Petrochemical equipment fault prediction and safety management system and method thereof

Abstract

The invention discloses a petrochemical equipment fault prediction and safety management system and method, and belongs to petrochemical equipment management. The system comprises the following modules: a working condition sensing and classifying module for realizing real-time identification, classification and feature extraction of the equipment operation state; the health state evaluation module is used for calculating equipment health indexes and outputting an equipment performance degradation trend analysis result; the fault prediction and diagnosis module is used for predicting potential fault types and occurrence time, performing diagnosis analysis on fault mechanisms and providing credibility evaluation of diagnosis results; the risk assessment and early warning module comprehensively assesses multi-dimensional risk factors and generates graded early warning information through dynamic threshold optimization and a multi-stage early warning strategy; the maintenance decision support module is used for optimizing a maintenance strategy and providing resource scheduling and maintenance scheme suggestions based on the equipment health state, the fault prediction and the risk assessment result; and the management and optimization module ensures efficient cooperative operation of each module and continuously improves the system performance.

Description

Technical Field

[0001] This application relates to the management of petrochemical equipment, and more specifically, to a petrochemical equipment failure prediction and safety management system and method thereof. Background Technology

[0002] The petrochemical industry is a vital component of modern industry, and the safe operation of its equipment directly impacts production efficiency and environmental safety. However, due to their complex working environment and high-load operation, petrochemical equipment frequently faces the threat of various malfunctions. These malfunctions can not only lead to production interruptions but also potentially cause serious environmental pollution and safety accidents.

[0003] Traditional petrochemical equipment maintenance relies primarily on periodic inspections and experience-based judgment; however, this method has significant limitations. Periodic inspections may fail to detect potential faults in a timely manner, while experience-based judgment is often limited by the experience and expertise of the personnel, making it difficult to be comprehensive and accurate. With technological advancements, sensor network-based equipment monitoring systems have begun to be applied in the petrochemical field; however, simple data acquisition and monitoring are insufficient for fault prediction and diagnosis.

[0004] In recent years, with the development of artificial intelligence and big data technologies, the application of deep learning algorithms and knowledge graph technologies in fault prediction and diagnosis has gradually attracted attention. By integrating multiple advanced technologies, a comprehensive fault prediction and safety management system can be built, enabling real-time monitoring, health status assessment, and fault prediction of petrochemical equipment, significantly improving equipment operational safety and production efficiency.

[0005] In conclusion, establishing an efficient and reliable fault prediction and safety management system for the complex operating environment of petrochemical equipment has become an urgent technical problem to be solved. Summary of the Invention

[0006] In order to overcome a series of defects in the existing technology, the purpose of this application is to provide a petrochemical equipment fault prediction and safety management system, including the following modules:

[0007] The working condition perception and classification module monitors equipment operating parameters in real time, builds a dynamic working condition feature library, and realizes real-time identification, classification and feature extraction of equipment operating status;

[0008] The health status assessment module, based on a multi-parameter fusion and working condition adaptive weight adjustment mechanism, calculates the equipment health index and outputs the analysis results of equipment performance degradation trend.

[0009] The fault prediction and diagnosis module integrates deep learning algorithms and knowledge graph technology, combines historical fault case databases and real-time health status assessment data to predict potential fault types and occurrence times, perform diagnostic analysis on fault mechanisms, and provide a reliability assessment of diagnostic results.

[0010] The risk assessment and early warning module, based on the results of fault prediction and diagnosis, comprehensively assesses multi-dimensional risk factors, generates graded early warning information through dynamic threshold optimization and multi-level early warning strategies, and provides targeted emergency response suggestions.

[0011] The maintenance decision support module optimizes maintenance strategies and provides resource scheduling and repair plan suggestions based on equipment health status, fault prediction and risk assessment results, so as to achieve the optimal balance between maintenance costs and benefits.

[0012] The management and optimization module ensures efficient and collaborative operation of all modules and continuously improves system performance.

[0013] Furthermore, the condition perception and classification module includes the following components:

[0014] The real-time data acquisition unit is responsible for acquiring various operating parameters of the equipment in real time to ensure accurate acquisition of operating data.

[0015] The data preprocessing unit performs noise reduction, filtering, and normalization on the collected data.

[0016] The dynamic feature extraction unit extracts time-series features from the preprocessed data to form the equipment's operating condition feature vector.

[0017] The operating condition classification unit uses extracted features for training and classification to achieve real-time identification of different equipment operating conditions.

[0018] The operating condition feature library management unit builds and maintains a dynamically updated operating condition feature library, stores the historical operating condition features of devices, and provides comparison and reference for new operating condition classifications;

[0019] The working condition classification verification unit verifies the accuracy of the working condition classification based on the matching degree between the classification results and the feature library, and outputs a working condition classification credibility report.

[0020] Furthermore, the health status assessment module includes the following components:

[0021] The data fusion unit integrates multiple operating parameters, eliminates redundant information and noise, improves the accuracy and completeness of data, and provides more comprehensive input for health assessment.

[0022] The adaptive weight adjustment unit dynamically adjusts the weight of each parameter in the health assessment according to the operating conditions of the equipment, so as to adapt to the characteristics and performance of the equipment under different operating conditions.

[0023] The health index calculation unit calculates the health index of the equipment based on the fused data and adjusted weights, reflecting the current operational health status of the equipment.

[0024] The performance degradation trend analysis unit combines historical equipment data and health index to analyze the degradation trend of equipment performance.

[0025] The health status assessment report unit automatically generates a health status report for the device based on health indices and degradation trends.

[0026] Furthermore, the fault prediction and diagnosis module includes the following components:

[0027] The historical fault case library unit stores and manages historical fault data and related parameters of the equipment.

[0028] The fault prediction unit trains and infers from historical and real-time data to identify the type, location and development trend of potential faults and perform fault prediction.

[0029] The knowledge graph construction unit builds a knowledge graph based on the working principle of the equipment, failure cases and their interrelationships, in order to more accurately analyze the causes and influencing factors of failures;

[0030] The fault mechanism diagnosis unit performs mechanism analysis on the predicted fault types, determines the root cause of the fault, and provides a detailed explanation of the fault cause.

[0031] The fault prediction result analysis unit assesses the occurrence time, severity, and potential impact of potential faults based on the prediction results.

[0032] The diagnostic reliability assessment unit evaluates the reliability of fault diagnosis results. Through confidence quantification analysis, it ensures the reliability of fault prediction and diagnosis and provides a basis for decision-making.

[0033] Furthermore, the risk assessment and early warning module includes the following components:

[0034] The comprehensive risk assessment unit assesses equipment safety and performance risks based on fault prediction results, combined with equipment health status, environmental factors, and operating conditions, and generates a risk impact analysis report.

[0035] The dynamic threshold optimization unit dynamically adjusts the risk threshold based on real-time and historical data to ensure more accurate risk assessment under different operating conditions and equipment states, and to reflect potential risks in a timely manner.

[0036] The early warning level determination unit, based on the risk assessment results and the set thresholds, determines the severity of the current risk and classifies the risk into different early warning levels;

[0037] The multi-level early warning strategy generation unit generates multi-level early warning information and automatically formulates corresponding early warning strategies according to the risk level, ensuring a step-by-step response from early warning to emergency response.

[0038] The emergency response suggestion generation unit provides specific emergency response suggestions based on early warning information and risk levels, including operational guidance, equipment shutdown suggestions, and maintenance scheduling, to help personnel respond in a timely manner.

[0039] Furthermore, the maintenance decision support module includes the following components:

[0040] The maintenance requirements analysis unit analyzes the current maintenance requirements of the equipment based on its health status, fault prediction, and historical maintenance data, providing basic data for optimization decisions.

[0041] The maintenance strategy optimization unit optimizes maintenance strategies based on requirements analysis, combined with failure modes, risk assessment results, and maintenance costs, and selects the most suitable maintenance type and frequency.

[0042] The resource scheduling optimization unit rationally schedules maintenance personnel, equipment, and material resources based on the optimized maintenance strategy to ensure the efficient execution of maintenance activities and reduce resource waste.

[0043] The maintenance solution suggestion unit automatically generates targeted maintenance solutions, including specific operating steps, required time and tools, to ensure the smooth completion of maintenance tasks and minimize equipment downtime.

[0044] The cost-benefit assessment unit conducts cost-benefit analysis on different maintenance plans, comprehensively considering maintenance costs, downtime, and equipment life extension factors to provide the optimal balance for decision-making.

[0045] The priority sorting unit optimizes the priority of maintenance tasks based on the urgency of the equipment, the type of failure, and the maintenance cost, ensuring timely maintenance of critical equipment.

[0046] Furthermore, the management and optimization module includes the following components:

[0047] The system status monitoring unit is responsible for monitoring the overall system status, collecting and analyzing the output data of each module, ensuring that all system tasks are performed as expected, and promptly identifying potential problems.

[0048] The performance evaluation unit periodically evaluates the overall performance of the system, including computational efficiency, resource utilization, and task completion quality, and provides feedback information for performance optimization.

[0049] The parameter optimization unit manages and optimizes the operating parameters of all modules in the system in a unified manner.

[0050] The collaborative scheduling unit optimizes the collaborative work between modules. Through dynamic scheduling and task allocation mechanisms, it ensures the coordination of different modules and maximizes the overall efficiency of the system.

[0051] The fault detection and recovery unit monitors the health status of the system, detects communication anomalies or faults between modules, and automatically isolates and recovers from faults to ensure the continuous and stable operation of the system.

[0052] Methods for predicting and managing the failures of petrochemical equipment include the following steps:

[0053] Real-time data collection of equipment operation, such as temperature, pressure, vibration and noise parameters, is achieved through a distributed sensor network, and the data is preprocessed by adaptive filtering and normalization.

[0054] By using deep learning algorithms to extract time-series and frequency-domain features from the processed data, and comparing them in real time with a dynamically updated operating condition feature library, accurate classification of equipment operating status can be achieved.

[0055] By integrating equipment operating parameters, historical performance data, and environmental factors, a comprehensive health index is calculated, and the performance degradation trend of the equipment is predicted.

[0056] A fault prediction model is established by combining deep learning and knowledge graph technology to identify potential faults and compare them with an expert database for fault diagnosis.

[0057] Based on the equipment health status and fault prediction results, combined with the equipment importance and environmental impact factors, the risk level is calculated, and corresponding early warning and emergency response suggestions are generated.

[0058] By integrating health status, fault prediction, and risk assessment results, the system automatically generates the optimal maintenance strategy to ensure the long-term stable operation of the equipment.

[0059] Compared with the prior art, this application has the following beneficial effects:

[0060] This application comprehensively utilizes real-time monitoring, multi-parameter fusion, deep learning algorithms, and knowledge graph technology to achieve dynamic operating condition identification, health status assessment, potential fault prediction and diagnosis, multi-dimensional risk assessment and early warning of equipment, and provides optimized maintenance decision support solutions to ensure the safe and stable operation of equipment and maximize maintenance benefits. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the structure of the petrochemical equipment fault prediction and safety management system disclosed in the embodiments of this application.

[0062] Figure 2 This is a flowchart illustrating the petrochemical equipment failure prediction and safety management method disclosed in the embodiments of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some embodiments of this invention, but not all embodiments.

[0064] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0065] The embodiments and directional terms described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0066] like Figure 1 As shown, the petrochemical equipment fault prediction and safety management system includes the following modules:

[0067] The working condition perception and classification module monitors equipment operating parameters in real time, builds a dynamic working condition feature library, and realizes real-time identification, classification and feature extraction of equipment operating status;

[0068] The health status assessment module, based on a multi-parameter fusion and working condition adaptive weight adjustment mechanism, calculates the equipment health index and outputs the analysis results of equipment performance degradation trend.

[0069] The fault prediction and diagnosis module integrates deep learning algorithms and knowledge graph technology, combines historical fault case databases and real-time health status assessment data to predict potential fault types and occurrence times, perform diagnostic analysis on fault mechanisms, and provide a reliability assessment of diagnostic results.

[0070] The risk assessment and early warning module, based on the results of fault prediction and diagnosis, comprehensively assesses multi-dimensional risk factors, generates graded early warning information through dynamic threshold optimization and multi-level early warning strategies, and provides targeted emergency response suggestions.

[0071] The maintenance decision support module optimizes maintenance strategies and provides resource scheduling and repair plan suggestions based on equipment health status, fault prediction and risk assessment results, so as to achieve the optimal balance between maintenance costs and benefits.

[0072] The management and optimization module ensures efficient and collaborative operation of all modules and continuously improves system performance.

[0073] This embodiment discloses a petrochemical equipment fault prediction and safety management system, which forms a comprehensive equipment management system through the organic collaboration of multiple modules. The technical design and implementation of each module ensures the system's efficiency, accuracy, and reliability. From real-time monitoring to health assessment, fault prediction, risk assessment, maintenance decision-making, and system management and optimization, all aspects of the system work closely together to achieve early warning of equipment faults, risk minimization, and optimization of maintenance costs. This not only improves equipment operating efficiency but also greatly enhances the safety of the production process and reduces the risk of accidents caused by equipment failures.

[0074] Furthermore, the condition perception and classification module includes the following components:

[0075] The real-time data acquisition unit is responsible for acquiring various operating parameters of the equipment in real time to ensure accurate acquisition of operating data.

[0076] The data preprocessing unit performs noise reduction, filtering, and normalization on the collected data.

[0077] The dynamic feature extraction unit extracts time-series features from the preprocessed data to form the equipment's operating condition feature vector.

[0078] The operating condition classification unit uses extracted features for training and classification to achieve real-time identification of different equipment operating conditions.

[0079] The operating condition feature library management unit builds and maintains a dynamically updated operating condition feature library, stores the historical operating condition features of devices, and provides comparison and reference for new operating condition classifications;

[0080] The working condition classification verification unit verifies the accuracy of the working condition classification based on the matching degree between the classification results and the feature library, and outputs a working condition classification credibility report.

[0081] In this embodiment, the operating condition perception and classification module, as a crucial component of the petrochemical equipment fault prediction and safety management system, provides accurate and real-time equipment operating status identification and fault early warning capabilities through a series of precise steps, including real-time data acquisition, data preprocessing, feature extraction, operating condition classification, feature library management, and classification verification. Each component complements the others, forming a closed-loop system from data acquisition to result verification. This system efficiently and accurately reflects changes in equipment operating conditions, providing early fault warnings and management support. By continuously optimizing the operating condition feature library and classification model, it can adapt to various changes during long-term equipment operation, ensuring accurate management and maintenance under different operating conditions. This module provides strong technical support for equipment health management, fault prediction, and safety early warning, greatly improving the efficiency and safety of equipment management.

[0082] Specifically, the sampling frequency of the real-time data acquisition unit is no less than 100Hz, the data preprocessing unit uses a low-pass filter with a cutoff frequency of 1 / 4 of the sampling frequency for signal filtering, the dynamic feature extraction unit performs frequency domain feature extraction based on 128-point fast Fourier transform, and the working condition classification unit uses a deep neural network classifier with an accuracy of no less than 90%.

[0083] Furthermore, the health status assessment module includes the following components:

[0084] The data fusion unit integrates multiple operating parameters, eliminates redundant information and noise, improves the accuracy and completeness of data, and provides more comprehensive input for health assessment.

[0085] The adaptive weight adjustment unit dynamically adjusts the weight of each parameter in the health assessment according to the operating conditions of the equipment, so as to adapt to the characteristics and performance of the equipment under different operating conditions.

[0086] The health index calculation unit calculates the health index of the equipment based on the fused data and adjusted weights, reflecting the current operational health status of the equipment.

[0087] The performance degradation trend analysis unit combines historical equipment data and health index to analyze the degradation trend of equipment performance.

[0088] The health status assessment report unit automatically generates a health status report for the device based on health indices and degradation trends.

[0089] In this embodiment, the health status assessment module effectively enhances the intelligence and precision of equipment management through multiple components, including data fusion, adaptive weight adjustment based on operating conditions, health index calculation, performance degradation trend analysis, and health status assessment reports. The data collection, processing, and assessment stages work together to provide strong support for equipment health management. The data fusion unit ensures high-quality input data, while the adaptive weight adjustment unit makes the assessment more flexible and accurate. The health index calculation unit provides clear quantitative indicators of health status, the performance degradation trend analysis unit can predict potential equipment problems in advance, and finally, the health status assessment report unit ensures that the assessment results are communicated intuitively and effectively to equipment managers. Through the collaborative work of this module, comprehensive and dynamic health monitoring and analysis of equipment can be performed, thereby helping petrochemical companies improve equipment reliability, extend equipment life, and take preventative measures before equipment failures occur, significantly reducing the risk of equipment failures and production downtime.

[0090] Furthermore, the health index H of the equipment is expressed by the formula: Where n represents the number of all operating parameters involved in the equipment health assessment; X iW represents the actual value of the i-th operating parameter of the equipment under the current operating conditions; i (C) represents the dynamic weight of the i-th parameter in the equipment health assessment under the current operating condition C, expressed by the formula: in, f represents the initial weight of the j-th parameter in the equipment health assessment; j (C) indicates that under the current operating condition C, parameter X j Operating condition factors; f is the initial weight of the i-th parameter, which serves as the base weight for each parameter in the health assessment when there are no changes in operating conditions; i (C) is the parameter X i The operating condition factor under the current operating conditions reflects the effect of the current operating conditions on parameter X. i The extent of the impact; It is the normalization factor for all parameter weights, ensuring that the sum of the weights is 1.

[0091] Furthermore, the fault prediction and diagnosis module includes the following components:

[0092] The historical fault case library unit stores and manages historical fault data and related parameters of the equipment.

[0093] The fault prediction unit trains and infers from historical and real-time data to identify the type, location and development trend of potential faults and perform fault prediction.

[0094] The knowledge graph construction unit builds a knowledge graph based on the working principle of the equipment, failure cases and their interrelationships, in order to more accurately analyze the causes and influencing factors of failures;

[0095] The fault mechanism diagnosis unit performs mechanism analysis on the predicted fault types, determines the root cause of the fault, and provides a detailed explanation of the fault cause.

[0096] The fault prediction result analysis unit assesses the occurrence time, severity, and potential impact of potential faults based on the prediction results.

[0097] The diagnostic reliability assessment unit evaluates the reliability of fault diagnosis results. Through confidence quantification analysis, it ensures the reliability of fault prediction and diagnosis and provides a basis for decision-making.

[0098] In this embodiment, the fault prediction and diagnosis module integrates historical fault data, real-time monitoring data, knowledge graphs, and deep learning technologies to accurately predict potential equipment faults, analyze their root causes, and provide decision-makers with scientific fault handling solutions. The various components work together to form a complete fault prediction and diagnosis system. The historical fault case library unit provides the system with rich historical data; the fault prediction unit identifies potential faults through intelligent algorithms; the knowledge graph construction unit enhances the depth and accuracy of fault analysis; the fault mechanism diagnosis unit provides detailed explanations of fault causes; the fault prediction result analysis unit assesses the consequences and impacts of the fault; and the diagnostic credibility assessment unit ensures the reliability of the diagnostic results. The comprehensive application of these technologies and analytical methods not only improves the intelligence level of equipment management but also provides strong support for the safe operation and reliability assurance of equipment.

[0099] Specifically, the historical fault case library unit has a storage capacity of no less than 10TB and supports the storage of no less than 1 million fault records; the knowledge graph construction unit contains no less than 1,000 core concept nodes and 5,000 relation edges and supports no less than 100 graph query operations per second; the confidence level of the fault prediction results is divided into five levels, of which: ≥95% is extremely high, 85-94% is high, 70-84% is medium, 50-69% is low, and <50% is extremely low.

[0100] Furthermore, the risk assessment and early warning module includes the following components:

[0101] The comprehensive risk assessment unit assesses equipment safety and performance risks based on fault prediction results, combined with equipment health status, environmental factors, and operating conditions, and generates a risk impact analysis report.

[0102] The dynamic threshold optimization unit dynamically adjusts the risk threshold based on real-time and historical data to ensure more accurate risk assessment under different operating conditions and equipment states, and to reflect potential risks in a timely manner.

[0103] The early warning level determination unit, based on the risk assessment results and the set thresholds, determines the severity of the current risk and classifies the risk into different early warning levels;

[0104] The multi-level early warning strategy generation unit generates multi-level early warning information and automatically formulates corresponding early warning strategies according to the risk level, ensuring a step-by-step response from early warning to emergency response.

[0105] The emergency response suggestion generation unit provides specific emergency response suggestions based on early warning information and risk levels, including operational guidance, equipment shutdown suggestions, and maintenance scheduling, to help personnel respond in a timely manner.

[0106] In this embodiment, the core of the risk assessment and early warning module lies in its ability to promptly identify and address equipment risks through accurate risk assessment, dynamic threshold optimization, intelligent early warning, and emergency response suggestions. By integrating the risk assessment unit, dynamic threshold optimization unit, early warning level determination unit, multi-level early warning strategy generation unit, and emergency response suggestion generation unit, timely warnings can be issued when potential equipment failures occur, along with detailed emergency response strategies. The precise coordination of each component enables equipment management not only to respond promptly to changes in equipment health but also to take appropriate preventative measures before risks materialize, thereby improving equipment safety and operational efficiency.

[0107] Furthermore, the risk threshold is expressed by the formula: Where RiskThreshold(t) is the risk threshold at the current time t, used to determine the current risk level of the device; H base The historical data-based risk threshold represents the standard risk threshold set based on the equipment's historical operating data; λ data D is a weighting factor for the data, representing the relative importance of real-time and historical data; max P represents the maximum value of the equipment's health status, signifying the health index under ideal or optimal conditions; D(t) represents the equipment's health status at time t, derived from a comprehensive evaluation of multiple operating parameters; max θ represents the maximum value of the equipment's operating parameters, signifying the upper limit of parameters that the equipment can reach under optimal operating conditions; P(t) represents the equipment's operating parameters at the current time t, indicating the real-time operating data of the equipment under the current operating conditions; adaptive (t) is the adaptive adjustment factor for operating conditions, which reflects the dynamic changes of the risk threshold under different operating conditions.

[0108] Furthermore, the maintenance decision support module includes the following components:

[0109] The maintenance requirements analysis unit analyzes the current maintenance requirements of the equipment based on its health status, fault prediction, and historical maintenance data, providing basic data for optimization decisions.

[0110] The maintenance strategy optimization unit optimizes maintenance strategies based on requirements analysis, combined with failure modes, risk assessment results, and maintenance costs, and selects the most suitable maintenance type and frequency.

[0111] The resource scheduling optimization unit rationally schedules maintenance personnel, equipment, and material resources based on the optimized maintenance strategy to ensure the efficient execution of maintenance activities and reduce resource waste.

[0112] The maintenance solution suggestion unit automatically generates targeted maintenance solutions, including specific operating steps, required time and tools, to ensure the smooth completion of maintenance tasks and minimize equipment downtime.

[0113] The cost-benefit assessment unit conducts cost-benefit analysis on different maintenance plans, comprehensively considering maintenance costs, downtime, and equipment life extension factors to provide the optimal balance for decision-making.

[0114] The priority sorting unit optimizes the priority of maintenance tasks based on the urgency of the equipment, the type of failure, and the maintenance cost, ensuring timely maintenance of critical equipment.

[0115] In this embodiment, the maintenance needs analysis unit provides basic data for decision-making; the maintenance strategy optimization unit selects the most suitable maintenance strategy by comprehensively considering maintenance costs and risks; the resource scheduling optimization unit improves maintenance efficiency by intelligently allocating resources; the repair plan suggestion unit generates targeted plans to ensure the smooth progress of repair tasks; the cost-benefit evaluation unit provides support for decision-making through economic analysis; and the priority ranking unit ensures that critical equipment is repaired in a timely manner.

[0116] Specifically, maintenance strategy optimization is evaluated and adjusted on a 30-day cycle; the calculation time for resource scheduling optimization does not exceed 5 minutes; the generation time for maintenance plans does not exceed 10 minutes; cost-benefit evaluation uses the ROI indicator, requiring an ROI of not less than 200%; maintenance task priorities are divided into 4 levels, with P1 requiring a response within 2 hours, P2 requiring a response within 24 hours, P3 requiring a response within 72 hours, and P4 requiring a response within 168 hours.

[0117] Furthermore, the management and optimization module includes the following components:

[0118] The system status monitoring unit is responsible for monitoring the overall system status, collecting and analyzing the output data of each module, ensuring that all system tasks are performed as expected, and promptly identifying potential problems.

[0119] The performance evaluation unit periodically evaluates the overall performance of the system, including computational efficiency, resource utilization, and task completion quality, and provides feedback information for performance optimization.

[0120] The parameter optimization unit manages and optimizes the operating parameters of all modules in the system in a unified manner.

[0121] The collaborative scheduling unit optimizes the collaborative work between modules. Through dynamic scheduling and task allocation mechanisms, it ensures the coordination of different modules and maximizes the overall efficiency of the system.

[0122] The fault detection and recovery unit monitors the health status of the system, detects communication anomalies or faults between modules, and automatically isolates and recovers from faults to ensure the continuous and stable operation of the system.

[0123] In this embodiment, the management and optimization module ensures the efficient, stable, and continuous operation of the system through the collaborative work of multiple sub-modules, including system status monitoring, performance evaluation, parameter optimization, collaborative scheduling, and fault detection and recovery. The system status monitoring unit provides real-time system operation data to help identify potential problems promptly; the performance evaluation unit periodically evaluates and optimizes system performance to ensure optimal operating efficiency; the parameter optimization unit intelligently adjusts system parameters to improve overall system performance; the collaborative scheduling unit optimizes cooperation between modules to maximize resource utilization; and the fault detection and recovery unit ensures system reliability by automatically repairing and isolating faults to guarantee uninterrupted system operation. Through the effective collaboration of these components, the management and optimization module can continuously improve the system's performance and stability, providing a solid guarantee for the efficient completion of complex tasks.

[0124] like Figure 2 As shown, this embodiment also provides a method for fault prediction and safety management of petrochemical equipment, including the following steps:

[0125] Real-time data collection of equipment operation, such as temperature, pressure, vibration and noise parameters, is achieved through a distributed sensor network, and the data is preprocessed by adaptive filtering and normalization.

[0126] By using deep learning algorithms to extract time-series and frequency-domain features from the processed data, and comparing them in real time with a dynamically updated operating condition feature library, accurate classification of equipment operating status can be achieved.

[0127] By integrating equipment operating parameters, historical performance data, and environmental factors, a comprehensive health index is calculated, and the performance degradation trend of the equipment is predicted.

[0128] A fault prediction model is established by combining deep learning and knowledge graph technology to identify potential faults and compare them with an expert database for fault diagnosis.

[0129] Based on the equipment health status and fault prediction results, combined with the equipment importance and environmental impact factors, the risk level is calculated, and corresponding early warning and emergency response suggestions are generated.

[0130] By integrating health status, fault prediction, and risk assessment results, the system automatically generates the optimal maintenance strategy to ensure the long-term stable operation of the equipment.

[0131] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A petrochemical equipment fault prediction and safety management system, characterized in that, Includes the following modules: The operating condition perception and classification module enables real-time identification, classification, and feature extraction of equipment operating status; The health status assessment module calculates the equipment health index and outputs the analysis results of the equipment performance degradation trend. The fault prediction and diagnosis module predicts potential fault types and occurrence times, performs diagnostic analysis on fault mechanisms, and provides a reliability assessment of the diagnostic results. The risk assessment and early warning module comprehensively evaluates multi-dimensional risk factors and generates graded early warning information through dynamic threshold optimization and multi-level early warning strategies. The maintenance decision support module optimizes maintenance strategies and provides resource scheduling and repair plan suggestions to achieve the optimal balance between maintenance costs and benefits. The management and optimization module ensures efficient and collaborative operation of all modules and continuously improves system performance.

2. The petrochemical equipment fault prediction and safety management system according to claim 1, characterized in that, The condition perception and classification module includes the following components: The real-time data acquisition unit is responsible for acquiring various operating parameters of the equipment in real time to ensure accurate acquisition of operating data. The data preprocessing unit performs noise reduction, filtering, and normalization on the collected data. The dynamic feature extraction unit extracts time-series features from the preprocessed data to form the equipment's operating condition feature vector. The operating condition classification unit uses extracted features for training and classification to achieve real-time identification of different equipment operating conditions. The operating condition feature library management unit builds and maintains a dynamically updated operating condition feature library, stores the historical operating condition features of devices, and provides comparison and reference for new operating condition classifications; The working condition classification verification unit verifies the accuracy of the working condition classification based on the matching degree between the classification results and the feature library, and outputs a working condition classification credibility report.

3. The petrochemical equipment fault prediction and safety management system according to claim 1, characterized in that, The health status assessment module includes the following components: The data fusion unit integrates multiple operating parameters, eliminates redundant information and noise, improves the accuracy and completeness of data, and provides more comprehensive input for health assessment. The adaptive weight adjustment unit dynamically adjusts the weight of each parameter in the health assessment according to the operating conditions of the equipment, so as to adapt to the characteristics and performance of the equipment under different operating conditions. The health index calculation unit calculates the health index of the equipment based on the fused data and adjusted weights, reflecting the current operational health status of the equipment. The performance degradation trend analysis unit combines historical equipment data and health index to analyze the degradation trend of equipment performance. The health status assessment report unit automatically generates a health status report for the device based on health indices and degradation trends.

4. The petrochemical equipment fault prediction and safety management system according to claim 1, characterized in that, The fault prediction and diagnosis module includes the following components: The historical fault case library unit stores and manages historical fault data and related parameters of the equipment. The fault prediction unit trains and infers from historical and real-time data to identify the type, location and development trend of potential faults and perform fault prediction. The knowledge graph construction unit builds a knowledge graph based on the working principle of the equipment, failure cases and their interrelationships, in order to more accurately analyze the causes and influencing factors of failures; The fault mechanism diagnosis unit performs mechanism analysis on the predicted fault types, determines the root cause of the fault, and provides a detailed explanation of the fault cause. The fault prediction result analysis unit assesses the occurrence time, severity, and potential impact of potential faults based on the prediction results. The diagnostic reliability assessment unit evaluates the reliability of fault diagnosis results. Through confidence quantification analysis, it ensures the reliability of fault prediction and diagnosis and provides a basis for decision-making.

5. The petrochemical equipment fault prediction and safety management system according to claim 1, characterized in that, The risk assessment and early warning module includes the following components: The comprehensive risk assessment unit assesses equipment safety and performance risks based on fault prediction results, combined with equipment health status, environmental factors, and operating conditions, and generates a risk impact analysis report. The dynamic threshold optimization unit dynamically adjusts the risk threshold based on real-time and historical data to ensure more accurate risk assessment under different operating conditions and equipment states, and to reflect potential risks in a timely manner. The early warning level determination unit, based on the risk assessment results and the set thresholds, determines the severity of the current risk and classifies the risk into different early warning levels; The multi-level early warning strategy generation unit generates multi-level early warning information and automatically formulates corresponding early warning strategies according to the risk level, ensuring a step-by-step response from early warning to emergency response. The emergency response suggestion generation unit provides specific emergency response suggestions based on early warning information and risk levels, including operational guidance, equipment shutdown suggestions, and maintenance scheduling, to help personnel respond in a timely manner.

6. The petrochemical equipment fault prediction and safety management system according to claim 1, characterized in that, The maintenance decision support module includes the following components: The maintenance requirements analysis unit analyzes the current maintenance requirements of the equipment based on its health status, fault prediction, and historical maintenance data, providing basic data for optimization decisions. The maintenance strategy optimization unit optimizes maintenance strategies based on requirements analysis, combined with failure modes, risk assessment results, and maintenance costs, and selects the most suitable maintenance type and frequency. The resource scheduling optimization unit rationally schedules maintenance personnel, equipment, and material resources based on the optimized maintenance strategy to ensure the efficient execution of maintenance activities and reduce resource waste. The maintenance solution suggestion unit automatically generates targeted maintenance solutions, including specific operating steps, required time and tools, to ensure the smooth completion of maintenance tasks and minimize equipment downtime. The cost-benefit assessment unit conducts cost-benefit analysis on different maintenance plans, comprehensively considering maintenance costs, downtime, and equipment life extension factors to provide the optimal balance for decision-making. The priority sorting unit optimizes the priority of maintenance tasks based on the urgency of the equipment, the type of failure, and the maintenance cost, ensuring timely maintenance of critical equipment.

7. The petrochemical equipment fault prediction and safety management system according to claim 1, characterized in that, The management and optimization module includes the following components: The system status monitoring unit is responsible for monitoring the overall system status, collecting and analyzing the output data of each module, ensuring that all system tasks are performed as expected, and promptly identifying potential problems. The performance evaluation unit periodically evaluates the overall performance of the system, including computational efficiency, resource utilization, and task completion quality, and provides feedback information for performance optimization. The parameter optimization unit manages and optimizes the operating parameters of all modules in the system in a unified manner. The collaborative scheduling unit optimizes the collaborative work between modules. Through dynamic scheduling and task allocation mechanisms, it ensures the coordination of different modules and maximizes the overall efficiency of the system. The fault detection and recovery unit monitors the health status of the system, detects communication anomalies or faults between modules, and automatically isolates and recovers from faults to ensure the continuous and stable operation of the system.

8. A method for predicting and managing the failure of petrochemical equipment, implemented based on the petrochemical equipment failure prediction and safety management system described in any one of claims 1-7, characterized in that, Includes the following steps: Real-time data collection of equipment operation, such as temperature, pressure, vibration and noise parameters, is achieved through a distributed sensor network, and the data is preprocessed by adaptive filtering and normalization. By using deep learning algorithms to extract time-series and frequency-domain features from the processed data, and comparing them in real time with a dynamically updated operating condition feature library, accurate classification of equipment operating status can be achieved. By integrating equipment operating parameters, historical performance data, and environmental factors, a comprehensive health index is calculated, and the performance degradation trend of the equipment is predicted. A fault prediction model is established by combining deep learning and knowledge graph technology to identify potential faults and compare them with an expert database for fault diagnosis. Based on the equipment health status and fault prediction results, combined with the equipment importance and environmental impact factors, the risk level is calculated, and corresponding early warning and emergency response suggestions are generated. By integrating health status, fault prediction, and risk assessment results, the system automatically generates the optimal maintenance strategy to ensure the long-term stable operation of the equipment.

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