Intelligent fault diagnosis and maintenance method and system based on dynamic cause-effect graph of multimodal data fusion

Abstract

The present invention proposes a dynamic causal graph reasoning model based on fusion, including the following steps: collecting multimodal data of equipment, performing preprocessing such as standardization, missing data filling and noise cleaning; then fusing different modal data through a cross-modal embedding network to generate a unified high-dimensional feature representation; constructing a dynamic causal graph based on the causal reasoning model, with nodes representing fault phenomena, edges representing causal relationships, and weights dynamically adjusted through Bayesian updating; identifying key nodes in the fault chain through the maximum a posteriori (MAP) algorithm, and generating multi-step maintenance suggestions; introducing a dynamic feedback mechanism through reinforcement learning to continuously optimize the reasoning model and maintenance strategy. The present invention can effectively fuse multimodal data, and through dynamic causal graph reasoning and dynamic feedback and self-optimization mechanisms, the system can continuously optimize the diagnosis model according to real-time data to improve the efficiency of fault diagnosis and maintenance.

Description

Technical Field

[0001] The present invention relates to the field of intelligent diagnosis and maintenance, and in particular to a dynamic causal graph intelligent fault diagnosis and maintenance method and system based on multimodal data fusion. Background Art

[0002] As the complexity of industrial equipment and automation systems continues to increase, the diagnosis and maintenance of equipment failures have become increasingly challenging. Traditional equipment fault diagnosis methods usually rely on a single data source (such as fault codes or sensor data) or a rule-based reasoning system. However, these methods have limitations when dealing with complex faults, which are mainly manifested in the following aspects: (1) Single data source limitation: Traditional diagnostic models usually rely only on sensor data or fault codes, ignoring other key data sources, such as historical fault data of the equipment and external environment data. This leads to a lack of sufficient contextual information when diagnosing complex faults, which is prone to misdiagnosis or missed diagnosis; (2) Unable to handle multi-cause and multi-effect faults: Modern industrial equipment failures usually manifest as multi-cause and multi-effect, that is, multiple factors may jointly cause a certain fault phenomenon, and a certain fault phenomenon may also cause multiple subsequent faults. Traditional diagnostic methods based on simple rule reasoning have difficulty in handling these complex cause-and-effect relationships and cannot provide accurate diagnosis and effective maintenance suggestions; (1) Lack of dynamic adjustment and optimization capabilities: Most existing fault diagnosis systems are static models and cannot dynamically adjust their diagnostic strategies as the status of the equipment changes during operation. As a result, these systems are unable to optimize themselves when the equipment’s operating conditions change, and the accuracy of diagnostics drops significantly.

[0003] For example, some Bayesian network models and expert system-based diagnostic systems in the existing technology can represent the causal relationship between fault phenomena to a certain extent, but the causal network they construct is usually static, difficult to adapt to the real-time changes in equipment status, and fails to fully utilize the fusion capabilities of multimodal data, resulting in limited performance in complex fault diagnosis.

[0004] Based on this, the present invention proposes a dynamic causal graph-based intelligent fault diagnosis and maintenance method and system based on multimodal data fusion, which can make full use of sensor data, historical fault data and external environment data, achieve self-optimization by constructing a dynamic causal network and combining reinforcement learning, solve the limitations of the existing technology, and provide more accurate and efficient fault diagnosis and maintenance suggestions. Summary of the invention

[0005] One of the purposes of the present invention is to provide a dynamic causal graph intelligent fault diagnosis and maintenance method based on multimodal data fusion, aiming to realize intelligent diagnosis of complex equipment faults and generation of maintenance suggestions. The core technical steps include multimodal data acquisition and preprocessing, multimodal data fusion, construction of dynamic causal graph, fault chain analysis and reasoning, multi-step maintenance suggestion generation and dynamic feedback and self-optimization. The specific steps are as follows:

[0006] 1. Multimodal data acquisition and preprocessing: Obtain the operating status information of the equipment from multiple data sources, mainly including sensor data of the real-time operating status of the equipment such as temperature, pressure, vibration, and speed, historical fault data of the historical fault records of the equipment or similar equipment, and external environmental data of information such as temperature, humidity, air quality, and workload in the equipment operating environment. Use standardized data processing technology to eliminate the data scale differences between different sensors. For the problem of missing data, process the missing data through interpolation, mean filling, etc. At the same time, remove noise data or outliers, and clean the data through anomaly detection algorithms (such as those based on local outlier factors (LOF)).

[0007] 2. Multimodal data fusion: Use multi-layer perceptron (MLP) or convolutional neural network (CNN) to fuse data from different sources and types into a unified feature vector for integrated analysis. This process achieves data fusion through cross-modal embedding technology in deep learning. Cross-modal embedding combines sensor data D s 、Historical data D h and environmental data D e Mapped to the same high-dimensional feature space. The formula is:

[0008] F m =f embed (D s ,D h ,D e )

[0009] Among them, f embed It is a deep neural network for cross-modal fusion. It uses the attention mechanism to automatically select the most relevant modality information for the current diagnostic task, improving the accuracy and efficiency of data fusion.

[0010] 3. Construction of dynamic causal graph: Based on the feature data after multimodal fusion, a causal graph is constructed. The graph is represented by a directed acyclic graph (DAG). The nodes represent specific fault phenomena, the directed edges between the nodes represent causal relationships, and the weights reflect the strength of the causal relationships. The initial causal relationship graph is constructed with the assistance of an expert knowledge base, which is particularly suitable for areas with rich equipment maintenance experience. The Markov Chain Monte Carlo (MCMC) method is introduced to sample the parameters of the causal graph and further optimize the graph construction. The Bayesian network is then used for causal reasoning, and the state of the node is distributed according to the conditional probability of its parent node:

[0011] P(X i |P a (X i ))=P(X i |X1,X2,…,X k )

[0012] The Bayesian update method is used to dynamically adjust the edge weights to adapt to real-time changes in device status:

[0013]

[0014] Among them, θ is the model parameter and D is the observed data.

[0015] 4. Fault chain analysis and reasoning: In the constructed causal graph, analyze the relationship between multiple fault phenomena and find the key fault chain. Use the maximum a posteriori (MAP) algorithm for reasoning:

[0016]

[0017] The system identifies the key nodes that cause equipment abnormalities through the reasoning chain. The variational inference (VI) method can also be used instead of MAP inference to improve the speed and efficiency of reasoning.

[0018] 5. Generate multi-step maintenance suggestions: Based on the causal graph reasoning results, after identifying the key fault nodes, the system generates multi-step maintenance suggestions for the equipment. The suggestions include not only the repair of the key faults, but also the handling of related fault phenomena. For example: Check and repair the key fault nodes. Perform secondary inspections for sensor false alarms or other potential problems. Update the status of related components or systems to confirm that there are no other hidden dangers in the equipment.

[0019] 6. Dynamic feedback and self-optimization: When performing maintenance tasks, the system collects equipment feedback data and optimizes the reasoning model and maintenance strategy based on the feedback information. Reinforcement Learning (RL) is used for strategy optimization.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] (1) Multimodal data fusion improves diagnostic accuracy: By fusing sensor data, historical fault data, and external environment data to generate a unified high-dimensional feature vector, the model can obtain complete contextual information about the equipment's operating status. This multi-dimensional data input significantly improves the accuracy of complex fault diagnosis and solves the problem of misdiagnosis or missed diagnosis caused by traditional methods that rely only on a single data source.

[0022] (2) Dynamic causal graph reasoning to handle multiple causes and multiple effects: The present invention can accurately represent the causal relationship between multiple fault phenomena by constructing a dynamic causal graph, and identify key fault nodes through the maximum a posteriori (MAP) algorithm. This method can effectively handle the multiple causes and multiple effects phenomenon in equipment failure, so that the diagnostic system can not only identify surface faults, but also find the deep-seated root causes of faults, thereby generating more targeted maintenance strategies.

[0023] (3) Self-optimization to improve diagnostic efficiency: By introducing a reinforcement learning mechanism, the system can dynamically adjust the weights and reasoning paths of the causal graph based on real-time feedback data, and gradually optimize the diagnostic model. This enables the system to adapt to changes in equipment status, improve diagnostic efficiency, and effectively reduce misjudgments and repeated detections, solving the problem that traditional static diagnostic systems cannot cope with changes in equipment operating conditions.

[0024] (4) Multi-step maintenance suggestions to improve maintenance quality: By analyzing the key nodes in the fault chain, the model can generate multi-step maintenance suggestions, not only for a single fault, but also for related faults. This maintenance strategy has a wide coverage, can reduce maintenance time, avoid missing potential problems, and improve the overall quality of equipment maintenance.

[0025] (5) Dynamic feedback and optimization to enhance system adaptability: The present invention continuously optimizes the reasoning strategy through a real-time feedback mechanism and uses reinforcement learning algorithms such as Q learning to update the reasoning path during the fault diagnosis process. As maintenance tasks accumulate, the system can improve the accuracy of its fault identification and maintenance suggestions, ensuring that optimized diagnosis and maintenance solutions can be provided in different fault scenarios.

[0026] The second object of the present invention is to provide a dynamic causal graph intelligent fault diagnosis and maintenance system based on multimodal data fusion, the system comprising:

[0027] Data collection and perception module:

[0028] Sensor unit: including temperature sensor, pressure sensor, vibration sensor, speed sensor, etc., used to obtain equipment operating status parameters in real time.

[0029] Data interface and communication unit: realize real-time transmission and access of on-site sensor data, and obtain historical fault data from the equipment history record database; at the same time, connect to external environmental sensors (such as thermometers and hygrometers, air quality detectors) to obtain equipment environmental parameters.

[0030] This module is responsible for connecting data from multiple sources (real-time operation data, historical fault data, external environment data) to the system to provide basic input for subsequent data processing and fusion.

[0031] Data preprocessing and cleaning module:

[0032] Data standardization submodule: standardizes data of different dimensions and scales so that the parameters of each sensor can be compared under the same reference scale.

[0033] Data missing filling submodule: Use interpolation, mean filling and other methods to repair missing data to ensure the integrity of data input.

[0034] Data cleaning and anomaly detection submodule: Use algorithms based on local outlier factor (LOF) to identify and remove noise points and outliers in the data to improve data quality.

[0035] This module ensures that the feature data input into the system is clean and available for subsequent model training and reasoning. Multimodal data fusion module:

[0036] Deep feature extraction unit: Uses deep learning models such as multi-layer perceptron (MLP) and convolutional neural network (CNN) to extract features from sensor data, historical data, and environmental data.

[0037] Cross-modal embedding and fusion unit: Through the attention mechanism and cross-modal embedding technology, different types of data are mapped into the same high-dimensional feature space to generate a comprehensive feature vector F_m.

[0038] This module realizes unified feature representation of multi-source data and provides comprehensive and rich input information for constructing causal graphs.

[0039] Dynamic causal graph building blocks:

[0040] Causal relationship construction submodule: A directed acyclic graph (DAG) is used to represent the causal structure, where nodes represent fault phenomena and edges represent causal relationships.

[0041] Parameter learning and updating submodule: The parameters (edge ​​weights) of the graph are optimized and updated through Bayesian networks and Markov chain Monte Carlo (MCMC) methods, so that the causal relationship remains valid as the device state changes.

[0042] This module builds and maintains a dynamic causal graph to intuitively represent the complex fault relationship network of the equipment. Fault chain reasoning and analysis module:

[0043] Inference engine: Uses maximum a posteriori (MAP) inference or variational inference (VI) algorithm to identify key fault nodes and the direction of fault chains from dynamic causal graphs.

[0044] Key fault location unit: By analyzing the cause-effect chain, the root node of the equipment abnormality is found to guide subsequent maintenance decisions.

[0045] This module uses causal reasoning methods to analyze complex fault relationships into clear fault chains and determine maintenance priorities and key points.

[0046] Multi-step repair suggestion generation module

[0047] Maintenance step planning submodule: Based on the fault chain analysis results, a maintenance plan containing multiple steps is generated for equipment maintenance personnel.

[0048] Related Failure Prevention Submodule: Includes component inspection and maintenance plans related to critical failures in recommendations to avoid potential problems from happening again.

[0049] The module outputs recommendations that can directly guide on-site maintenance operations, including fault repair, follow-up inspections, preventive maintenance, etc.

[0050] Dynamic feedback and self-optimization module

[0051] Reinforcement learning decision-making submodule: Utilizes reinforcement learning algorithms such as Q-learning to optimize the cause-effect graph and maintenance strategy based on the feedback information of the equipment after maintenance is performed.

[0052] Model adaptive update unit: It continuously iterates and updates the parameters and strategies of the inference model in multiple rounds of feedback loops, making the system adaptive to environmental changes and the evolution of equipment failure modes.

[0053] This module enables the system to evolve in real-world scenarios, improving the accuracy and effectiveness of fault diagnosis and maintenance recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of intelligent fault diagnosis and maintenance based on a dynamic cause-effect graph of multimodal data fusion according to the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0057] The present invention proposes a dynamic causal graph intelligent fault diagnosis and maintenance method and system based on multimodal data fusion, aiming to solve the limitations of traditional methods in complex equipment fault diagnosis and maintenance. The method realizes intelligent fault diagnosis and optimized maintenance strategy through the fusion of multimodal data, dynamic construction of causal graph, reasoning of fault chain and generation of multi-step maintenance suggestions. The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] Embodiment 1:

[0059] The intelligent fault diagnosis and maintenance method and system based on dynamic causal graph of multimodal data fusion include:

[0060] Step 1: Multimodal data collection (corresponding to Figure 1 The first part of

[0061] The present invention obtains real-time status information of the device through multiple data sources, mainly including:

[0062] Equipment operation sensor data: The equipment operation data is obtained in real time through sensors, such as temperature, vibration, pressure and other information. This data can reflect the current status of the equipment and is the basis for diagnosis.

[0063] Historical fault record data: By querying the fault log of the device, obtain the previous fault data of the same or similar equipment to provide historical reference for the model.

[0064] External environment data: Collect parameters in the environment where the device is located, such as temperature, humidity, noise, etc., as potential influencing factors of device status changes.

[0065] These data are collected in real time through the system and become the basic input of multimodal data.

[0066] Step 2: Multimodal data processing (corresponding to Figure 1 The second part of

[0067] The collected multimodal data are processed as follows:

[0068] Data standardization: Since sensor data has different dimensions (e.g. temperature is in degrees Celsius, vibration is in meters per second squared), standardization is used to adjust the data to the same scale to ensure uniformity in subsequent processing.

[0069] Missing data filling: In the event of sensor failure or data loss, use interpolation or mean filling methods to repair the missing data to ensure complete input to the model.

[0070] Noise data cleaning: Use anomaly detection algorithms (such as those based on local outlier factors LOF) to identify and remove abnormal data points to ensure data validity.

[0071] Step 3: Cross-modal fusion network (corresponding to Figure 1 The third part of

[0072] To achieve a unified representation of multimodal data, the present invention adopts a cross-modal fusion network, including: a multi-layer perceptron (MLP): used to extract and fuse deep features of sensor data, historical data, and environmental data, and transform these different modal data into a unified representation in a high-dimensional feature space.

[0073] Convolutional Neural Network (CNN): further extracts the characteristic relationships of spatiotemporal data, which is particularly suitable for processing time series data from sensors, enabling the model to perceive the operating trends of equipment.

[0074] Through the above fusion network, a feature vector containing multi-dimensional information is finally formed, providing a more comprehensive input for subsequent causal reasoning.

[0075] Step 4: Dynamic causal graph construction (corresponding to Figure 1 Part 4 of

[0076] The present invention intuitively represents the causal relationship between device failure phenomena by constructing a dynamic causal graph. The causal graph is represented by a directed acyclic graph (DAG), in which nodes represent device failure phenomena and edges represent the causal relationship between failure phenomena.

[0077] Nodes of fault phenomena: Each node corresponds to a specific fault phenomenon (such as overtemperature, abnormal vibration). Edges of causal relationships: The directed edges between nodes represent the causal relationships between fault phenomena, and the weights reflect the strength of the causal relationships. The weights are dynamically adjusted through the Bayesian update method to adapt to changes in the device status.

[0078] This step dynamically updates the causal graph structure through the causal reasoning model to adapt to the real-time changing device status.

[0079] Step 5: Fault chain analysis and reasoning (corresponding to Figure 1 The fifth part of

[0080] Based on the construction of the causal graph, the system analyzes the connections between multiple fault phenomena through the maximum a posteriori (MAP) algorithm and identifies the key nodes in the fault chain.

[0081] Maximum a posteriori (MAP): By calculating the posterior probability of the fault phenomenon, the most likely fault path is identified. MAP inference can accurately locate the key nodes in the fault chain, that is, the root cause that is most likely to cause the current equipment failure.

[0082] Variational Inference (VI): As a complementary algorithm for inference, it is used to process high-dimensional complex data and improve inference efficiency and accuracy.

[0083] The result of this step is that the system identifies the key failure nodes of the equipment, which serves as the basis for subsequent maintenance recommendations.

[0084] Step 6: Generate multi-step repair recommendations (corresponding to Figure 1 Part 6 of

[0085] After identifying the key fault nodes, the system generates multi-step maintenance recommendations based on the inference results, including: Repair of key faults: It is recommended that maintenance personnel check and repair the key nodes that cause the fault phenomenon. For example, if it is inferred that the motor bearing wear causes abnormal vibration, the system will recommend replacing or lubricating the bearing.

[0086] Handling of fault phenomena: Based on the cause-effect chain, it is recommended to check other components that may be affected to avoid potential problems.

[0087] This multi-step maintenance recommendation not only targets critical faults, but also includes preventive treatment of related faults, which can significantly reduce maintenance time and improve maintenance efficiency.

[0088] Step 7: Dynamic feedback and self-optimization (corresponding to Figure 1 Part 7 of

[0089] In the dynamic feedback and self-optimization link, the present invention uses the Q-learning reinforcement learning method to model the equipment fault diagnosis and maintenance strategy update as a continuous iterative and optimized decision-making process. State space: includes causal graph structure (nodes, edges and weights), equipment failures and operating indicator changes, historical decisions and execution results. The state comprehensively describes the current equipment health and the effectiveness of previous maintenance plans. Action: adjust the causal graph structure and edge weights, modify the fault diagnosis strategy (such as MAP, VI inference parameters), and optimize the maintenance suggestion steps and sequence, so that the system can more effectively solve faults in similar scenarios in the future. Reward function: assign reward values ​​to each decision by evaluating indicators such as fault elimination rate, maintenance cost and time, prediction accuracy and robustness. Successfully reducing the equipment failure rate or improving maintenance efficiency will receive positive rewards, and vice versa.

[0090] Q-learning update process: Using the Q-value update rule, the system continuously modifies the Q table based on the current state, the selected action, the immediate reward obtained, and the optimal Q-value of the next state, and gradually learns the optimal (or approximately optimal) action strategy under various states.

[0091] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A dynamic causal graph intelligent fault diagnosis and maintenance method based on multimodal data fusion, characterized in that: The following steps are involved: (1) Multimodal data collection and preprocessing, including obtaining equipment sensor data, historical fault data, and external environment data, and performing data standardization, missing data filling, and noise elimination; (2) Multimodal data fusion: Mapping data of different modalities into the same high-dimensional feature space through cross-modal embedding technology, and using the attention mechanism to select the most relevant data modality at the moment; (3) Dynamic causal graph construction, using directed acyclic graph (DAG) to represent the causal relationship of fault phenomena, and using Bayesian network to dynamically adjust the causal relationship weight according to conditional probability distribution; (4) Fault chain analysis, which uses maximum a posteriori (MAP) to identify key nodes in the fault chain and generate the causal path that is most likely to cause the fault phenomenon; (5) Generate multi-step maintenance recommendations and provide multi-step maintenance strategies for key fault nodes and possible related fault phenomena; (6) Dynamic feedback and self-optimization: using reinforcement learning methods to update the inference model based on equipment feedback data and optimize the generation of maintenance recommendations.

2. The method according to claim 1, characterized in that The multimodal data collection and preprocessing steps include: (1) standardizing data from different sources to ensure that the scale of different sensor data is unified; (2) using interpolation or mean filling methods to solve the problem of missing data; (3) detecting and removing outliers through the local outlier factor (LOF) algorithm.

3. The method according to claim 1, characterized in that The multimodal data fusion uses cross-modal embedding technology to fuse sensor data, historical fault data and external environment data into a unified feature representation. The formula is as follows: F m =f embed (D s ,D h ,D e ) Among them, D s , D h and D e are sensor data, historical fault data and external environment data, respectively, embed is the cross-modal embedding function.

4. The method according to claim 1, characterized in that: The dynamic causal graph is constructed by using a Bayesian network to represent causal relationships. The state of each node is determined by the conditional probability distribution of its parent node. The formula is as follows: P(X i |P a (X i ))=P(X i |X1,X2,…,X k ) Among them, X i is the fault phenomenon node, P a (X i ) as its parent node set.

5. The method according to claim 1, characterized in that The weight of the dynamic causal graph is dynamically adjusted by the Bayesian update method, and the formula is as follows: Among them, θ is the parameter of the causal graph, and D is the observation data of the device.

6. The method according to claim 1, characterized in that The fault chain analysis adopts the maximum a posteriori (MAP) method to perform reasoning and identify the most likely path leading to the current fault phenomenon.

7. The method according to claim 1, characterized in that The multi-step repair suggestion includes: (1) a direct treatment method for key fault nodes; (2) relevant steps to check for sensor false alarms or other potential problems; and (3) additional suggestions for further testing of relevant parts of the equipment.

8. The method according to claim 1, characterized in that The dynamic feedback and self-optimization optimizes the reasoning strategy through reinforcement learning, and uses the Q learning algorithm to update the Q value according to the feedback data. The formula is as follows: Among them, s is the current state, a is the action taken, r is the feedback reward, and γ is the discount factor.

9. The method according to claim 1, characterized in that: The construction of the dynamic causal graph can be combined with an expert knowledge base to construct an initial causal relationship network, or the inference of causal relationships can be optimized through a Markov Chain Monte Carlo (MCMC) method.

10. A dynamic causal graph intelligent fault diagnosis and maintenance system based on multimodal data fusion, comprising: Data collection and perception module: This module is responsible for obtaining the operating status information of the equipment from multiple data sources (including real-time sensor data, historical fault record data, and external environment data). Through standardized perception and collection interfaces, it ensures the integrity and timeliness of the data, laying the foundation for subsequent analysis and diagnosis. Data preprocessing and cleaning module: In this module, the collected multimodal data is preprocessed and cleaned, including unified scale standardization of the data, missing value filling (such as interpolation or mean filling), and the removal of noise data and outliers through anomaly detection algorithms, so as to ensure the reliability and consistency of the input data quality; Multimodal data fusion module: This module uses deep learning cross-modal embedding technology to integrate various types of feature information such as sensor data, historical fault data, and external environment data into a unified high-dimensional feature vector representation. It selects the modal features most relevant to the current diagnostic task through the attention mechanism, improves the accuracy and efficiency of data fusion, and provides rich and effective input features for causal analysis; Dynamic causal graph construction module: In this module, the causal relationship graph is dynamically constructed and optimized by using the directed acyclic graph (DAG) structure, expert knowledge, MCMC sampling and Bayesian network technology. The graph is used to represent the causal relationship and weight between different fault phenomena, and can be dynamically updated as the equipment operating status and data input change; Fault chain reasoning and analysis module: Based on the constructed dynamic causal graph, this module conducts correlation analysis on multiple fault phenomena to find the core problem chain that causes equipment abnormality. Through reasoning methods such as maximum a posteriori inference (MAP) or variational inference (VI), this module identifies key fault nodes and potential fault sources, providing a basis for further maintenance strategies; Multi-step maintenance suggestion generation module: This module provides targeted multi-step maintenance guidance and suggestions for the equipment based on the fault chain reasoning results, including the repair sequence of key fault nodes, the inspection and replacement steps of related components, and the troubleshooting plan for potential secondary faults, providing high value-added decision support for front-line engineers or automated maintenance systems; Dynamic feedback and self-optimization module: After the actual maintenance operation is performed, this module is responsible for collecting feedback data from the equipment and using it to update and optimize the causal graph model and reasoning strategy. Through reinforcement learning (RL) method, strategy optimization is performed, so that the system can continuously improve itself in long-term operation, and improve the accuracy and practicality of subsequent diagnosis and maintenance suggestions.

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