Fault Warning Method and System for Non-uniform Distributed Fasteners in Complex Working Conditions

Abstract

The present application relates to the technical field of fault detection, and discloses a method and system for fault early warning of non-uniformly distributed fasteners under complex working conditions, including: obtaining stress data and image data of a device to be detected; identifying areas where fasteners are distributed in the device according to the stress data and image data to obtain multiple distribution areas of the fasteners; within each distribution area, combining the current state of the fastener and the influence of stress on the state of the fastener to predict the state of the fastener, and when a fault occurs in the state of the fastener, fault early warning is carried out; the present application combines the stress data and image data of the device under complex working conditions to obtain accurate position and state recognition results, and based on the prediction results of the stress change trend of the device, predicts the state change of the fasteners, can discover potential fault hazards in advance and carry out early warning, which helps to improve the timeliness and reliability of device fault early warning.

Description

Technical Field

[0001] This application relates to the technical field of fault detection, and particularly to a method and system for fault early warning of non-uniformly distributed fasteners under complex working conditions. Background Art

[0002] Current fastener fault early warning methods rely on real-time image data, with single data and single judgment methods, unable to predict faults in advance for early warning, unable to predict the time and type of fault occurrence in advance, and unable to ensure the timeliness and effectiveness of fault early warning.

[0003] For example, the Chinese patent application with the publication number CN117094952A discloses a method and device for fault detection of fasteners on a port crane. Among them, the method obtains a denoised port crane fastener image by denoising the acquired port crane fastener image, locates the fastener area in the denoised port crane fastener image through the YOLO v5s algorithm to obtain the fastener area image, performs image enhancement processing on it to obtain the enhanced fastener area image, and inputs it into a fault detection model constructed based on the YOLOv5s algorithm for fault detection, thereby outputting the fault detection result.

[0004] The above prior art has the problems raised in this background art: single data, single judgment method, unable to predict faults in advance for early warning, unable to predict the time and type of fault occurrence in advance, and unable to ensure the timeliness and effectiveness of fault early warning; to solve at least one of the above problems, this application proposes a method and system for fault early warning of non-uniformly distributed fasteners under complex working conditions. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the main purpose of the present invention is to provide a method and system for fault early warning of non-uniformly distributed fasteners under complex working conditions, which can effectively solve the problems in the background art. The specific technical solutions of the present invention are as follows:

[0006] A method for fault early warning of non-uniformly distributed fasteners under complex working conditions includes:

[0007] Obtain the stress data and image data of the device to be detected;

[0008] Identify the area where the fasteners are distributed in the device according to the stress data and image data to obtain multiple distribution areas of the fasteners;

[0009] In each distribution area, combine the current state of the fastener and the influence of stress on the fastener state to predict the fastener state, and when the fastener state fails, give a fault early warning.

[0010] Specifically, identifying the areas where fasteners are distributed in the device based on the stress data and image data to obtain multiple distribution areas of the fasteners, including:

[0011] Based on the image data, through a preset image recognition model, identifying multiple initial areas of the fasteners;

[0012] Based on the stress data, screening the multiple initial areas to obtain multiple distribution areas of the fasteners.

[0013] Specifically, the identifying multiple initial areas of the fasteners based on the image data through a preset image recognition model includes:

[0014] Using the image data with the positions of the fasteners marked to train the image recognition model to obtain a pre-trained image recognition model;

[0015] Inputting the current image data into the pre-trained image recognition model to identify multiple initial areas containing the fasteners.

[0016] Specifically, the screening the multiple initial areas based on the stress data to obtain multiple distribution areas of the fasteners includes:

[0017] Within each initial area, extracting the stress values of the corresponding area from the stress data to obtain multiple initial area stress values;

[0018] Calculating the initial area stress characteristics based on the initial area stress values;

[0019] Setting a stress characteristic threshold according to the preset stress distribution pattern of the fasteners;

[0020] Eliminating the initial areas where the initial area stress characteristics are less than the stress characteristic threshold to obtain multiple distribution areas of the fasteners.

[0021] Specifically, within each distribution area, combining the current state of the fastener and the influence of stress on the fastener state to predict the fastener state, and when the fastener state fails, giving a fault warning, including:

[0022] Within each distribution area, identifying the fastener state based on the image data to obtain the current state of each fastener;

[0023] Within a preset time period, analyzing the influence of stress on the fastener state based on the stress data to predict the state of each fastener to obtain the predicted state of each fastener;

[0024] When the predicted state fails, giving a fault warning.

[0025] Specifically, within each distribution area, the status of fasteners is identified based on image data to obtain the current status of each fastener, including:

[0026] Using image data containing various fastener statuses to train a fastener status recognition model, and obtaining a pre-trained fastener status recognition model;

[0027] Within each distribution area, based on the pre-trained fastener status recognition model, analyze the image data to obtain the current status of each fastener, where the current status includes normal, damaged, and detached.

[0028] Specifically, within a preset time period, analyze the influence of stress on the status of fasteners based on stress data analysis, and predict the status of each fastener to obtain the predicted status of each fastener, including:

[0029] Within a preset time period, calculate the stress gradient value by calculating the gradients of stress data in space and time;

[0030] Based on the stress gradient value, dynamically divide the working area of the fasteners to obtain a multi-level area division result;

[0031] Based on the multi-level area division result, calculate the average stress tensor of each area by performing tensor calculations on the stress in each area;

[0032] Based on the average stress tensor of each area, combined with the stress influence relationship between different areas, obtain a multi-area stress coupling model;

[0033] Based on the multi-area stress coupling model, calculate the comprehensive stress of each area;

[0034] Based on the comprehensive stress of each area, combined with the current status of each fastener, predict the change of the fastener status to obtain the predicted status of each fastener.

[0035] Specifically, based on the comprehensive stress of each area, combined with the current status of each fastener, predict the change of the fastener status to obtain the predicted status of each fastener, including:

[0036] Based on the comprehensive stress of each area, obtain the abnormal stress direction vector of each area through a preset abnormal propagation path prediction model;

[0037] Based on the abnormal stress direction vector, determine multiple abnormal areas;

[0038] Within each abnormal area, obtain the stress change trend of each fastener in the abnormal area through a preset stress distribution prediction model;

[0039] According to the stress change trend, analyze the stress at each moment in a preset time period to obtain the instantaneous stress value at each moment;

[0040] Within the preset time period, calculate the cumulative damage effect of the stress on the fastener according to the stress change trend to obtain the stress cumulative effect;

[0041] Formulate a state mapping rule according to the stress change trend, the instantaneous stress value, and the stress cumulative effect;

[0042] According to the state mapping rule, combined with the current state of each fastener, predict the change of the fastener state to obtain the predicted state of each fastener.

[0043] Specifically, the calculating the cumulative damage effect of the stress on the fastener according to the stress change trend within the preset time period to obtain the stress cumulative effect includes:

[0044] Within the preset time period, group the stress change trend according to a preset stress level to obtain multiple stress level groups;

[0045] Calculate the number of cycles of each stress level group;

[0046] According to the fastener material, obtain the fatigue life of the fastener corresponding to each stress level group;

[0047] By calculating the ratio of the number of cycles to the fatigue life of the fastener, obtain the damage rate of each stress level group;

[0048] Accumulate the damage rates of each stress level group to obtain the stress cumulative effect.

[0049] A fault warning system for non-uniformly distributed fasteners under complex working conditions, used to implement the method for fault warning of non-uniformly distributed fasteners under complex working conditions, includes:

[0050] A data acquisition module, which acquires stress data and image data of the device to be detected;

[0051] A region recognition module, which recognizes the regions where the fasteners are distributed in the device according to the stress data and the image data to obtain multiple distribution regions of the fasteners;

[0052] A fault warning module, within each distribution region, combined with the current state of the fastener and the influence of the stress on the fastener state, predicts the fastener state, and when the fastener state fails, issues a fault warning.

[0053] Compared with the prior art, the present application has the following beneficial effects:

[0054] This application combines the stress data and image data of equipment under complex working conditions to identify the position and status of fasteners, can comprehensively and accurately monitor the status of fasteners of equipment under complex working conditions, obtain accurate position and status recognition results, predict the status change of fasteners based on the prediction results of the stress change trend of the equipment, can detect potential faults in advance and give early warnings, helps to improve the timeliness and reliability of equipment fault early warning, reduces equipment damage and production accidents caused by fastener faults, can carry out targeted maintenance and repair, avoid unnecessary over-maintenance, and reduce the maintenance cost of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of the working process of the method for early warning of non-uniformly distributed fastener faults under complex working conditions in Embodiment 1 of the present invention;

[0056] Figure 2 It is a schematic diagram of stress data acquisition in Embodiment 1 of the present invention;

[0057] Figure 3 It is a schematic diagram of the division of the abnormal area of fasteners in Embodiment 1 of the present invention;

[0058] Figure 4 It is a flowchart of the working process of calculating the stress accumulation effect in Embodiment 1 of the present invention;

[0059] Figure 5 It is a schematic structural diagram of the system for early warning of non-uniformly distributed fastener faults under complex working conditions in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the drawings in the specification.

[0061] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0062] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment mutually exclusive with other embodiments.

[0063] Embodiment 1

[0064] This embodiment provides a method for early warning of faults of non-uniformly distributed fasteners under complex working conditions, such as Figure 1 The method for early warning of faults of non-uniformly distributed fasteners under complex working conditions includes:

[0065] S101. Obtain the stress data and image data of the device to be detected;

[0066] S102. Identify the areas where the fasteners are distributed in the device according to the stress data and image data, and obtain multiple distribution areas of the fasteners;

[0067] S103. In each distribution area, combine the current state of the fastener and the influence of stress on the state of the fastener to predict the state of the fastener. When the state of the fastener fails, early warning of the fault is carried out.

[0068] During the operation of the device, it is crucial to identify the connection state of the fasteners. At present, the identification of the state of the fasteners is based on the image recognition method. This recognition method is based on a single type of data, and the accuracy of the identified fastener state is relatively low. Moreover, based on the current image data for identification, early warning cannot be realized. This application combines stress data and image data to identify the position and state of the fasteners, improves the accuracy of the identification results, predicts the change of the state of the fasteners based on the change trend of the stress data on the damage effect of the fasteners, and realizes early warning of faults.

[0069] In this embodiment, such as Figure 2 , during the operation of the device, select appropriate stress sensors (such as strain gauges, pressure sensors, etc.) and install them at key positions of the device. The key positions can be the center of the device or each working part of the device to measure the stress change during the operation of the device; select an industrial camera with appropriate resolution and frame rate, install it at a position where the fasteners can be clearly photographed, collect the image data of the device at regular time intervals, and transmit the image data to the data processing center through the network; at the same time, obtain the stress data and image data, which can comprehensively understand the state of the fasteners from both mechanical and visual perspectives and improve the accuracy of fault judgment.

[0070] Specifically, identify the area of the fasteners according to the image data, and then combine the stress data to verify the area of the fasteners, and eliminate the areas that do not meet the stress conditions of the fasteners to obtain a more accurate distribution area of the fasteners; compared with the traditional method of only identifying the position of the fasteners according to the image data, this application adds stress verification, improves the accuracy of the fastener position identification results, can effectively reduce the misjudgment in image recognition, and improves the accuracy of fastener positioning.

[0071] After determining the distribution area of the fasteners, the status of each fastener is identified using image data and stress data respectively, and combined with the working status of the equipment, the stress change trend in the future time period is predicted to predict the status change of the fasteners; by analyzing the change trend and cumulative effect of the stress, it is predicted whether faults such as damage or detachment of the fasteners occur. When the identified status of the fastener is a fault, a fault warning mechanism is triggered to notify relevant personnel to take measures in a timely manner; compared with the traditional fastener fault detection method that only judges the status of the fasteners based on the current image, this application can predict the status change of the fasteners in advance through real-time monitoring and status prediction, timely discover the potential fault hazards of the fasteners, avoid the expansion of faults, reduce the equipment downtime and maintenance costs; by combining image data and stress data for fault judgment, the accuracy and reliability of the warning are improved.

[0072] This application combines the stress data and image data of the equipment under complex working conditions to identify the position and status of the fasteners, can comprehensively and accurately monitor the status of the fasteners of the equipment under complex working conditions, obtain accurate position and status recognition results, and predict the status change of the fasteners based on the prediction results of the stress change trend of the equipment, can discover potential fault hazards in advance and give warnings, which helps to improve the timeliness and reliability of equipment fault warnings, reduce equipment damage and production accidents caused by fastener faults, can carry out targeted maintenance and repair, avoid unnecessary over-maintenance, and reduce the maintenance cost of the equipment.

[0073] Further, the method for identifying the distribution area of the fasteners in the equipment according to the stress data and image data, obtaining multiple distribution areas of the fasteners, includes:

[0074] S201. According to the image data, through a preset image recognition model, multiple initial areas of the fasteners are identified;

[0075] S202. According to the stress data, the multiple initial areas are screened to obtain multiple distribution areas of the fasteners.

[0076] The traditional method for identifying the position of the fasteners relies on a single image recognition method. The recognition method is single and only depends on the accuracy of the image recognition model, which cannot guarantee the accuracy of the identified fastener position result. In the embodiment of this application, based on the image recognition result, the stress data is used to further screen the image recognition result, which can effectively remove the misjudgment area in the image recognition, improve the accuracy of the fastener distribution area positioning, and combine the objective physical quantity of the stress data for screening, considering multi-dimensional data information, making the obtained distribution area more reliable and reducing misjudgments caused by factors such as image noise and illumination.

[0077] In this embodiment, based on a machine learning algorithm, a large amount of labeled fastener image data is used to train a machine learning model, enabling the model to learn the feature patterns of fasteners in images and obtain a well-trained image recognition model with high accuracy. The model can master the features that distinguish fasteners from other objects, such as shape, texture, color distribution, etc. When new image data is input, the model searches and locates multiple initial regions containing fasteners in the image based on the learned features; through the machine learning model, the region containing fasteners can be quickly located in the image, improving the processing efficiency.

[0078] Specifically, in combination with the stress data of each region, the initial regions are further screened to remove misjudged regions and improve the accuracy of fastener distribution region positioning; the stress data reflects the stress distribution on the object surface. During the operation of the device, the fasteners will bear corresponding stresses, and stress concentration phenomena will occur around the fasteners. By analyzing the stress data, it is judged whether there is obvious stress concentration in the initial region. If so, it is considered that the initial region contains real fasteners and is retained as the distribution region; if not, it is removed, thereby improving the accuracy of fastener positioning.

[0079] Further, the multiple initial regions of fasteners are identified according to the image data through a preset image recognition model, including:

[0080] S301. Use a large amount of image data with labeled fastener positions to train the image recognition model to obtain a pre-trained image recognition model;

[0081] S302. Input the current image data into the pre-trained image recognition model to identify multiple initial regions containing fasteners.

[0082] In this embodiment, a large amount of image data containing fasteners and with the fastener positions labeled using a professional image annotation tool is used to train the image recognition model. Specifically, the image recognition model is the YOLOv5 model. The labeled image data set is divided into a training set and a test set. Among them, 70% of the data is used as the training set, and 30% is used as the test set. The training set is used for the model to learn features, and the test set is used to evaluate the final performance of the trained model. After training, a pre-trained image recognition model is obtained. Through the training with a large amount of labeled data, the model can learn rich features of fasteners, so as to more accurately identify the positions of fasteners in practical applications. Through the training with a large amount of labeled data, the model can learn rich features of fasteners and more accurately identify the positions of fasteners.

[0083] Specifically, the current image data is input into a pre-trained image recognition model. The image recognition model analyzes the input image according to the learned fastener feature patterns and position information, extracts the features therein, and matches them with the features learned during the training process. According to the matching results, the model marks the areas containing fasteners in the image, and these areas are the initial areas. The pre-trained model can process and recognize the input image in a short time, quickly obtain the initial areas containing fasteners, improving work efficiency; the entire recognition process is automatically completed by the model, reducing manual intervention and lowering labor costs and human errors.

[0084] Further, screening the multiple initial areas according to the stress data to obtain multiple distribution areas of fasteners includes:

[0085] S401. In each initial area, extract the stress value of the corresponding area from the stress data to obtain multiple initial area stress values;

[0086] S402. Calculate the initial area stress characteristics according to the initial area stress values;

[0087] S403. Set a stress characteristic threshold according to the preset fastener stress distribution pattern;

[0088] S404. Eliminate the initial areas whose initial area stress characteristics are less than the stress characteristic threshold to obtain multiple distribution areas of fasteners.

[0089] In this embodiment, the initial areas are screened according to the stress distribution of the identified initial areas to improve the accuracy of fastener position recognition. According to the stress information of the positions obtained by the stress sensors, combined with the distance between the fastener positions and the stress sensors, the stress values of each initial area are obtained. The stress data is a two-dimensional matrix, and each element represents the stress value at the corresponding position in the image. According to the coordinate range of the initial area, all the stress values within the corresponding area are extracted from the stress data matrix.

[0090] Specifically, by calculating the stress characteristics of the stress value, the concentration degree and change trend of the stress in the area can be comprehensively reflected, and it is possible to more accurately judge whether there is stress concentration caused by fasteners in the area. Specific stress characteristic indicators include average stress, maximum stress, stress standard deviation, etc. The stress characteristics of each initial area are calculated to more comprehensively describe the stress state of the initial area. The stress distribution pattern of the fastener under different working conditions is obtained through experimental measurement, numerical simulation or experience summary, and the key parameters of the stress characteristics in the pattern are found, such as the range of average stress, the upper limit of maximum stress, etc. According to the analysis results, a corresponding threshold is set for each stress characteristic indicator. By setting the characteristic threshold, those areas that are in the initial area but whose stress characteristics do not conform to the stress distribution pattern of the fastener can be excluded, thereby reducing misjudgment.

[0091] Specifically, the initial area is screened according to the set stress characteristic threshold, and the stress characteristic of each initial area is compared with the corresponding threshold. When the stress characteristic of the initial area is less than the preset threshold, it means that the stress concentration degree of the area is low, which does not conform to the stress distribution pattern that the fastener should have when subjected to force. The corresponding area is not the real fastener distribution area. These areas are eliminated to obtain a more accurate fastener distribution area, thereby improving the accuracy of fastener distribution area positioning.

[0092] Furthermore, in each distribution area, the fastener state is predicted by combining the current state of the fastener and the influence of stress on the fastener state, and when the fastener state fails, a fault warning is performed, including:

[0093] S501, in each distribution area, identifying the state of the fasteners according to the image data to obtain the current state of each fastener;

[0094] S502, within a preset time period, analyzing the influence of stress on the state of fasteners according to stress data, predicting the state of each fastener, and obtaining a predicted state of each fastener;

[0095] S503: When a fault occurs in the predicted state, a fault warning is issued.

[0096] The traditional method of fastener status recognition analyzes the real-time status of the fastener based on the current image. When the real-time status fails, an alarm is issued, but the failure situation cannot be warned in advance. The present application identifies the current status of the fastener and predicts the state changes of the fastener in the future period to achieve early warning. It issues a warning before the failure occurs, and can promptly discover and deal with the hidden dangers of fastener failure, which helps to ensure the safe and stable operation of the equipment.

[0097] In this embodiment, within each distribution area, the status of the fasteners in the image data is identified. The image data contains the appearance information of the fasteners, and different statuses will present different visual features in the image. For example, loose fasteners will show position deviation, increased gap, etc., and damaged fasteners will have obvious features such as fracture and deformation. Based on the image recognition method, the current status of the fasteners is identified through the image feature information, and the current status of each fastener is obtained.

[0098] Specifically, as the working time of the equipment increases and the stress effect accumulates, the status of the fasteners will change accordingly. For example, when a fastener is loose, the stress distribution it bears will change; when a fastener is about to be damaged, the stress will show abnormal fluctuations or accumulations. According to the working process of the equipment, the stress change trend of the equipment is predicted, and based on the stress change trend, the status change of the fasteners is predicted. By analyzing the stress data sequence within a preset time period and using the time series analysis method, a prediction model between the stress data and the fastener status is established, so as to predict the status of the fasteners in the future for a period of time, and the predicted status of each fastener is obtained.

[0099] Specifically, when the status of the fastener predicted by the stress data is a fault status, relevant personnel are reminded to take measures in time, triggering the fault warning mechanism. The warning mechanism can be implemented in various ways, such as SMS notification, email reminder, etc., to ensure that relevant personnel can learn the fault information in time. By combining the image data and the stress data, the current status of the fasteners can be intuitively identified, and the future status can be predicted through the stress data, improving the comprehensiveness and accuracy of fault detection. Through the prediction function of the stress data, a warning can be issued before the fault occurs, providing sufficient time for the maintenance and repair of the equipment, and realizing the transformation from passive maintenance to active prevention.

[0100] Further, the identifying the status of the fasteners according to the image data in each distribution area to obtain the current status of each fastener includes:

[0101] S601. Using a large amount of image data containing various fastener statuses to train the fastener status recognition model to obtain a pre-trained fastener status recognition model;

[0102] S602. In each distribution area, analyzing the image data according to the pre-trained fastener status recognition model to obtain the current status of each fastener, where the current status includes normal, damaged, and detached.

[0103] In this embodiment, based on the deep learning model, the status of fasteners in each distribution area is identified to obtain the current status of the fasteners. Through the deep learning model, the current status of each fastener can be quickly and accurately identified in the image. In industrial production, timely detection of the breakage or detachment of fasteners can avoid equipment failures and safety accidents, ensuring the safety and stability of the production process.

[0104] Specifically, a large number of images containing fasteners with various labeled statuses (normal, broken, detached) are used to train the fastener status recognition model. These images should be diverse, including different lighting conditions, shooting angles, background environments, etc., to improve the generalization ability of the model. The labeled image dataset is divided into a training set and a test set. 70% of the image data is used as the training set, and 30% of the image data is used as the test set to train the fastener status recognition model. Specifically, the fastener status recognition model is a CNN model. CNN can automatically extract meaningful features from images. Through training with a large number of labeled image data, the model can learn the feature patterns of fasteners in different statuses (normal, broken, detached). When processing new images, the model can accurately identify the status of fasteners based on the learned features.

[0105] Specifically, the pre-trained fastener status recognition model analyzes the image data in each input distribution area according to the learned feature patterns of fasteners in different statuses, extracts the features therein, and matches them with the features learned during the training process. According to the matching results, the model outputs the current status (normal, broken, detached) of each fastener. The pre-trained model can process and analyze the input images in a short time, quickly obtaining the current status of each fastener, improving work efficiency; the entire recognition process is automatically completed by the model, reducing manual intervention, and lowering labor costs and human errors.

[0106] Furthermore, within a preset time period, based on the stress data analysis of the influence of stress on the status of fasteners, the status of each fastener is predicted to obtain the predicted status of each fastener, including:

[0107] S701. Within a preset time period, by calculating the gradients of the stress data in space and time, the stress gradient value is obtained;

[0108] S702. According to the stress gradient value, the working area of the fastener is dynamically divided to obtain a multi-level area division result;

[0109] S703. Based on the multi-level area division result, by performing tensor calculations on the stress in each area, the average stress tensor of each area is obtained;

[0110] S704, obtaining a multi-region stress coupling model according to the average stress tensor of each region and the stress influence relationship between different regions;

[0111] S705, calculating the comprehensive stress of each region according to the multi-region stress coupling model;

[0112] S706: Based on the comprehensive stress of each region and in combination with the current state of each fastener, predict the change of the fastener state to obtain the predicted state of each fastener.

[0113] In this embodiment, the stress data during the operation of the equipment is analyzed, and the fasteners under complex working conditions are analyzed by region. This can reduce the difficulty of analysis, clarify the status of fasteners in different regions, and analyze the stress influence between different regions in combination with the stress coupling relationship between different regions. This can more accurately capture the status changes of the fasteners, and when the fastener status is abnormal, an early warning can be issued in time.

[0114] Specifically, within a preset time period, the finite difference method is used to analyze the stress data and calculate the gradient change of stress. By calculating the gradient of stress in space and time, the changing trend of stress at different locations and at different times can be comprehensively considered, so as to analyze the stage of drastic stress change; according to the calculated stress gradient value, a gradient threshold is set, and the specific gradient threshold can be set according to the actual calculation accuracy requirements. When the gradient value in a certain area is greater than the gradient threshold and the stress continues to rise, it is divided into a high-risk area; if the gradient value is less than the gradient threshold and the stress tends to be stable, it is divided into a low-risk area. The high-risk area indicates that the stress changes drastically and there is a high risk of failure; the stress in the low-risk area is relatively stable, and different levels of risk areas are obtained through dynamic division. Different monitoring and analysis strategies can be adopted for different areas to make the analysis more targeted.

[0115] Specifically, after the fasteners under complex working conditions are divided into regions, the stress in each divided region is tensor calculated to calculate the average stress tensor of each region. The calculation formula is as follows:

[0116] ;

[0117] In the formula, is the average stress tensor, N is the number of data points in the region, is the stress tensor component of the kth data point. In three-dimensional space, i and j take values of 1, 2, and 3, representing the stress tensors in various directions. The stress tensor contains 9 components in three-dimensional space. By calculating the average stress tensor in each region, the influence of local stress fluctuations in the region can be eliminated, and the representative stress state of the region can be obtained.

[0118] Furthermore, analyze the stress influence relationship between different regions. Combine the average stress tensors of each region to calculate the comprehensive stress of each region, obtaining a more accurate comprehensive stress value. Based on the mutual influence relationship of the stress between different regions of the fastener, establish a coupling coefficient matrix to describe the stress influence degree between different regions. Combine the average stress tensors of each region to calculate the comprehensive stress of each region. The calculation formula is as follows:

[0119] ;

[0120] In the formula, is the comprehensive stress tensor of region m considering coupling, is the average stress tensor of region m, is the average stress tensor of region n, is the stress influence degree between region m and region n. Based on the multi-region stress coupling model, by superimposing the self-stress of each region and the coupling stress from other regions on it, a more realistic comprehensive stress value of each region can be obtained. Taking the comprehensive stress of each region as an influencing factor and combining the current state of the fastener, predict the future state of the fastener. By comprehensively considering the stress and the current state, accurately predict the state change of the fastener; by combining the current state and stress data of the fastener for prediction, it avoids the limitation of judging based on a single factor and improves the accuracy of state prediction; it can predict potential failure risks in advance before the actual state change of the fastener occurs, providing a basis for the preventive maintenance of the equipment.

[0121] Furthermore, based on the comprehensive stress of each region and combining the current state of each fastener, predict the state change of the fastener to obtain the predicted state of each fastener, including:

[0122] S801. Based on the comprehensive stress of each region, through a preset abnormal propagation path prediction model, obtain the abnormal stress direction vector of each region;

[0123] S802. Determine multiple abnormal regions according to the abnormal stress direction vector;

[0124] S803. Within each abnormal region, through a preset stress distribution prediction model, obtain the stress change trend of each fastener within the abnormal region;

[0125] S804. Analyze the stress at each moment within a preset time period according to the stress change trend to obtain the instantaneous stress value at each moment;

[0126] S805. Within a preset time period, calculate the cumulative damage effect of the stress on the fastener according to the stress change trend to obtain the stress cumulative effect;

[0127] S806. Develop a state mapping rule based on the stress change trend, instantaneous stress value, and stress accumulation effect;

[0128] S807. Predict the change in the fastener state according to the state mapping rule and in combination with the current state of each fastener to obtain the predicted state of each fastener.

[0129] In this embodiment, as Figure 3 , determine the abnormal area according to the stress direction vector of each area. In the figure, according to the abnormal stress direction vector, the dotted area is determined as the abnormal area. Analyze the state of the fasteners in the abnormal area to achieve accurate area analysis. Conduct personalized analysis on the abnormal stress conditions of the fasteners in the abnormal area, which can improve the accuracy of fastener state prediction, reduce misjudgment and missed judgment, and at the same time avoid analyzing all fasteners, improving the efficiency of abnormal state detection.

[0130] Specifically, train the abnormal propagation path prediction model with a large amount of historical data and stress change data to obtain a preset abnormal propagation path prediction model, and learn the mode of stress anomaly propagation between regions under different comprehensive stress conditions. In this embodiment, the abnormal propagation path prediction model is a Bayesian network model based on machine learning. Input the comprehensive stress data of each area into the preset abnormal propagation path prediction model, and the model outputs the predicted direction of abnormal stress occurrence to obtain the abnormal stress direction vector of each area; the abnormal stress direction vector indicates the direction of stress anomaly propagation. According to the predicted abnormal stress direction vector, determine the abnormal area with stress anomaly from the fasteners under complex working conditions. When the abnormal stress direction vector of a certain area exceeds a certain threshold, it indicates that the area is greatly affected by abnormal stress, and it is determined as the abnormal area; by quickly locating the area greatly affected by abnormal stress, in-depth analysis can be carried out on the abnormal area, improving the efficiency and accuracy of fault prediction.

[0131] Specifically, a large amount of historical stress data of abnormal regions is used to train the stress distribution prediction model to obtain a preset stress distribution prediction model. In this embodiment, the stress distribution prediction model is an LSTM model. This model learns the stress distribution characteristics within the region, the positions of the fasteners, and the mechanical relationships between them. By inputting the characteristics of the abnormal region and the relevant information of the fasteners within the region, the stress distribution prediction model can predict the stress change trend of each fastener in the future for a period of time, including whether the stress increases, decreases, or remains stable, etc. The stress change trend reflects the change law of stress over time. According to the predicted stress change trend, the stress values corresponding to each moment over time are calculated to obtain the corresponding instantaneous stress values. Through the instantaneous stress values, the impact of instantaneous stress changes on the fasteners can be evaluated, and the stress state of the fasteners within the preset time period can be understood more precisely, which helps to detect in time the situation where the fasteners are damaged due to stress peaks.

[0132] Specifically, within the preset time period, according to the predicted stress change trend, the total damage degree endured by the fasteners during this period is calculated. The fastener material will generate fatigue damage under the action of alternating stress. The damage caused to the fasteners by the accumulation of stress over time is calculated, so as to evaluate the change situation of the fastener state. By considering the cumulative effect of stress, the damage degree of the fasteners within the preset time period can be evaluated more comprehensively, avoiding only focusing on the instantaneous stress and ignoring the influence of long-term damage, and detecting potential failures such as fatigue damage of the fasteners in advance, providing a basis for preventive maintenance.

[0133] Specifically, after obtaining the stress change trend, the instantaneous stress value, and the stress cumulative effect, comprehensively considering the three influencing factors of the stress change trend, the instantaneous stress value, and the stress cumulative effect, the state of the fasteners is predicted, and corresponding state mapping rules are set. According to the stress change trend, the corresponding stress change rate is calculated, and the calculation formula is as follows:

[0134] ;

[0135] In the formula, is the stress change rate per unit time, is the stress value at the current moment, is the stress value at the previous moment, is the preset unit time interval. According to the state of the fasteners under actual working conditions, a failure threshold is set to obtain the stress change rate threshold, the instantaneous stress threshold, and the stress cumulative effect threshold. According to the stress change trend, the instantaneous stress, and the stress cumulative effect, combined with the current state of each fastener, state mapping rules are formulated, and the state mapping rules are as follows:

[0136] The current state is normal, and the stress change rate of the predicted stress change trend is less than the stress change rate threshold, the instantaneous stress value is less than the instantaneous stress threshold, and the stress accumulation effect is less than the stress accumulation effect threshold, then the predicted state remains normal;

[0137] The current state is normal, but the stress change rate of the predicted stress change trend is greater than the stress change rate threshold, or the predicted instantaneous stress value is greater than the instantaneous stress threshold, or the stress accumulation effect is greater than the stress accumulation effect threshold, then the predicted state is damaged;

[0138] The current state is damaged, and the predicted instantaneous stress value is greater than the instantaneous stress threshold or the stress accumulation effect is greater than the stress accumulation effect threshold, then the predicted state is detachment. Different thresholds are set to determine the state of the fastener; the stress change trend reflects the dynamic change of stress over time, the instantaneous stress value reflects the magnitude of stress at the current moment, and the stress accumulation effect takes into account the damage accumulation under long-term stress; combined with the current state of the fastener, the future state of the fastener can be predicted more accurately. When the predicted state shows damage or detachment, a warning is issued in a timely manner, and the staff can replace the corresponding fastener in a timely manner.

[0139] Furthermore, as Figure 4 stated, within a preset time period, the cumulative damage effect of stress on the fastener is calculated according to the stress change trend to obtain the stress accumulation effect, including:

[0140] S901. Within a preset time period, group the stress change trend according to a preset stress level to obtain multiple stress level groups;

[0141] S902. Calculate the number of cycles for each stress level group;

[0142] S903. According to the fastener material, obtain the fatigue life of the fastener corresponding to each stress level group;

[0143] S904. By calculating the ratio of the number of cycles to the fatigue life of the fastener, obtain the damage rate for each stress level group;

[0144] S905. Accumulate the damage rates of each stress level group to obtain the stress accumulation effect.

[0145] In this embodiment, the cumulative damage effect of the fastener is calculated according to the stress change trend. By calculating the damage situation through the Miner's rule, the fatigue damage degree caused by the stress to the fastener within a preset time period can be accurately evaluated. Specifically, the fatigue damage characteristics of the fastener are different under different stress levels. The stress change trend is grouped according to the preset stress levels, and the damage situations under different stress levels are calculated respectively to obtain the influence of different stress intervals on the fastener. According to the design requirements, material characteristics and actual working condition experience of the fastener, a series of preset stress levels are determined. Within the preset time period, the stress values in the stress change trend are read one by one, and each stress value is classified into the corresponding preset stress level interval to obtain multiple stress level groups.

[0146] Specifically, at each stress level, the cumulative effect is calculated. Within each stress level group, the stress will cycle. The fatigue damage caused by the stress to the fastener is closely related to the number of stress cycles. The number of complete stress cycles within each stress level group is counted to obtain the stress cycle number. Specifically, the rainflow counting method is used to calculate the stress cycle number. The rainflow counting method finds the peak and valley points of the stress by comparing the magnitude relationship of adjacent stress values, and determines the number of cycles according to the number of peak and valley points. The number of cycles is one of the key parameters for calculating fatigue damage. Accurately calculating the number of cycles in each stress level group can more precisely evaluate the damage effect of this stress level on the fastener.

[0147] Specifically, different materials of the fastener have different fatigue lives under different stress levels. According to the fastener material, the calculation method of the fastener fatigue life under different stress actions can be determined. Specifically, the stress-life curve (S-N curve) of the corresponding fastener material can be obtained by referring to material manuals, experimental data or relevant research literature. According to the stress-life curve, the corresponding fatigue life of the fastener can be calculated, and the calculation formula is as follows:

[0148] ;

[0149] In the formula, N is the fatigue life, S is the stress level, and z and C are constants related to the fastener material, which need to be determined according to the specific fastener material. The fatigue life of the corresponding fastener is calculated through the formula. The fatigue performances of different materials vary greatly. Determining the fatigue life according to the fastener material can more accurately reflect the fatigue damage situation of the fastener in actual situations.

[0150] Specifically, according to Miner's rule, the cumulative fatigue damage of fasteners under different stress levels can be calculated. The damage rate of each stress level group is defined as the ratio of the number of cycles of that stress level group to the corresponding fatigue life, indicating the degree of damage caused by that stress level group to the fasteners within a preset time period. The damage rate provides a quantitative index for the damage of each stress level group, facilitating the comparison of the damage magnitudes of different stress levels to the fasteners. After obtaining the damage rates of each stress level group, the total fatigue damage of the fasteners can be calculated. According to Miner's rule, the total fatigue damage of the material is the linear accumulation of the damages under various stress levels. By summing up the damage rates of each stress level group, the cumulative damage effect of stress on the fasteners within the preset time period can be obtained, reflecting the overall fatigue damage degree of the fasteners during this time period. The specific calculation formula is as follows:

[0151] ;

[0152] In the formula, D is the stress cumulative damage effect, Q is the number of stress level groups, is the number of cycles of the p-th stress level group, is the fatigue life corresponding to the p-th stress level group. By calculating the stress cumulative effect, the damage effects of different stress levels on the fasteners within the preset time period are comprehensively considered, enabling a comprehensive assessment of the fatigue damage degree of the fasteners, and then determining whether the fasteners are approaching fatigue failure, so as to conduct early fault warning and maintenance decision-making.

[0153] Embodiment 2

[0154] In this embodiment, as Figure 5 , a fault warning system for non-uniformly distributed fasteners under complex working conditions is provided, which is used to implement the method for fault warning of non-uniformly distributed fasteners under complex working conditions, including:

[0155] A data acquisition module that acquires stress data and image data of the device to be detected;

[0156] A region recognition module that recognizes the regions where the fasteners are distributed in the device according to the stress data and image data, and obtains multiple distribution regions of the fasteners;

[0157] A fault warning module that predicts the state of the fasteners by combining the current state of the fasteners and the influence of stress on the fasteners in each distribution region, and issues a fault warning when the state of the fasteners fails.

[0158] In this embodiment, the data acquisition module includes a stress sensor, an image acquisition device, a data transmission unit, and a data preprocessing sub-module, which can acquire the stress data and image data of the equipment under complex working conditions in real time and accurately, and perform preliminary processing and transmission of the data, providing a reliable data basis for subsequent area recognition and fault warning. Through the collaborative work of multiple sensors and flexible data transmission methods, it is ensured that the required data can be effectively collected under different complex working conditions.

[0159] Specifically, the area recognition module includes an image recognition sub-module, a stress analysis sub-module, and an area screening decision unit, which accurately identify the distribution area of the fasteners by combining the stress data and image data. The fastener area is initially determined through image recognition, and then these areas are screened and verified using the stress data to remove misjudged areas, and finally a reliable fastener distribution area is obtained, providing accurate position information for subsequent fault warning; the fault warning module includes a state recognition sub-module, a stress prediction sub-module, a state prediction sub-module, and a warning trigger unit, which accurately identify the state of the fasteners in each distribution area and predict their future state based on the stress data. When it is predicted that the state of the fasteners fails, a fault warning is issued in a timely manner to remind relevant personnel to take corresponding measures to avoid equipment failures and safety accidents and ensure the normal operation of the equipment and the continuity of production.

[0160] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of non-uniformly distributed fastener failures under complex working conditions, characterized in that Including: Obtain the stress data and image data of the device to be detected; Identify the areas where fasteners are distributed in the device according to the stress data and image data, and obtain multiple distribution areas of the fasteners; In each distribution area, combine the current state of the fastener and the influence of stress on the fastener state to predict the fastener state. When the fastener state fails, give a fault warning; The step of, in each distribution area, combining the current state of the fastener and the influence of stress on the fastener state to predict the fastener state and giving a fault warning when the fastener state fails includes: In each distribution area, identify the state of the fastener according to the image data to obtain the current state of each fastener; Within a preset time period, analyze the influence of stress on the fastener state according to the stress data, and predict the state of each fastener to obtain the predicted state of each fastener; When the predicted state fails, give a fault warning; The step of, within a preset time period, analyzing the influence of stress on the fastener state according to the stress data, and predicting the state of each fastener to obtain the predicted state of each fastener includes: Within a preset time period, calculate the stress gradient value by calculating the gradients of the stress data in space and time; According to the stress gradient value, dynamically divide the working area of the fastener to obtain a multi-level area division result; Based on the multi-level area division result, calculate the average stress tensor of each area by performing tensor calculation on the stress in each area; According to the average stress tensor of each area, and combining the stress influence relationship between different areas, obtain a multi-area stress coupling model; According to the multi-area stress coupling model, calculate the comprehensive stress of each area; Based on the comprehensive stress of each area, and combining the current state of each fastener, predict the change of the fastener state to obtain the predicted state of each fastener; The step of, based on the comprehensive stress of each area, and combining the current state of each fastener, predicting the change of the fastener state to obtain the predicted state of each fastener includes: Based on the comprehensive stress of each area, obtain the abnormal stress direction vector of each area through a preset abnormal propagation path prediction model; According to the abnormal stress direction vector, determine multiple abnormal areas; In each abnormal area, obtain the stress change trend of each fastener in the abnormal area through a preset stress distribution prediction model; According to the stress change trend, analyze the stress at each moment within a preset time period to obtain the instantaneous stress value at each moment; Within a preset time period, calculate the cumulative damage effect of stress on the fastener according to the stress change trend to obtain the stress cumulative effect; According to the stress change trend, instantaneous stress value and stress cumulative effect, formulate a state mapping rule; According to the state mapping rule, and combining the current state of each fastener, predict the change of the fastener state to obtain the predicted state of each fastener.

2. The method for early warning of complex working condition non-uniform distributed fastener faults according to claim 1, characterized in that The step of identifying the areas where fasteners are distributed in the device according to the stress data and image data, and obtaining multiple distribution areas of the fasteners includes: Based on the image data, multiple initial regions of the fastener are identified through a preset image recognition model; Based on the stress data, the multiple initial regions are screened to obtain multiple distribution regions of the fastener.

3. The method for early warning of complex working condition non-uniform distributed fastener faults according to claim 2, wherein, The step of identifying multiple initial regions of the fastener through a preset image recognition model based on the image data includes: Using the image data with the positions of the fasteners marked, training the image recognition model to obtain a pre-trained image recognition model; Inputting the current image data into the pre-trained image recognition model to identify multiple initial regions containing the fasteners.

4. The method for non-uniform distributed fastener fault warning under complex working conditions according to claim 2, characterized in that The step of screening the multiple initial regions based on the stress data to obtain multiple distribution regions of the fastener includes: Within each initial region, extracting the stress value of the corresponding region from the stress data to obtain multiple initial region stress values; Calculating the initial region stress characteristics based on the initial region stress values; Setting a stress characteristic threshold according to the preset stress distribution pattern of the fastener; Eliminating the initial regions with the initial region stress characteristics less than the stress characteristic threshold to obtain multiple distribution regions of the fastener.

5. The method for fault warning of non-uniform distributed fasteners under complex working conditions according to claim 1, wherein The step of identifying the current state of each fastener based on the image data within each distribution region includes: Using the image data containing multiple fastener states to train the fastener state recognition model to obtain a pre-trained fastener state recognition model; Within each distribution region, analyzing the image data according to the pre-trained fastener state recognition model to obtain the current state of each fastener, where the current state includes normal, damaged, and detached.

6. The method for fault warning of non-uniformly distributed fasteners under complex working conditions according to claim 1, characterized in that The step of calculating the cumulative damage effect of the stress on the fastener according to the stress change trend within a preset time period to obtain the stress cumulative effect includes: Within a preset time period, grouping the stress change trend according to a preset stress level to obtain multiple stress level groups; Calculating the number of cycles of each stress level group; Obtaining the fatigue life of the fastener corresponding to each stress level group according to the fastener material; Calculating the damage rate of each stress level group by calculating the ratio of the number of cycles to the fatigue life of the fastener; Accumulating the damage rates of each stress level group to obtain the stress cumulative effect.

7. A non-uniform distributed fastener fault warning system for complex working conditions, characterized in that, A device for implementing the method for early warning of faults of non-uniform distributed fasteners under complex working conditions according to any one of claims 1 to 6, includes: A data acquisition module, acquiring the stress data and image data of the device to be detected; A region recognition module, identifying the regions where the fasteners are distributed in the device according to the stress data and image data to obtain multiple distribution regions of the fasteners; A fault early warning module, within each distribution region, predicting the state of the fastener by combining the current state of the fastener and the influence of the stress on the fastener state, and giving a fault early warning when the state of the fastener fails.

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