the deficiencies and improvement spaces of the early warning mechanism.

[0091] Specifically, the warning filtering unit sets the minimum warning time interval to 3 minutes and filters out instantaneous warnings with a duration less than 30 seconds. The evaluation criteria for the warning accuracy rate and the false alarm rate by the effect evaluation unit are as follows: the warning accuracy rate for fatal failures is not less than 95% and the false alarm rate is less than 0.1%; the warning accuracy rate for major failures is not less than 90% and the false alarm rate is less than 0.5%; the warning accuracy rate for general failures is not less than 85% and the false alarm rate is less than 1%; the warning accuracy rate for minor failures is not less than 80% and the false alarm rate is less than 2%. The maximum response time delay from the occurrence of an anomaly to the warning output does not exceed 5 seconds.

[0092] As Figure 2 shown, an automated instrument fault prediction method based on big data analysis includes the following steps:

[0093] Adopt a distributed network architecture to collect instrument operation data and environmental sensing data in real time and perform preprocessing to ensure the high quality and accuracy of the data;

[0094] Precisely identify the working condition modes of the instrument, analyze the influence of environmental variables on the instrument performance through a multi-layer perceptron network, and construct an environmental factor influence scoring system;

[0095] Use the attention mechanism to identify key monitoring parameters and evaluate the coupling relationship between parameters by combining dynamic time-varying Granger causality test;

[0096] Based on the working condition characteristics and environmental influence factors, dynamically calculate the warning threshold and use the fuzzy evaluation method to achieve the smooth transition of the threshold;

[0097] Extract fault features through a residual network, accurately identify the fault type by combining the attention mechanism, and calculate the probability of fault occurrence using a Bayesian network;

[0098] Optimize the warning rules through reinforcement learning, establish a quantitative evaluation system for prediction effects, and design a feedback closed-loop mechanism for continuous optimization.

[0099] In this embodiment, high-precision and high self-adaptability of fault prediction are achieved through multi-level technology integration. First, the application of a distributed network architecture ensures real-time data collection and high-quality preprocessing, providing an accurate and reliable basis for subsequent analysis. Through an accurate working condition pattern recognition and environmental impact scoring system, the impact of working conditions and environmental changes on instrument performance can be comprehensively considered, improving the comprehensiveness and accuracy of prediction. The attention mechanism and time-varying Granger causality test are used to accurately model the coupling relationship between monitoring parameters, further enhancing the perception ability of complex parameter interactions. In addition, the dynamically calculated early warning threshold combined with the fuzzy evaluation method realizes a smooth transition, reducing the risks of false alarms and missed alarms. Through the combination of the residual network and the Bayesian network, not only can fault features be efficiently extracted, but also the probability of fault occurrence can be quantified, providing strong support for risk assessment. The application of reinforcement learning optimizes the early warning rules, establishes a quantitative evaluation system for prediction effects, and continuously improves the accuracy and reliability of prediction through a feedback closed-loop mechanism. Overall, this method greatly improves the intelligent level of instrument fault prediction and effectively supports the health management and fault prevention of equipment.

[0100] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automated instrument fault prediction system based on big data analysis, characterized in that: Includes the following modules: Intelligent data processing and normalization module, which collects multi-source heterogeneous data from instruments and environmental sensors in real time, and performs data cleaning, standardization and quality assessment through a layered architecture; The working condition and environment feature analysis module builds a unified working condition-environment joint feature space, identifies the current working condition status and quantitatively evaluates the impact of environmental factors on instrument operation; The multi-monitoring parameter coupling analysis module calculates and analyzes the mutual influence relationship between the various monitoring parameters of the instrument, and evaluates the coupling strength and influence link between the monitoring parameters in real time; Dynamic threshold calculation module, which uses adaptive weight allocation mechanism to dynamically calculate and adjust the early warning threshold system of each monitoring parameter; The fault prediction decision module comprehensively evaluates various monitoring parameters, calculates the probability of fault risk, and generates explainable prediction conclusions; The warning output and feedback module generates graded warning information, optimizes warning output through intelligent filtering mechanism, and collects warning effect feedback for continuous optimization.

2. The automatic instrument fault prediction system based on big data analysis according to claim 1 is characterized in that: The intelligent data processing and normalization module includes a data acquisition unit, a data cleaning unit, a data standardization unit, a data quality assessment unit, a time series data construction unit and a data storage unit, wherein the data acquisition unit is responsible for real-time acquisition of multi-source heterogeneous data from instruments and environmental sensors; the data cleaning unit is used to perform preliminary processing on the collected raw data; the data standardization unit is used to convert the cleaned data into a unified format and unit; the data quality assessment unit is used to perform quality assessment on the standardized data; the time series data construction unit is used to organize the standardized and quality-assessed data in a time series and construct a unified time series data format; the data storage unit is used to store the processed high-quality data in a database or data warehouse.

3. The automatic instrument fault prediction system based on big data analysis according to claim 1 is characterized in that: The operating condition and environmental feature analysis module includes an operating condition feature extraction unit, an environmental feature extraction unit, a joint feature space construction unit, a dimensionality reduction analysis unit and an impact assessment unit, wherein the operating condition feature extraction unit is responsible for extracting key operating condition features from multi-source heterogeneous data; the environmental feature extraction unit is used to extract feature data related to environmental factors; the joint feature space construction unit is responsible for integrating the operating condition features and the environmental features into a unified joint feature space, and at the same time calculating the initial correlation between the features; the dimensionality reduction analysis unit is used to reduce the dimension of the feature space, highlight key features and reduce calculation complexity; the classification model identification unit is used to determine whether the equipment is in a normal working state or an abnormal state; the impact assessment unit is used to quantitatively evaluate the impact of environmental factors on instrument operation and identify the potential impact of environmental variables on equipment performance or operating status.

4. The automatic instrument fault prediction system based on big data analysis according to claim 1 is characterized in that: The multi-monitoring parameter coupling analysis module includes a monitoring parameter association calculation unit, an influence propagation modeling unit, a key monitoring parameter identification unit, a coupling strength evaluation unit and an influence link analysis unit, wherein the monitoring parameter association calculation unit is responsible for calculating the correlation between the various monitoring parameters of the instrument to quantify the mutual influence relationship between the monitoring parameters; the influence propagation modeling unit is responsible for revealing the complex coupling relationship between the monitoring parameters; the key monitoring parameter identification unit is used to identify the key monitoring parameters that have the greatest impact on the operation of the equipment and locate the monitoring indicators that need to be focused on; the coupling strength evaluation unit quantifies the degree of influence between different monitoring parameters by real-time evaluation of the coupling strength between the monitoring parameters; the influence link analysis unit is used to analyze the key links in the monitoring parameter influence propagation map, identify the main influence paths between the monitoring parameters, and clarify the action mechanism of the monitoring parameter coupling.

5. The automatic instrument fault prediction system based on big data analysis according to claim 1 is characterized in that: The dynamic threshold calculation module includes a weight allocation unit, a threshold calculation unit, a multi-level threshold construction unit and a threshold optimization unit, wherein the weight allocation unit is responsible for dynamically adjusting the weights of each monitoring parameter; the threshold calculation unit dynamically calculates the warning threshold of each monitoring parameter to adapt to changes under different working conditions and environmental conditions; the multi-level threshold construction unit meets the warning requirements of different scenarios by establishing a multi-level warning threshold system; the smooth transition unit is used to avoid false alarms or missed alarms caused by threshold mutations; the threshold optimization unit is used to dynamically optimize the threshold calculation results to improve accuracy and adaptability.

6. The automatic instrument fault prediction system based on big data analysis according to claim 1 is characterized in that: The fault prediction decision module includes a monitoring parameter comprehensive evaluation unit, a historical fault matching unit, a risk probability calculation unit, a prediction conclusion generation unit and a decision suggestion unit, wherein: the monitoring parameter comprehensive evaluation unit is used to perform a comprehensive evaluation on each monitoring parameter and quantify the abnormality and deviation of the monitoring parameters; the historical fault matching unit is responsible for matching the similarity between the current monitoring parameter abnormality and the historical fault and identifying the potential fault type; the risk probability calculation unit is used to calculate the fault risk probability under the current monitoring parameter abnormality; the prediction conclusion generation unit is used to generate an explainable fault prediction conclusion and clarify the fault type, risk level and potential impact; the decision suggestion unit is responsible for providing targeted operation and maintenance decision suggestions to support fault prevention and handling.

7. The automatic instrument fault prediction system based on big data analysis according to claim 1 is characterized in that: The warning output and feedback module includes a warning information generation unit, a warning filtering unit, a warning distribution unit, a feedback collection unit and an effect evaluation unit; the warning filtering unit is responsible for optimizing the warning information output and reducing false alarms and redundant information; the warning distribution unit is used to publish the optimized graded warning information in real time through multiple channels; the feedback collection unit is responsible for collecting users' responses and processing feedback on the warning information, and recording the warning effect and actual fault conditions; the effect evaluation unit is used to evaluate the accuracy and effectiveness of the warning information and identify the deficiencies and improvement space of the warning mechanism.

8. A method for predicting failure of an automated instrument based on big data analysis, implemented based on a system for predicting failure of an automated instrument based on big data analysis as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: Adopting a distributed network architecture, real-time collection of instrument operation data and environmental sensor data, and pre-processing to ensure high data quality and accuracy; Accurately identify the working mode of the instrument, analyze the impact of environmental variables on instrument performance through a multi-layer perceptron network, and build an environmental factor impact scoring system; The attention mechanism is used to identify key monitoring parameters, and the coupling relationship between parameters is evaluated by combining the dynamic time-varying Granger causality test; Based on the working condition characteristics and environmental influencing factors, the warning threshold is dynamically calculated, and the fuzzy evaluation method is used to achieve a smooth transition of the threshold; The residual network is used to extract fault features, and the attention mechanism is used to accurately identify the fault type, and the Bayesian network is used to calculate the probability of fault occurrence. Optimize early warning rules through reinforcement learning, establish a quantitative evaluation system for prediction effects, and design a feedback closed-loop mechanism for continuous optimization.

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