Intelligent fault diagnosis method and system for portal crane

Abstract

The invention provides an intelligent fault diagnosis method and system for a portal crane. The method comprises the following steps: acquiring real-time working condition data and strain monitoring data of the portal crane; the data is preprocessed; carrying out trend analysis by adopting a comparative analysis algorithm based on the actual strain monitoring data and the predicted strain data; carrying out trend analysis by adopting a comparative analysis algorithm based on the actual stress and the theoretical stress; establishing a fault decision tree based on the trend analysis result, man-made fault judgment and fault feature data of a historical fault library; based on a strain signal trend analysis result, combining a fault decision tree and a fault knowledge graph, and constructing a fault diagnosis algorithm to carry out fault diagnosis on the main beam; and updating the fault decision tree based on the diagnosis result data and the early warning result data. The equipment state of the portal crane can be monitored in real time, fault diagnosis and intelligent early warning can be carried out on the equipment according to the collected data, and faults can be repaired in time, so that the safety of the portal crane during production is improved, operation accidents of the portal crane are avoided, and the working efficiency of the portal crane is improved.

Description

Technical Field

[0001] The present invention relates to the field of special equipment operation and maintenance, and in particular to an intelligent fault diagnosis method and system for a gantry crane. Background Art

[0002] With the advancement of my country's infrastructure construction and the overall development of the heavy industry in the national economy, gantry cranes have become an important equipment for the rapid development of the national economy and have been widely used in many national economic sectors. As gantry cranes develop towards large-scale, high-speed and automated directions, the safety of gantry cranes has received more and more attention.

[0003] The gantry cranes in the prior art lack effective and timely safety monitoring means, making it difficult to detect and diagnose gantry crane faults in a timely manner. Therefore, the gantry cranes cannot be repaired in a timely manner according to the faults, and the occurrence of safety accidents cannot be avoided in a timely manner.

[0004] Therefore, a solution is needed to perform real-time fault diagnosis on gantry cranes. Summary of the Invention

[0005] One purpose of the present application is to provide an intelligent fault diagnosis method and system for a gantry crane, so as to solve the problem that it is difficult to diagnose faults in the real-time working process of a gantry crane under the existing technology.

[0006] To achieve the above objectives, some embodiments of the present application provide a method for intelligent fault diagnosis of a gantry crane, the method comprising the following steps:

[0007] Step 1. Collecting real-time operating condition data and strain monitoring data of the gantry crane;

[0008] Step 2. Preprocessing the collected working condition data and strain monitoring data;

[0009] Step 3. Perform trend analysis based on the strain monitoring data and the predicted strain data using a comparative analysis algorithm;

[0010] Step 4. Calculate the actual stress based on the strain monitoring data, and perform trend analysis on the actual stress and theoretical stress using a comparative analysis algorithm;

[0011] Step 5. Establish a fault decision tree based on the trend analysis results of steps 3 and 4, manual fault judgment, and fault feature data in the historical fault library;

[0012] Step 6. Based on the trend analysis results and in combination with the fault decision tree and the strain signal fault knowledge graph, a fault diagnosis algorithm is constructed to perform fault diagnosis;

[0013] Step 7: Update the fault decision tree based on the diagnosis result data and the early warning result data.

[0014] Furthermore, collecting the strain monitoring data in step 1 further includes: distributing the measuring points of the strain sensors on the two main beams of the gantry crane, arranging 3 measuring points on each main beam, and arranging a total of 6 strain sensors for monitoring the stress of the main beams, and the 6 strain sensors are installed on the lower panel inside the main beam box; and, arranging 2 strain sensors for temperature compensation, and the strain sensors for temperature compensation are installed on a movable mounting block near the strain sensor at the middle position of the main beam.

[0015] Furthermore, the preprocessing in step 2 includes cleaning, correcting or filtering the data.

[0016] Furthermore, step 3 further includes: training a prediction model through the XGBoost algorithm based on the pre-processed real-time operating data and strain monitoring data; processing the historical operating data for the strain sensor signal and using it for model training; the trained model uses the data collected by the programmable logic controller PLC as input, finds the relationship between the PLC data and the strain sensor signal value based on the historical operating data, predicts the signal data of the specified sensor, and then calculates the predicted value with the strain monitoring data collected by the strain sensor in actual work, uses the time series residual as the judgment criterion, and issues an alarm if an abnormality continues to occur within a certain time series.

[0017] Furthermore, step 4 further includes: comparing the theoretical stress and actual stress of the gantry crane main beam to mutually verify the correctness of the numerical values; after mutual verification of rationality, the theoretical stress is used as a reference value of the actual stress to monitor and warn the main beam strain data.

[0018] Furthermore, constructing the fault decision tree in step 5 includes the following steps:

[0019] 1) Using the fault type in the historical fault data as a label and associating it with the corresponding strain monitoring data and sensor characteristic values, determining the fault type in the historical case and labeling it;

[0020] 2) Analyzing sensor data and historical fault records using the historical fault data to identify common fault modes, determine the range of characteristic values ​​corresponding to the common fault modes, and classify these characteristics into different fault types, and associating the extracted signal characteristic values ​​with the labels of the fault types;

[0021] 3) Build a fault decision tree

[0022] ① Design the hierarchical structure of the decision tree

[0023] First-layer node: Is there an anomaly?

[0024] By comparing the signal characteristic value with the standard value under normal operating conditions, it is determined whether there is an abnormality; if the amplitude or frequency of the vibration signal exceeds the normal range, it is determined to be "abnormal", otherwise it is "normal";

[0025] Second-layer nodes: preliminary classification of fault types

[0026] Preliminarily classifying abnormal situations based on different characteristics of the sensor data;

[0027] The third layer node: refined classification

[0028] By further analyzing the specific characteristic patterns, the fault types can be subdivided;

[0029] ②Set decision conditions and thresholds

[0030] Setting feature thresholds: determining a decision criterion for each node based on the historical fault data;

[0031] Decision-making based on multi-parameter correlation: For faults associated with multiple parameters, decision-making conditions are established by combining the changes in multiple signals;

[0032] ③Build relationships between nodes

[0033] According to the above conditions, the branch structure of the fault decision tree is gradually constructed; each node makes a judgment based on the specific characteristic value of the sensor and enters the corresponding branch;

[0034] 4) Verify and optimize the fault decision tree

[0035] ①Model verification

[0036] Input the collected historical fault data or new data into the fault decision tree model to verify the accuracy of its fault classification. If the classification accuracy is not high, it is necessary to adjust the threshold of the feature value or redesign the branch logic;

[0037] ②Optimize fault decision tree

[0038] The fault decision tree is optimized through the following three methods: i) Dynamically adjust the threshold: Based on more historical fault data, continuously adjust the various feature thresholds set in the fault decision tree; ii) Add new nodes: As the fault library expands, continuously add new fault types and new judgment nodes; iii) Remove redundant branches: If some branch judgments are no longer applicable or the fault mode changes, simplify or remove redundant branches.

[0039] Furthermore, the construction process of the fault knowledge graph in step 6 includes:

[0040] S1. Knowledge acquisition and ontology model construction:

[0041] ① Knowledge acquisition: Extracting knowledge from various data sources related to crane fault diagnosis, including fault types, fault handling methods, professional maintenance experience, and equipment technical manuals. This knowledge provides detailed background and handling measures for various crane faults.

[0042] ② Ontology model construction: Construct a gantry crane fault diagnosis ontology model and define the core concepts and relationships in the graph;

[0043] ③Graph data storage: convert the ontology model into structured graph data and store it in the fault knowledge graph database to support subsequent fault diagnosis reasoning and knowledge retrieval;

[0044] S2. Preliminary construction and visualization of the map:

[0045] ① Relationship entity mapping: From the collected knowledge and data, fault cases, diagnostic processes, and equipment components are mapped into nodes and relationships in the knowledge graph;

[0046] ② Hierarchical relationship construction: hierarchical relationships are established in the fault knowledge graph according to fault types, so that fault nodes at different levels can reflect the correlation between fault symptoms and root causes, and from treatment methods to spare parts requirements;

[0047] ③ Visualization: Generate a visualization of the fault knowledge graph, clearly presenting the core nodes and important relationships;

[0048] S3. Fault knowledge graph update mechanism:

[0049] ① Trend change determination and knowledge graph update: Operation and maintenance personnel analyze the causes of fault trend changes based on the equipment's operating trends and diagnostic data, and add the diagnostic results to the knowledge graph. By recording the cause analysis and treatment methods, the knowledge graph's ability to identify and respond to trend faults is improved.

[0050] ② Manual judgment and trend update: In the early stage, operation and maintenance experts manually judge trend changes and update the nodes or relationships of the knowledge graph;

[0051] ③ Manual and algorithmic updates: In the later stages, expert judgment is combined with diagnostic algorithms for updates. When the algorithm identifies an abnormal trend, it automatically updates the knowledge graph and refines the nodes and relationships after manual confirmation, gradually reducing manual involvement.

[0052] S4. Recording and processing of diagnostic algorithm output information:

[0053] ① Information flow reception and storage: The knowledge graph framework receives the key fault information flow output by the diagnostic algorithm, including detected anomalies, predicted fault types, and possible causes;

[0054] ② Graph update of important information: Map the key information output by the algorithm to the graph nodes. If a new fault mode or new treatment method is detected, add or modify nodes and relationships in the graph to improve the fault diagnosis content of the graph;

[0055] ③ Automatic update of anomalies: When the algorithm identifies a new anomaly pattern, the system automatically generates a corresponding anomaly node in the graph and associates it with the relevant equipment and fault type;

[0056] S5. Knowledge graph training and self-learning:

[0057] ① Automatic graph training: The updated graph data is used to train and optimize the fault diagnosis model, forming a closed loop. The diagnostic algorithm improves the recognition rate of new fault types based on the structured data of the graph.

[0058] ② Self-learning mechanism: Through the continuous accumulation of fault handling cases and experience data in the atlas, the diagnostic algorithm gradually achieves self-learning, which can more quickly identify new faults and adjust maintenance strategies;

[0059] ③ Manual supervision and feedback: The self-learning process of the atlas still requires manual supervision, and the automatically updated content of the atlas is regularly checked to ensure the diagnostic accuracy of the model;

[0060] S6. Continuous optimization and application of knowledge graphs:

[0061] ① Fault prediction and reasoning: Through searching and reasoning on the knowledge graph, the system can predict potential faults, assist operation and maintenance personnel in early troubleshooting, and automatically recommend treatment methods based on historical data;

[0062] ② Intelligent operation and maintenance decision support: The knowledge graph integrates the fault information and working condition data of all crane equipment, supporting the operation and maintenance team to make quick decisions and improve fault handling efficiency;

[0063] ③Model visualization optimization: Through the continuous optimization of the visualization map, complex relationships are made intuitive, which helps further operation and maintenance personnel training and knowledge sharing.

[0064] Furthermore, building the fault diagnosis algorithm in step 6 includes: combining a Bayesian classifier, a support vector machine, and a fault decision tree for fault diagnosis, wherein the combination of the three provides a basis for node selection or probability inference in the fault decision tree;

[0065] The Bayesian classifier calculates the posterior probability of a node object using the Bayesian formula through the prior probability of the node object, and selects the class with the maximum posterior probability as the class to which the object belongs. The probabilistic reasoning of the Bayesian classifier can smooth the decision-making process and provide a probabilistic basis for fault classification, making the fault decision tree more robust.

[0066] The support vector machine uses a multi-classification expansion algorithm to perform fault diagnosis on multiple faults, and improves the classification accuracy of the fault decision tree by processing high-dimensional data and nonlinear relationships; the support vector machine can also reduce the risk of overfitting through maximum margin optimization.

[0067] Furthermore, updating the fault decision tree in step 7 includes the following steps:

[0068] Ss1. Structural adjustment of fault decision tree

[0069] 1) Introducing new features: Introducing new feature nodes based on the diagnosis results and warning result data;

[0070] 2) Weight adjustment: For existing decision nodes, the diagnostic and warning result data are used to update the weight or probability of the decision nodes; statistical methods based on historical data are used to adjust the failure probability of nodes in the decision tree based on the existing data;

[0071] 3) Pruning redundant nodes: If the diagnosis and warning results indicate that the probability of certain faults is extremely low, or the related faults are no longer important in decision-making, these nodes are appropriately pruned in the fault decision tree to simplify the model;

[0072] Ss2. Rule Update

[0073] 1) Rule improvement based on actual failure patterns: When diagnostic data reveals new failure patterns or patterns that differ from existing ones, the rules in the fault decision tree are updated based on these patterns. A rule is a criterion or condition defined at each node of the fault decision tree, which determines the path branching and ultimately leads to a specific fault classification or decision.

[0074] 2) Threshold update guided by early warning information: The early warning result data reveals early signs of failure and is used to update the thresholds of relevant nodes in the fault decision tree, enabling it to identify potential failures earlier;

[0075] Ss3. Model optimization and verification

[0076] 1) Training and testing: combining the updated fault decision tree with new diagnosis and warning results data, training the model and testing it, using cross-validation to evaluate its accuracy and robustness;

[0077] 2) Real-time update: With the continuous input of new diagnostic results and warning result data, the structure and parameters of the fault decision tree are continuously updated.

[0078] On the other hand, an intelligent fault diagnosis system for a gantry crane is provided, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the system is triggered to execute the aforementioned diagnostic method. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a diagram showing the arrangement of strain measurement points on the main beam of a gantry crane according to the present invention;

[0080] Figure 2 Schematic diagram showing the comparison between the predicted value and the actual value of the gantry crane strain sensor signal of the present invention;

[0081] Figure 3 Schematic diagram comparing the theoretical stress and actual stress of the gantry crane main beam of the present invention. DETAILED DESCRIPTION

[0082] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0083] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0084] Here, the intelligent fault diagnosis method and system for gantry cranes of the embodiments of the present application are suitable for scenarios where real-time fault monitoring and diagnosis of the working process of the gantry crane are performed.

[0085] In this scenario, the gantry crane may experience real-time failures during operation. If the failures are not handled in a timely manner, it may cause gantry crane accidents, which will not only affect the normal operation of the gantry crane, but may also cause significant personal or property losses.

[0086] The gantry crane fault diagnosis method provided in the embodiment of the present application can collect the working status data of the gantry crane in real time, pre-process the collected real-time working data, perform trend analysis on the pre-processed data, analyze the cause of the trend change based on the trend analysis results, make fault judgments based on the cause analysis results and the historical fault library, establish a fault decision tree, and perform fault diagnosis and intelligent early warning based on the fault knowledge graph and signal trend changes of the fault decision tree, thereby improving the timeliness of fault monitoring and diagnosis during the operation of the gantry crane, timely discovering related faults of the gantry crane, so that timely countermeasures can be taken to the fault and the occurrence of gantry crane accidents can be avoided.

[0087] Some embodiments of the present application provide a method for intelligent fault diagnosis of a gantry crane, the method comprising the following steps:

[0088] Step 1. Collect real-time operating data and strain monitoring data of the gantry crane.

[0089] For example, this embodiment installs strain sensors at key locations on the gantry crane's main beam structure to monitor stress trends and, in turn, its operating status. Real-time operating data for a gantry crane refers to information related to the crane's operating conditions; strain monitoring data for a gantry crane refers to strain monitoring data on the operating status of the main beam acquired by sensors deployed on the gantry crane's main beam.

[0090] The relationship between gantry crane girder deflection and girder panel stress changes is well understood: Gantry crane girder deflection is typically caused by long-term use or overloading, which can lead to girder deformation. This deformation can cause stress changes in the girder structure, leading to stress concentration. Stress concentration can damage or even fracture the girder. Therefore, it is necessary to monitor stress trends at key locations on the gantry crane girder structure to ensure safe operation.

[0091] When installing strain sensors, the layout of the sensors must fully consider the stress characteristics of the main beam and select key locations where deflection is likely to occur for installation. Generally speaking, the mid-span of the main beam and near the support points at both ends are key areas for deflection monitoring. The deflection at these locations is relatively large and has a significant impact on the stability of the overall structure. As the main component that bears the load of the gantry crane, the main beam panel has a complex stress distribution. Therefore, stress sensors need to be installed at multiple key locations to fully monitor its stress state. Common monitoring locations include the connection between the panel and the upper and lower flange plates of the main beam, the middle of the panel, and possible stress concentration areas.

[0092] When arranging sensors, the following points should also be noted: First, the installation location of the sensor should be convenient for data collection and monitoring, and avoid interference from other equipment or components; second, the sensor should have good durability and stability, and be able to operate stably for a long time in harsh working environments; finally, the layout of the sensor should follow certain specifications and standards to ensure the accuracy and reliability of the monitoring results.

[0093] Taking the above factors into consideration, in this embodiment, the measuring points of the strain sensors are distributed on the two main beams, such as Figure 1 As shown, three measuring points are placed on each main beam, for a total of six strain sensors. In addition, two strain sensors are required for temperature compensation. The six strain sensors for monitoring main beam stress are mounted on the lower panel inside the main beam box. The two strain sensors for temperature compensation are installed near the strain sensor at the center of the main beam, located at S / 2, on a removable mounting block. This strategically placed sensor arrangement allows for real-time monitoring of the gantry crane's operating status, enabling the timely identification and resolution of potential issues and ensuring safe and stable operation.

[0094] Step 2. Preprocess the collected operating condition data and monitoring data.

[0095] In some embodiments, the sampling frequency of the strain monitoring data of the main beam is 1 Hz, and the sampling frequency of the working condition data is 1 Hz.

[0096] During the preprocessing of main beam strain monitoring data, a series of data cleaning and correction tasks are required. First, the collected raw data must be checked to eliminate outliers caused by equipment failure, sensor errors, or data transmission errors. These outliers may cause deviations in data analysis and therefore need to be eliminated or corrected.

[0097] Next, to better understand the strain characteristics of the main beam, the data was divided into different time periods according to the crane's operating status and load conditions. Statistical analysis was performed to discover the changing patterns of the main beam strain under different working conditions, providing a basis for subsequent structural analysis and optimization.

[0098] In addition, the collected data is filtered to eliminate noise and interference signals and improve the signal-to-noise ratio of the data.

[0099] Through the data preprocessing in the above steps, more accurate and reliable main beam strain data can be obtained, providing strong support for subsequent structural analysis and optimization.

[0100] Step 3. Perform trend analysis based on the actual strain monitoring data and the predicted strain data using a comparative analysis algorithm.

[0101] Based on pre-processed real-time operating data and actual strain monitoring data, a machine learning algorithm is used to perform trend analysis of the main beam strain signal. The collected operating data and strain monitoring data are processed and analyzed, and a machine learning algorithm is used to provide intelligent early warning of abnormal strain signals in the gantry crane main beam.

[0102] In some embodiments, the prediction model is trained by the XGBoost algorithm based on the real-time working condition data and strain monitoring data collected on the large gantry crane. The XGBoost algorithm is a machine learning algorithm that is widely used in big data processing. The full name of XGBoost is eXtreme Gradient Boosting. The biggest feature of XGBoost is that it can automatically utilize the multi-threading of the CPU for parallel processing, while improving the algorithm to improve accuracy. XGBoost performs a second-order Taylor expansion on the loss function and adds a regularization term to the loss function to find the optimal solution as a whole, in order to balance the decrease in the loss function and the complexity of the model to avoid overfitting. The XGBoost algorithm uses a second-order Taylor expansion to approximate the objective function, so that it can perform leaf splitting optimization based only on the value of the input data, which improves scalability and freedom, and can construct a corresponding error function according to the specific application scenario to train a specific tree model.

[0103] The algorithm flow of XGBoost is as follows:

[0104] S1: Initialize the predicted value of each sample;

[0105] S2: Define a specific loss function;

[0106] S3: Calculate the derivative of the loss function for each sample prediction value;

[0107] S4: Build a new decision tree based on the derivative information;

[0108] S5: Use the new decision tree to predict the sample value and add it to the original value;

[0109] S6: Loop to create decision trees until the loss function meets the conditions.

[0110] For the strain sensor signal, the historical operation data is processed and used for model training. The trained model uses PLC data as input, finds the relationship between the PLC and the strain sensor signal value based on historical data, predicts the signal data of the specified sensor, and then calculates its predicted value with the data value collected by the strain sensor in actual work. The time series residual is used as the judgment standard. If anomalies continue to occur within a certain time series, an alarm will be issued. This alarm signal reflects the difference between the current equipment condition of the main beam of a large gantry crane and the equipment condition when it is healthy. The comparison between the predicted value and the actual value of the strain sensor signal is as follows. Figure 2 shown.

[0111] In some embodiments, the data collected by the programmable logic controller (PLC) includes operating data such as the weight of the lifting mechanism, lifting height, lifting speed, trolley running speed, trolley running range, trolley running speed, and trolley running range.

[0112] Step 4. Perform trend analysis based on the actual stress and theoretical stress using a comparative analysis algorithm.

[0113] By collecting equipment operating status data and monitoring sensor data, we record structural component strain information under all operating conditions. We then develop a comparative analysis algorithm for the actual and theoretical stresses of gantry crane main beams based on sensor data. We also conduct big data analysis and prediction of main beam stress trends. We use historical data to determine the current deformation state, verifying the main structure's condition from two dimensions. We provide instantaneous response warnings for sudden changes in variables, thereby extending equipment life and ensuring production safety.

[0114] It can be understood that the actual structural strain ε (dimensionless, usually expressed as microstrain or percentage) data measured by the strain sensor can be calculated based on the elastic modulus E (unit: Pa, or N / m 2 ) calculate the stress σ (unit: Pa, or N / m 2 In dynamic load monitoring, strain data is converted into stress in real time via the elastic modulus, enabling real-time stress trend analysis. Within the elastic range, stress and strain satisfy the following relationship: σ = E·ε.

[0115] 1) Verification of theoretical strain value of gantry crane main beam

[0116] When designing gantry cranes, the main girder stress must be verified according to extreme operating conditions. Therefore, it is necessary to compare the theoretical and actual stresses of the main girder to verify the correctness of the values. Once the theoretical stress is verified to be reasonable, it can be used as a reference value for the actual stress, enabling monitoring and early warning of main girder strain data.

[0117] 2) Actual stress of main beam

[0118] In some embodiments, strain sensors are installed on the bottom plate of a gantry crane's main beam along its extension direction. These sensors are installed after the crane is already operational, making it impossible to measure strain due to the main beam's own weight. Therefore, the collected data is microstrain (i.e., tensile strain) along the main beam caused by the moving load. After accounting for temperature compensation, these strain data, combined with the elastic modulus, are converted to actual stress for subsequent data analysis.

[0119] 3) Main beam theoretical stress

[0120] In some embodiments, the purpose of calculating the theoretical stress of a gantry crane's main beam is to compare it with the actual stress in the crane's gantry beam and verify the actual stress value. To maintain consistency in the analysis, only the tensile stress caused by the moving load is considered (hereinafter referred to as the "theoretical stress of the main beam"). The main beam has upper and lower trolleys, which contain the lifting mechanism and hook-shift mechanism. The load on the main beam includes the trolley's own weight and the weight of the hoisted load, which acts on the main beam through the trolley's wheels. Therefore, the stress of the main beam is related to operating conditions such as the trolley's position, lifting load, and hook-shift distance.

[0121] 4) Verification of theoretical value and actual value

[0122] The overall trend of the theoretical stress and actual stress of the gantry crane main beam is consistent, but they are not completely parallel. Figure 3 The correlation coefficient between the two is checked to test the correlation between theory and practice, so as to achieve the purpose of mutual verification between theoretical stress and actual stress.

[0123] Step 5. Establish a fault decision tree based on the trend analysis results of steps 3 and 4, human fault judgment and fault feature data in the historical fault library.

[0124] In some embodiments, constructing a fault decision tree includes the following steps:

[0125] 1) Use the fault types in historical fault data as labels and associate them with the corresponding sensor data and features. Expert experience is used to manually identify the fault types in certain historical cases and label them, to compensate for the inadequacy of labeling based solely on sensor data.

[0126] 2) By analyzing sensor data and historical fault records based on historical data and expert judgment, common fault modes are identified. The characteristic value ranges corresponding to common fault modes are determined, and these characteristics are grouped into different fault types. The extracted signal characteristic values ​​are then associated with the fault type.

[0127] 3) Build a fault decision tree

[0128] ① Design the hierarchical structure of the decision tree

[0129] First-layer node: Is there an anomaly?

[0130] By comparing the signal characteristic values ​​with the standard values ​​under normal operating conditions, it is determined whether there is an abnormality. For example, if the amplitude or frequency of the vibration signal exceeds the normal range, it is judged as "abnormal", otherwise it is "normal".

[0131] Second-layer nodes: preliminary classification of fault types

[0132] Based on the different characteristics of sensor data, perform preliminary classification of abnormal situations. For example:

[0133] Abnormal vibration → mechanical failure (such as bearings, gears, transmission mechanisms, etc.).

[0134] Abnormal current and voltage → electrical failure (such as motor, electrical control system).

[0135] Abnormal temperature → overheating or poor lubrication failure.

[0136] The third layer node: refined classification

[0137] By further analyzing the specific characteristic patterns, the fault types can be subdivided. For example:

[0138] Abnormal vibration:

[0139] High frequency vibration → bearing failure.

[0140] Low-frequency, large-amplitude vibration → mechanical structure is loose or unbalanced.

[0141] Electrical anomalies:

[0142] Frequent current fluctuations → motor overload.

[0143] Unstable voltage → control system failure.

[0144] ②Set decision conditions and thresholds

[0145] Set feature thresholds: Determine the decision criteria for each node based on historical data and expert experience. For example:

[0146] When the vibration amplitude exceeds a certain set value, it enters the "abnormal vibration" branch.

[0147] When the current rise rate exceeds the threshold, it enters the "Electrical Fault" branch.

[0148] Decision-making based on multi-parameter correlation: For faults involving multiple parameters, decision-making conditions can be established by combining the changes in multiple signals. For example, if temperature rise and abnormal vibration occur simultaneously, it can be further determined to be "overheating and mechanical wear caused by poor lubrication."

[0149] ③Build relationships between nodes

[0150] Based on the above conditions, the branch structure of the fault decision tree is gradually constructed. Each node makes a judgment based on the specific sensor feature value and enters the corresponding branch. For example:

[0151] Abnormal vibration → high frequency vibration → bearing failure.

[0152] Abnormal electrical signal → frequent current fluctuations → motor failure.

[0153] 4) Verify and optimize the decision tree

[0154] ①Model verification

[0155] Input the collected historical data or new data into the decision tree model to verify the accuracy of its fault classification. If the classification accuracy is not high, you need to adjust the feature value threshold or redesign the branch logic.

[0156] ②Optimize decision tree

[0157] Optimize the decision tree by:

[0158] Dynamically adjust thresholds: Continuously adjust the feature thresholds set in the fault decision tree based on more historical data and expert feedback.

[0159] Adding new nodes: As the fault library expands, new fault types and new judgment nodes can be continuously added.

[0160] Remove redundant branches: If certain branch decisions are no longer applicable or the failure mode changes, redundant branches can be simplified or removed.

[0161] ③Expert adjustment and feedback

[0162] Regularly communicate with experts to provide feedback on the diagnostic results in the decision tree. Combined with the fault types and characteristics in actual cases, the decision tree structure is further improved.

[0163] Step 6. Based on the strain signal trend analysis results, combined with the fault decision tree and strain signal fault knowledge graph, a fault diagnosis algorithm is built to perform fault diagnosis.

[0164] The process of building a crane fault diagnosis knowledge graph includes multiple steps to systematically and structuredly manage fault knowledge and enable automatic updates and intelligent applications. The following is a more detailed construction process:

[0165] (1) Knowledge acquisition and ontology model construction

[0166] Knowledge Collection: Extracts knowledge from various data sources related to crane fault diagnosis, including fault types, troubleshooting methods, maintenance professionals' experience, equipment manuals, and other diagnostic documentation. This knowledge provides detailed background and treatment measures for various crane faults.

[0167] Ontology model construction: Build a gantry crane fault diagnosis ontology model and define the core concepts and relationships in the graph. For example, establish basic entities such as "fault type," "fault cause," "fault symptoms," "treatment method," and "spare parts," and define their relationships (e.g., "fault type - corresponding - treatment method," "treatment method - required - spare parts," etc.).

[0168] Graph data storage: Convert the ontology model into structured graph data and store it in the knowledge graph database to support subsequent fault diagnosis reasoning and knowledge retrieval.

[0169] (2) Preliminary construction and visualization of the map

[0170] Relational Entity Mapping: Based on the collected knowledge and data, fault cases, diagnostic processes, and equipment components are mapped into nodes and relationships in the knowledge graph. Typical nodes include "equipment component" (such as a pulley or bearing seat) and "fault result" (such as wear or loose bolts).

[0171] Hierarchical relationship construction: Establish hierarchical relationships in the graph according to fault types, so that fault nodes at different levels can reflect the relationship from fault symptoms to root causes, from treatment methods to spare parts requirements.

[0172] Visualization: Generates a visual view of the knowledge graph, clearly presenting core nodes and important relationships, helping operation and maintenance personnel understand failure modes and common treatment methods.

[0173] (3) Knowledge graph update mechanism

[0174] Trend change determination and knowledge graph update: Operations and maintenance personnel analyze the causes of fault trend changes based on equipment operating trends and diagnostic data, and then add the diagnostic results to the knowledge graph. By recording the cause analysis and treatment methods, the knowledge graph's ability to identify and respond to trending faults is enhanced.

[0175] Manual judgment and trend update: In the early stage, operation and maintenance experts manually judge trend changes and update the nodes (such as new fault causes) or relationships (such as new fault handling methods) of the knowledge graph.

[0176] Combination of manual and algorithmic updates: In the later stages, expert judgment is combined with diagnostic algorithms for updates. When the algorithm identifies abnormal trends, it automatically updates the knowledge graph and improves the nodes and relationships after manual confirmation, gradually reducing manual participation.

[0177] (4) Recording and processing of diagnostic algorithm output information

[0178] Information flow reception and storage: The knowledge graph framework receives the key fault information flows output by two diagnostic algorithms (such as machine learning-based and rule-based diagnostic algorithms), including detected anomalies, predicted fault types, and possible causes.

[0179] Graph update of important information: Map the key information output by the algorithm to the graph nodes. If a new failure mode or new treatment method is detected, add or modify nodes and relationships in the graph to improve the fault diagnosis content of the graph.

[0180] Automatic update of anomalies: When the algorithm identifies a new abnormal pattern (such as a new vibration frequency abnormality in the lifting mechanism), the system will automatically generate a corresponding abnormal node in the map and establish an association with the relevant equipment and fault type.

[0181] (5) Knowledge graph training and self-learning

[0182] Automatic graph training: Updated graph data is used to train and optimize the fault diagnosis model, forming a closed loop. The diagnostic algorithm improves the recognition rate of new fault types based on the structured data of the graph.

[0183] Self-learning mechanism: Through the continuous accumulation of fault handling cases and experience data in the atlas, the diagnostic algorithm gradually achieves self-learning, which can more quickly identify new faults and adjust maintenance strategies.

[0184] Manual supervision and feedback: The self-learning process of the atlas still requires manual supervision, and the automatically updated content of the atlas must be checked regularly to ensure the diagnostic accuracy of the model.

[0185] (6) Continuous optimization and application of knowledge graphs

[0186] Fault prediction and reasoning: Through searching and reasoning on knowledge graphs, the system can predict potential faults, assist operation and maintenance personnel in early troubleshooting, and automatically recommend treatment methods based on historical data.

[0187] Intelligent operation and maintenance decision support: The knowledge graph integrates the fault information and working condition data of all crane equipment, supporting the operation and maintenance team to make quick decisions and improve fault handling efficiency.

[0188] Model visualization optimization: Through continuous optimization of the visualization map, complex relationships are made intuitive, which helps further operation and maintenance personnel training and knowledge sharing.

[0189] Through the above steps, the crane fault knowledge graph has achieved closed-loop management from construction, updating to intelligent application, greatly improving the efficiency and accuracy of fault diagnosis.

[0190] Crane equipment failures are complex events, influenced by multiple factors, including mechanical components, electrical components, control systems, and servo systems. For a specific crane failure case, the various factors influencing the failure form a complex network relationship with the case itself. Similar events belonging to the same failure phenomenon form a vast knowledge network system. Knowledge graphs, as a direct representation and organization of knowledge relationships, facilitate the accumulation and reuse of knowledge and effectively analyze specific potential failures within these complex relationships.

[0191] Combining the data sources and characteristics of the crane failure field, a knowledge graph construction technology combining "top-down" and "bottom-up" approaches, a data-driven incremental ontology modeling method and a pattern-based knowledge mapping mechanism are adopted to organize rich diagnosis-related knowledge and complete the construction and application of the crane failure knowledge graph.

[0192] Based on the expert knowledge related to cranes and historical failure cases, the failure results, failure phenomena, failure modes, failure causes, impact range, etc. are built into the knowledge graph.

[0193] For example, in a fault knowledge graph, a fault decision tree can be used as a reasoning path in the graph. Entities and relationships in the knowledge graph can be simplified through the hierarchical logic of the fault decision tree.

[0194] As you can understand, a fault decision tree is a hierarchical structure based on logical branches, used to gradually narrow down the possible faults and ultimately determine the cause. Each node represents a decision, and each branch represents the possible outcomes of that decision. The fault decision tree has a tree-like structure, with the root node representing the initial state of the system and the leaf nodes representing the final fault diagnosis results. Through a series of conditional judgments, the data is gradually segmented until a specific fault type is identified.

[0195] Furthermore, the fault knowledge graph is a network structure based on knowledge representation and semantic associations, encompassing entities (such as equipment components, fault types, and signal characteristics) and the relationships between them. It is used to capture the connections between different concepts in a system and support complex fault reasoning and knowledge querying. The fault knowledge graph is a graph-like structure, with nodes representing knowledge entities (such as equipment, fault symptoms, fault causes, and characteristic signals), and edges representing relationships between them (such as "caused by" and "dependent on").

[0196] The entities and relationships in the fault knowledge graph are simplified through the hierarchical logic of the fault decision tree. For example:

[0197] Fault feature nodes such as "abnormal vibration", "current fluctuation", and "temperature increase" in the knowledge graph may be connected to specific "bearing failure" or "motor failure" through the logical branches of the decision tree.

[0198] Decision trees provide a fast reasoning method based on conditional judgments, which can be embedded in a more complex reasoning framework of the knowledge graph to handle structured and standardized fault decision-making processes.

[0199] You can first use the fault decision tree to quickly and preliminarily screen faults and identify the most likely fault type. Then, based on the knowledge graph, you can conduct more in-depth reasoning and verification on the screened faults, using the multi-source knowledge in the graph to infer the root cause behind the fault. For example:

[0200] The decision tree determines the vibration anomaly by analyzing the sensor data and preliminarily judges it as a "mechanical failure."

[0201] Then, the knowledge graph further associates historical data and equipment maintenance records to infer that the mechanical failure may be caused by long-term wear or poor lubrication.

[0202] For example, suppose a crane experiences abnormal vibration and elevated temperatures. Using sensor data, the system triggers a fault decision tree, initially identifying a possible mechanical failure. The knowledge graph then further infers that historical data suggests mechanical failures are often caused by bearing wear or insufficient lubrication. The system ultimately recommends checking for bearing wear or adding lubricant, and records this case path in the knowledge graph for subsequent reasoning and verification of similar failures.

[0203] This integrated model based on decision trees and knowledge graphs improves the efficiency and accuracy of intelligent fault diagnosis through rapid fault screening and multi-level reasoning, providing systematic support for the operation and maintenance of gantry cranes.

[0204] Based on the trend analysis results of the main structure (main beam) strain signal and the strain signal knowledge graph, a Bayesian network-based fault diagnosis algorithm was built to infer the causes and corresponding probabilities of trend changes, output diagnostic results, and feed back key information flows of the fault to the knowledge graph. This system implements intelligent early warning and diagnosis functions for the strain signals of the main beams of large cranes. The diagnostic results convey the abnormal location, abnormal time, signal evaluation value, warning status, fault results, failure mode, failure cause, fault phenomenon, treatment measures, and impact range.

[0205] In some embodiments, the process of building a Bayesian network-based fault diagnosis algorithm is as follows:

[0206] Fault diagnosis can actually be viewed as a pattern recognition problem: determining whether a device is functioning normally or experiencing a fault; and determining the fault type and location of a faulty device. In a sense, this is a classification problem. By combining Bayesian classifiers, support vector machines, and decision trees for fault diagnosis, the robustness and accuracy of classification can be improved, especially in scenarios with high levels of uncertainty and noise. This combination provides a basis for node selection and probabilistic inference in the decision tree.

[0207] (1) Bayesian classifier

[0208] The Bayesian classifier is the classifier with the lowest probability of classification error or the lowest average risk under a pre-given cost among various classifiers. Its design method is a basic statistical classification method.

[0209] The Bayesian classification principle uses the Bayesian formula to calculate the posterior probability (the probability that the object belongs to a certain class) from the prior probability of an object. The class with the highest posterior probability is then selected as the class to which the object belongs. In short, it comprehensively evaluates the most likely outcome.

[0210] (2) Support Vector Machine

[0211] Support Vector Machines (SVM) is a binary classification model that aims to find a hyperplane to segment samples. The principle of segmentation is to maximize the interval, which is ultimately converted into a convex quadratic programming problem to solve.

[0212] Support vector machines generally deal with binary classification problems, but there are usually more than two faults. Therefore, when support vector machines are used for fault diagnosis, it is necessary to consider the expansion algorithm of multi-classification problems.

[0213] The advantages of decision tree combined with Bayesian classifier and SVM:

[0214] ① Improve classification accuracy: SVM can improve classification accuracy by processing high-dimensional data and nonlinear relationships, while Bayesian classifier can provide a probabilistic basis for fault classification, making the decision tree more robust.

[0215] ② Handling complex fault modes: In fault diagnosis scenarios, fault modes often have complex feature spaces. SVM and Bayesian reasoning can help handle these complex modes.

[0216] ③ Avoid overfitting: The probabilistic reasoning of the Bayesian classifier can smooth the decision-making process, while SVM reduces the risk of overfitting through maximum margin optimization.

[0217] In actual applications, the diagnostic result return format is shown in Table 1, where: location: strain sensor No. 1; time: 2024 / 10 / 18, 13:00-13:09; signal evaluation value: 1.61871904034377; warning status: abnormal.

[0218] Table 1 Main beam strain intelligent diagnosis results

[0219]

[0220]

[0221] Step 7. Update the fault decision tree based on the diagnosis result data and the warning result data.

[0222] In some embodiments, the early warning result data relies on health assessment and trend prediction algorithms, and is obtained through evaluation methods such as trend analysis and remaining life prediction. It provides early warnings of possible failures of crane structures or important components within a period of time, and intervenes before the failure occurs, such as arranging preventive maintenance, planned maintenance, spare parts replacement, etc., thereby effectively extending the life of the equipment and avoiding sudden downtime.

[0223] (1) Structural adjustment of decision tree

[0224] 1) Introducing new features: Based on the diagnosis results and warning result data, new feature nodes can be introduced. For example, if the warning result data indicates that the possibility of a certain fault has increased, the warning feature can be added to the relevant node in the fault decision tree.

[0225] 2) Weight Adjustment: For existing decision nodes, diagnosis and warning results can be used to update the node weights or probabilities. Statistical methods based on historical data (such as Bayesian inference) can adjust the failure probability of nodes in the decision tree based on existing data.

[0226] 3) Pruning redundant nodes: If the warning results or diagnosis results show that the probability of certain faults is extremely low, or the related faults are no longer important in the decision-making, these nodes can be appropriately pruned in the decision tree to simplify the model.

[0227] (2) Rule Updates

[0228] 1) Rule Improvement Based on Actual Fault Patterns: Diagnostic data may reveal new fault patterns or differences from existing ones. Rules in the fault decision tree must be updated based on these patterns. A "rule" is a judgment criterion or condition defined at each node in the decision tree, which determines the path branching and ultimately leads to a specific fault classification or decision. Each rule corresponds to a specific judgment criterion, such as a threshold range, specific signal characteristics, or data indicators.

[0229] 2) Threshold update guided by early warning information: Early warning result data may reveal early signs of failure, which can be used to update the thresholds of relevant nodes in the fault decision tree, enabling it to identify potential failures earlier.

[0230] (3) Model optimization and verification

[0231] 1) Training and testing: The updated decision tree is used in conjunction with new diagnosis and warning result data to train and test the model, and its accuracy and robustness are evaluated using methods such as cross-validation.

[0232] 2) Real-time update: As new diagnostic results and warning information are continuously input, the structure and parameters of the decision tree will be continuously updated.

[0233] Some embodiments of the present application also provide an intelligent fault diagnosis system for a gantry crane, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the system is triggered to execute the aforementioned intelligent fault diagnosis method for a gantry crane.

[0234] In summary, the solution provided by this application can obtain the real-time working condition data and strain monitoring data of the gantry crane; and pre-process the data; use a comparative analysis algorithm based on the actual strain monitoring data and the predicted strain data to perform trend analysis; use a comparative analysis algorithm based on the actual stress and the theoretical stress to perform trend analysis; establish a fault decision tree based on the above trend analysis results, manual fault judgment and fault feature data of the historical fault library; based on the strain signal trend analysis results, combined with the fault decision tree and the fault knowledge graph, build a fault diagnosis algorithm to perform main beam fault diagnosis; update the fault decision tree based on the diagnosis result data and the warning result data. This application can monitor the equipment status of the gantry crane in real time, perform fault diagnosis and intelligent warning on the equipment based on the collected data, and repair the fault in a timely manner, thereby improving the safety of the gantry crane during production, avoiding the occurrence of gantry crane operation accidents, and improving the working efficiency of the gantry crane.

[0235] It should be noted that the application can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the application can be executed by a processor to realize the steps or functions described above. Similarly, the software program of the application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the application can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0236] In a typical configuration of the present application, the terminal and the network device each include one or more processors (CPU), input / output interfaces, network interfaces and memory.

[0237] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0238] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc-read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include non-transitory media such as modulated data signals and carrier waves.

[0239] In addition, a part of the present application may be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can call or provide the method and / or technical solution according to the present application through the operation of the computer. The program instructions for calling the method of the present application may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-carrying medium, and / or stored in a working memory of a computer device that runs according to the program instructions. Here, according to an embodiment of the present application, a device is included, which includes a memory for storing computer program instructions and a processor for executing program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run the method and / or technical solution based on the aforementioned multiple embodiments of the present application.

[0240] It is obvious to those skilled in the art that the present application is not limited to the details of the above-mentioned exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claim involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware.

Claims

1. An intelligent fault diagnosis method for a gantry crane, characterized in that: The following steps are involved: Step 1. Collecting real-time operating data and strain monitoring data of the gantry crane; Step 2. Preprocessing the collected working condition data and strain monitoring data; Step 3. Perform trend analysis based on the strain monitoring data and the predicted strain data using a comparative analysis algorithm; Step 4. Calculate the actual stress based on the strain monitoring data, and use a comparative analysis algorithm to compare the actual stress with the theoretical stress to perform trend analysis; Step 5. Establish a fault decision tree based on the trend analysis results of steps 3 and 4, manual fault judgment and fault feature data of the historical fault library; Step 6. Based on the trend analysis results and in combination with the fault decision tree and strain signal fault knowledge graph, a fault diagnosis algorithm is constructed to perform fault diagnosis; Step 7: Update the fault decision tree based on the diagnosis result data and the warning result data.

2. The method according to claim 1, characterized in that Collecting the strain monitoring data in the step 1 further includes: distributing the measuring points of the strain sensors on the two main beams of the gantry crane, arranging 3 measuring points on each main beam, and arranging a total of 6 strain sensors for monitoring the stress of the main beams, and the 6 strain sensors are installed on the lower panel inside the main beam box; and, arranging 2 strain sensors for temperature compensation, and the strain sensors for temperature compensation are installed on a movable mounting block near the strain sensor at the middle position of the main beam.

3. The method according to claim 1, characterized in that The preprocessing in step 2 includes cleaning, correcting or filtering the data.

4. The method according to claim 1, characterized in that: The step 3 further includes: training a prediction model through the XGBoost algorithm based on the pre-processed real-time operating data and strain monitoring data; processing the historical operating data for the strain sensor signal and using it for model training; the trained model uses the data collected by the programmable logic controller (PLC) as input, finds the relationship between the PLC data and the strain sensor signal value based on the historical operating data, predicts the signal data of the specified sensor, and then calculates the predicted value with the strain monitoring data collected by the strain sensor in actual work, uses the time series residual as the judgment standard, and issues an alarm if the abnormality continues to occur within a certain time series.

5. The method according to claim 1, characterized in that The step 4 further includes: comparing the actual stress and theoretical stress of the gantry crane main beam to mutually verify the correctness of the numerical values; after mutual verification of rationality, the theoretical stress is used as a reference value of the actual stress to monitor and warn the strain data of the main beam.

6. The method according to claim 1, characterized in that Constructing the fault decision tree in step 5 includes the following steps: 1) Using the fault type in the historical fault data as a label and associating it with the corresponding strain monitoring data and sensor characteristic values, determining the fault type in the historical case and labeling it; 2) Analyzing sensor data and historical fault records through the historical fault data, identifying common fault modes, determining the feature value range corresponding to the common fault modes, and classifying these features into different fault types, and associating the extracted signal feature values ​​with the labels of the fault types; 3) Build a fault decision tree ① Design the hierarchical structure of the decision tree First-layer node: Is there an abnormality? By comparing the signal characteristic value with the standard value under normal operating conditions, it is determined whether there is an abnormality; if the amplitude or frequency of the vibration signal exceeds the normal range, it is determined as "abnormality exists", otherwise it is "normal"; Second-layer nodes: preliminary classification of fault types Preliminarily classifying abnormal situations according to different characteristics of the sensor data; The third layer node: refined classification By further analyzing the specific characteristic patterns, the fault types can be subdivided; ②Set decision conditions and thresholds Setting feature thresholds: determining a decision criterion for each node based on the historical fault data; Decision-making based on multi-parameter correlation: For faults with multi-parameter correlation, decision-making conditions are established by combining the changes in multiple signals; ③Build relationships between nodes According to the above conditions, the branch structure of the fault decision tree is gradually constructed; each node makes a judgment according to the specific characteristic value of the sensor and enters the corresponding branch; 4) Verify and optimize the fault decision tree ①Model verification Input the collected historical fault data or new data into the fault decision tree model to verify the accuracy of its fault classification. If the classification accuracy is not high, it is necessary to adjust the threshold of the feature value or redesign the branch logic; ②Optimize the fault decision tree The fault decision tree is optimized in the following three ways: i) Dynamically adjust the threshold: According to more of the historical fault data, continuously adjust the various feature thresholds set in the fault decision tree; ii) Add new nodes: As the fault library expands, continuously add new fault types and new judgment nodes; iii) Remove redundant branches: If some branch judgments are no longer applicable or the fault mode changes, simplify or remove redundant branches.

7. The method according to claim 6, characterized in that The construction process of the fault knowledge graph in step 6 includes: S1. Knowledge acquisition and ontology model construction: ① Knowledge acquisition: Extract knowledge from various data sources related to crane fault diagnosis, including fault type, fault handling method, maintenance experience of professionals, and equipment technical manuals. The knowledge provides detailed background and handling measures for various crane faults; ② Ontology model construction: Construct the gantry crane fault diagnosis ontology model and define the core concepts and relationships in the graph; ③Graph data storage: convert the ontology model into structured graph data and store it in the database of the fault knowledge graph to support subsequent fault diagnosis reasoning and knowledge retrieval; S2. Preliminary construction and visualization of the map: ① Relationship entity mapping: From the collected knowledge and data, fault cases, diagnostic processes, and equipment components are mapped to nodes and relationships in the knowledge graph; ② Hierarchical relationship construction: hierarchical relationships are established in the fault knowledge graph according to the fault type, so that the fault nodes at different levels reflect the correlation relationship from fault symptoms to root causes, from treatment methods to spare parts requirements; ③ Visualization display: Generate a visualization view of the fault knowledge graph to clearly present the core nodes and important relationships; S3. Fault knowledge graph update mechanism: ① Trend change determination and knowledge graph update: Operation and maintenance personnel analyze the causes of fault trend changes based on the equipment's operating trends and diagnostic data, and add the diagnostic results to the knowledge graph; by recording the cause analysis and treatment methods, the knowledge graph's ability to identify and respond to trend faults is improved; ② Manual judgment and trend update: In the early stage, operation and maintenance experts manually judge trend changes and update the nodes or relationships of the knowledge graph; ③ Manual and algorithmic updates: In the later stage, expert judgment is combined with diagnostic algorithms for updates. When the algorithm identifies abnormal trends, the knowledge graph is automatically updated, and nodes and relationships are improved after manual confirmation, gradually reducing manual participation; S4. Recording and processing of diagnostic algorithm output information: ① Information flow reception and storage: The knowledge graph framework receives the key fault information flow output by the diagnosis algorithm, including detected anomalies, predicted fault types and possible causes; ② Graph update of important information: Map the key information output by the algorithm to the graph nodes. If a new fault mode or new processing method is detected, add or modify nodes and relationships in the graph to improve the fault diagnosis content of the graph; ③ Automatic update of anomalies: When the algorithm identifies a new abnormal pattern, the system will automatically generate a corresponding abnormal node in the graph and establish associations with related equipment and fault types; S5. Knowledge graph training and self-learning: ① Automatic graph training: The updated graph data is used to train and optimize the fault diagnosis model to form a closed loop; the diagnosis algorithm will improve the recognition rate of new fault types based on the structured data of the graph; ② Self-learning mechanism: Through the fault handling cases and experience data continuously accumulated by the graph, the diagnostic algorithm gradually achieves self-learning, which can more quickly identify new faults and adjust maintenance strategies; ③ Manual supervision and feedback: The self-learning process of the graph still requires manual supervision, and the automatically updated content of the graph must be checked regularly to ensure the diagnostic accuracy of the model; S6. Continuous optimization and application of knowledge graph: ① Fault prediction and reasoning: Through the retrieval and reasoning of knowledge graphs, the system can predict potential faults, assist operation and maintenance personnel in early troubleshooting, and automatically recommend treatment methods based on historical data; ② Intelligent operation and maintenance decision support: The knowledge graph integrates the fault information and working condition data of all crane equipment, supports the operation and maintenance team to make quick decisions, and improves the efficiency of fault handling; ③Model visualization optimization: Through the continuous optimization of the visualization map, complex relationships can be visualized, which will help further operation and maintenance personnel training and knowledge sharing.

8. The method according to claim 7, characterized in that The fault diagnosis algorithm in step 6 includes: combining a Bayesian classifier, a support vector machine and a fault decision tree for fault diagnosis, wherein the combination of the three provides a basis for node selection or probability inference in the fault decision tree; The Bayesian classifier calculates the posterior probability of a node object using the Bayesian formula through the prior probability of the node object, and selects the class with the maximum posterior probability as the class to which the object belongs. The probabilistic reasoning of the Bayesian classifier can smooth the decision process and provide a probabilistic basis for fault classification, making the fault decision tree more robust. The support vector machine uses a multi-classification expansion algorithm to perform the fault diagnosis on multiple faults, and improves the classification accuracy of the fault decision tree by processing high-dimensional data and nonlinear relationships; the support vector machine can also reduce the risk of overfitting through maximum interval optimization.

9. The method according to claim 8, characterized in that The updating of the fault decision tree in step 7 includes the following steps: Ss1. Structural adjustment of fault decision tree 1) Introducing new features: Introducing new feature nodes based on the diagnosis results and warning result data; 2) Weight adjustment: For existing decision nodes, the diagnosis results and warning result data are used to update the weight or probability of the decision nodes; the failure probability of nodes in the decision tree is adjusted based on the existing data using statistical methods based on historical data; 3) Pruning redundant nodes: If the diagnosis results and warning result data indicate that the probability of occurrence of certain faults is extremely low, or the related faults are no longer important in decision-making, these nodes are appropriately pruned in the fault decision tree to simplify the model; SS2. Rule Update 1) Rule improvement based on actual fault patterns: When diagnostic data reveals new fault patterns or differences from existing fault patterns, the rules in the fault decision tree are updated according to these patterns; wherein the rules refer to the judgment criteria or conditions defined at each node of the fault decision tree, which are used to determine the branching of the path and ultimately lead to specific fault classification or decision; 2) Threshold update guided by early warning information: The early warning result data reveals early signs of faults and is used to update the thresholds of relevant nodes in the fault decision tree so that potential faults can be identified earlier; Ss3. Model optimization and verification 1) Training and testing: combining the updated fault decision tree with the new diagnosis results and warning results data, training the model and testing it, and using the cross-validation method to evaluate its accuracy and robustness; 2) Real-time update: With the continuous input of new diagnostic results and warning result data, the structure and parameters of the fault decision tree are continuously updated.

10. An intelligent fault diagnosis system for a gantry crane, the system comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein: When the computer program instructions are executed by the processor, the system is triggered to execute the method according to any one of claims 1 to 9.

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