A method and system for monitoring the failure of a bakelite guide rail

Abstract

The present application provides a method and system for monitoring the faults of bakelite guide rails, relating to the technical field of fault detection. The method includes: establishing a digital connection between a monitoring platform and a distributed sensor network; collecting multi-dimensional physical quantities during the operation of the guide rails to construct a status information library; generating an abnormal status information library; calling a group of vision sensors to collect images of the fault sections of the bakelite guide rails according to the abnormal sequence values to obtain a set of guide rail part images; analyzing the set of guide rail part images to obtain a guide rail fault cause library; performing a correlation analysis based on the abnormal status information library and the guide rail fault cause library to obtain a guide rail fault model; generating a fault repair plan according to the guide rail fault model, and feeding back the fault repair plan to the monitoring platform. It solves the technical problems existing in the prior art, such as the monitoring method is not accurate and comprehensive enough, lacks data processing ability, and lacks a systematic and mature fault model, resulting in slow judgment and easy generation of errors.

Description

Technical Field

[0001] This application relates to the technical field of fault detection, and particularly to a method and system for monitoring faults in bakelite guide rails. Background Art

[0002] Bakelite guide rails play an indispensable role in many production fields. They provide accurate guidance and support for the operation of production equipment, ensuring that each component moves stably along a predetermined trajectory. For example, in the furniture production field, the accuracy and stable operation of bakelite guide rails directly determine the cutting quality of solid wood boards. Any minor deviation in the guide rails may result in cutting dimensions not meeting the requirements, affecting the overall quality of furniture. Therefore, the normal operation of bakelite guide rails is directly related to the continuity of production and the stability of product quality. With the development of modern industrial production towards high precision, high efficiency, and large scale, the application scenarios of bakelite guide rails are constantly expanding, and their safe and stable operation has become one of the key factors to ensure the normal operation of the entire production system. Once a bakelite guide rail fails, it may cause the equipment to jam, increase wear, and even lead to the stagnation of the entire production line, resulting in huge economic losses. Moreover, a faulty bakelite guide rail may also cause damage to other associated components, increasing the equipment maintenance cost and difficulty. Therefore, fault monitoring of bakelite guide rails is an inevitable requirement to ensure the smooth progress of production, reduce costs, and improve production efficiency.

[0003] Currently, there are many deficiencies in the monitoring technologies for bakelite guide rails. Most traditional monitoring technologies are based on regular manual inspections, which lack real-time performance. Staff can only check the guide rails at fixed time intervals, making it difficult to detect problems at the first moment of a fault. Moreover, manual inspections are limited by the experience and skill levels of the staff, and there may be significant differences in the inspection results of different personnel, making it difficult to guarantee the accuracy of the detection results. Although some sensor-based monitoring technologies can achieve a certain degree of automated monitoring, the arrangement of sensors is often not reasonable enough, and they can only monitor partial areas of the guide rails, unable to comprehensively cover the entire operating state of the guide rails. In addition, the existing sensor monitoring technologies have slow judgment because there is no systematic and mature fault model. When facing the fault judgment of bakelite guide rails, there is a lack of comprehensive and accurate reference basis. This makes the diagnosis process not only time-consuming but also prone to errors.

[0004] In summary, the existing fault monitoring technologies for bakelite guide rails far from meet the industry requirements. Their monitoring methods are not precise and comprehensive enough, lack data processing capabilities, and the lack of a systematic and mature fault model leads to slow judgment and is prone to errors. Therefore, a method is needed to solve the above problems. Summary of the Invention

[0005] The present disclosure provides a method and system for monitoring the faults of bakelite guide rails, aiming to solve the technical problems in the prior art that the monitoring method is not accurate and comprehensive enough, lacks data processing capabilities, and lacks a systematic and mature fault model, resulting in slow judgment and easy generation of errors.

[0006] According to the first aspect of the present disclosure, a method for monitoring the faults of bakelite guide rails is provided, including:

[0007] Establish a digital connection between the monitoring platform and the distributed sensor network. The distributed sensor network includes M groups of multi-source sensors and M groups of visual sensor groups. The multi-source sensors are deployed at equal intervals on the edge of the bakelite guide rail, and the visual sensor groups are arranged around the guide rail according to the installation nodes of the multi-source sensors. Among them, the multi-source sensors include speed sensors, vibration sensors, and current sensors;

[0008] Continuously collect multi-dimensional physical quantities during the operation of the guide rail through the multi-source sensors, and construct a status information library. The status information library includes speed information, vibration information, current information, and sequence values;

[0009] Based on the anomaly detection algorithm, judge and extract the abnormal data in the status information library to generate an abnormal status information library. The abnormal status information library includes abnormal speed data sets, abnormal vibration data sets, abnormal current data sets, and abnormal sequence values;

[0010] According to the abnormal sequence value, call the visual sensor group to collect images of the fault section of the bakelite guide rail to obtain a set of guide rail part images;

[0011] Analyze the set of guide rail part images to obtain a guide rail fault cause library. The guide rail fault cause library includes a guide rail deformation fault cause set and a guide rail loss fault cause set;

[0012] According to the abnormal status information library and the guide rail fault cause library, perform a correlation analysis to obtain a guide rail fault model;

[0013] Generate a fault repair plan according to the guide rail fault model and feedback the fault repair plan to the monitoring platform.

[0014] According to the second aspect of the present disclosure, a system for monitoring the faults of bakelite guide rails is provided, including:

[0015] A sensor layout module, which is used to establish a digital connection between the monitoring platform and the distributed sensor network. The distributed sensor network includes M groups of multi-source sensors and M groups of visual sensor groups. The multi-source sensors are deployed at equal intervals on the edge of the bakelite guide rail, and the visual sensor groups are arranged around the guide rail according to the installation nodes of the multi-source sensors. Among them, the multi-source sensors include speed sensors, vibration sensors, and current sensors;

[0016] A state information database construction module, which is used to continuously collect multi-dimensional physical quantities during the operation of the guide rail through the multi-source sensors and construct a state information database. The state information database includes speed information, vibration information, current information, and sequence values;

[0017] An abnormal state information database generation module, which is used to judge and extract abnormal data in the state information database based on an abnormal detection algorithm and generate an abnormal state information database. The abnormal state information database includes an abnormal speed data set, an abnormal vibration data set, an abnormal current data set, and abnormal sequence values;

[0018] A fault image acquisition module, which is used to call the vision sensor group to acquire images of the faulty section of the bakelite guide rail according to the abnormal sequence value and obtain a set of guide rail part images;

[0019] A fault cause analysis module, which is used to analyze the set of guide rail part images and obtain a guide rail fault cause database. The guide rail fault cause database includes a guide rail deformation fault cause set and a guide rail loss fault cause set;

[0020] A fault model construction module, which is used to perform a correlation analysis based on the abnormal state information database and the guide rail fault cause database to obtain a guide rail fault model;

[0021] A fault repair plan generation and feedback module, which is used to generate a fault repair plan based on the guide rail fault model and feedback the fault repair plan to the monitoring platform.

[0022] One or more technical solutions provided in the present disclosure have at least the following technical effects or advantages:

[0023] Establish a digital connection between the monitoring platform and the distributed sensor network. The distributed sensor network includes M groups of multi-source sensors and M groups of vision sensor groups. The multi-source sensors are evenly deployed on the edge of the bakelite guide rail, and the vision sensor groups are arranged around the guide rail according to the installation nodes of the multi-source sensors. Among them, the multi-source sensors include speed sensors, vibration sensors, and current sensors. Continuously collect multi-dimensional physical quantities during the operation of the guide rail through the multi-source sensors, and construct a status information library. The status information library includes speed information, vibration information, current information, and sequence values. Based on the anomaly detection algorithm, judge and extract the abnormal data in the status information library to generate an abnormal status information library. The abnormal status information library includes an abnormal speed data set, an abnormal vibration data set, an abnormal current data set, and an abnormal sequence value. According to the abnormal sequence value, call the vision sensor group to collect images of the faulty section of the bakelite guide rail to obtain a guide rail part image set. Analyze the guide rail part image set to obtain a guide rail fault cause library. The guide rail fault cause library includes a guide rail deformation fault cause set and a guide rail loss fault cause set. According to the abnormal status information library and the guide rail fault cause library, perform a correlation analysis to obtain a guide rail fault model. Generate a fault repair plan based on the guide rail fault model and feedback the fault repair plan to the monitoring platform. This solves the technical problems in the prior art that the monitoring method is not accurate and comprehensive enough, lacks data processing capabilities, and lacks a systematic and mature fault model, resulting in slow judgment and easy errors. It achieves the technical effects of accurate and rapid fault diagnosis, comprehensive monitoring, in-depth data analysis, and improved production safety and stability.

[0024] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0025] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0026] Figure 1 It is a schematic flow chart of a method for monitoring the faults of a bakelite guide rail provided by an embodiment of this application;

[0027] Figure 2 It is a schematic structural diagram of a system for monitoring the faults of a bakelite guide rail provided by an embodiment of this application.

[0028] Explanation of the reference numerals: sensor deployment module 11, state information library construction module 12, abnormal state information library generation module 13, fault image acquisition module 14, fault cause analysis module 15, fault model construction module 16, fault repair plan generation and feedback module 17. DETAILED DESCRIPTION

[0029] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0030] Embodiment 1, a method for monitoring a bakelite rail fault provided by the present disclosure, hereby refer to Figure 1 For illustration, the methods include:

[0031] S1: Establishing a digital connection between the monitoring platform and a distributed sensor network, wherein the distributed sensor network includes M groups of multi-source sensors and M groups of visual sensor groups, wherein the multi-source sensors are deployed at equal intervals on the edge of the bakelite rail, and the visual sensor groups are arranged around the rail according to the multi-source sensor installation nodes, wherein the multi-source sensors include speed sensors, vibration sensors, and current sensors;

[0032] Specifically, a segmented equidistant deployment strategy is implemented on the mechanical structure of the guide rail, and a group of multi-source sensors is arranged at fixed intervals, totaling M groups. For example, the fixed distance value is determined to be 1 meter, and a group of multi-source sensors is installed on the edge of the guide rail every 1 meter, and they are numbered 1 to M in sequence. Each group of multi-source sensors integrates three types of monitoring devices: speed sensor, vibration sensor, and current sensor. Each group of sensors is wirelessly connected to the nearest edge computing node to form a distributed data acquisition network to ensure continuous state perception of the entire length of the guide rail. The deployment meets the requirements of equidistant coverage, signal stability, and maintenance convenience. According to the M groups of multi-source sensor installation nodes, the visual sensor group is arranged around the guide rail and numbered 1 to M in sequence. Each group of visual sensor groups contains visual sensors in four directions, which are used to capture image information of the bakelite guide rail from different perspectives, achieve visual coverage of the guide rail from all angles, obtain comprehensive image information of the guide rail, and provide rich visual data for subsequent fault analysis.

[0033] S2: Continuously collect multi-dimensional physical quantities of the guide rail during operation through the multi-source sensor to build a state information library, wherein the state information library includes speed information, vibration information, current information and sequence value;

[0034] Specifically, a non-contact optoelectronic encoder is used as the speed sensor, a triaxial MEMS accelerometer is selected as the vibration sensor, and a closed-loop Hall current sensor is used as the current sensor. The time bases of multi-source data are aligned through a high-precision time synchronization protocol, and the original signals are collected according to a preset sampling strategy, and preprocessing operations such as signal processing are performed and integrated and stored in the state information library. The state information library adopts a multi-dimensional matrix structure, with the multi-source sensor serial numbers as the row index and the three types of monitoring data as the column fields to construct a standardized data storage framework.

[0035] S3: Based on the anomaly detection algorithm, judge and extract the abnormal data in the state information library to generate an abnormal state information library, which includes an abnormal speed data set, an abnormal vibration data set, an abnormal current data set, and an abnormal sequence value;

[0036] Specifically, deeply mine and analyze all the data in the state information library. Through three anomaly detection frameworks, accurately strip the abnormal data from the three types of monitoring data to construct an abnormal state information library. For the speed information, use the Isolation Forest algorithm to identify abnormal patterns such as speed mutation, speed steady-state offset, and excessive speed volatility. Extract the speed data that meets the abnormal judgment criteria, including the sequence value, speed value, and speed anomaly type, to generate an abnormal speed data set; obtain the vibration information in the state information library, use the K-Means clustering algorithm to screen the abnormal vibration data, and judge the abnormal type, and integrate the sequence value, vibration value, and abnormal vibration type into the abnormal vibration data set; obtain the multi-position current data samples during the operation of the guide rail from the state information library. According to the current data under the normal operation state, draw a box plot, and compare the current value with the box plot threshold one by one. If the current value is less than the lower threshold or greater than the upper threshold, determine that the current information is abnormal. For the determined abnormal current data, construct an abnormal current data set, and each record in this data set should include the sequence value, current value, and current anomaly type. Integrate the abnormal speed data set, abnormal vibration data set, and abnormal current data set into the abnormal state information library with the sequence value as the reference index. Each sequence value may correspond to one or more abnormal data points, and these data points may come from different data sets. For example, an abnormal speed data and abnormal current data may be included under one sequence value, indicating that both the speed and current are abnormal under this sequence value number, or only abnormal vibration data exists under one sequence value, indicating that only the vibration is abnormal under this specific sequence value number.

[0037] S4: Call the vision sensor group to collect images of the faulty section of the bakelite guide rail according to the abnormal sequence value to obtain a guide rail part image set;

[0038] Specifically, according to the abnormal sequence value in the abnormal status information library, trigger the visual sensor group with the same number to capture the guide rail image. The visual sensor group performs image acquisition according to preset parameters such as resolution and shooting frequency to obtain the guide rail part image. According to the propagation speed of the traveling wave in the guide rail and the time difference of the traveling wave reaching different detection points, use the traveling wave positioning formula to calculate the specific position of the current fault in the guide rail. Use the LabelImg annotation tool to accurately mark the corresponding position of the current fault in the image on the guide rail part image with a rectangle or other suitable marking shape to generate the guide rail part image set.

[0039] S5: Analyze the guide rail part image set to obtain the guide rail fault cause library, where the guide rail fault cause library includes a guide rail deformation fault cause set and a guide rail loss fault cause set;

[0040] Specifically, for each guide rail part image, use the shape detection algorithm in image analysis technology to extract features. For example, use an algorithm based on edge detection, such as the Canny edge detection algorithm, to determine the edge contour of the guide rail part in the image. According to the edge contour, further extract the shape features. Summarize the shape features extracted from all part images to generate the guide rail shape feature set. Use the texture analysis algorithm to extract texture features and construct the guide rail texture feature set. Select a suitable deformation measurement algorithm, such as the SIFT algorithm, and compare the known standard shape features with the currently collected shape features to calculate the deformation parameters compared with the standard shape, such as the curvature and compression rate of a certain part. According to the deformation parameters, analyze the possible causes of deformation. Summarize all possible causes to form the guide rail deformation fault cause set. Use the gray level co-occurrence matrix and wavelet energy analysis method to decompose the guide rail texture features, calculate the image contrast, energy, energy ratio parameters, and obtain the guide rail loss parameters. According to the loss parameters, analyze the wear state of the guide rail and obtain the loss cause. When analyzing the wear state, focus on positioning the marked position of the LabelImg tool to judge the loss state of the guide rail insulation layer. Add the guide rail loss cause to the guide rail loss fault cause set.

[0041] S6: According to the abnormal status information library and the guide rail fault cause library, perform a correlation analysis to obtain the guide rail fault model;

[0042] Specifically, extract the relevant data from the abnormal information library and the guide rail fault cause library, and align all the data with the sequence value as the index. Select a correlation measurement method, such as the Spearman rank correlation coefficient, to calculate the correlation between the abnormal status and the fault cause, construct a correlation matrix, where the rows of the matrix represent the abnormal status information, the columns of the matrix represent the fault cause set, and the elements in the matrix are the corresponding correlation coefficients. Analyze the correlation matrix to determine the degree of association between each abnormal status and the fault, and establish the guide rail fault model.

[0043] S7: Generate a fault repair plan based on the guide rail fault model and feedback the fault repair plan to the monitoring platform.

[0044] Specifically, generate a fault repair plan according to the fault classification in the guide rail fault model. For example, if it is determined that the guide rail has a thermal deformation fault, a repair strategy of adjusting the pre-tightening force of the guide rail installation bolts can be formulated. By appropriately adjusting the tightness of the bolts and utilizing the elasticity of the guide rail itself and the characteristics of the connection structure, attempt to restore the flatness of the guide rail. Or use professional calibration tools, such as a guide rail straightening machine. The maintenance personnel need to first accurately measure the deformed part to determine the deformation amount and deformation direction, and then gradually perform the straightening operation on the guide rail according to the operation specifications of the straightening machine. Organize the generated fault repair plan in the format specified by the monitoring platform. For example, adopt a structured data format, including fields such as fault type, fault location, repair strategy, repair resource requirements, maintenance personnel requirements, and estimated repair time. Transmit the formatted fault repair plan data to the monitoring platform.

[0045] Furthermore, step S2 of this application further includes:

[0046] Continuously collect the speed information, vibration information, and current information during the operation of the guide rail through the multi-source sensors, and adopt the IEEE 1588 Precision Time Protocol to ensure that the multi-parameter sampling time synchronization error is less than the set threshold;

[0047] Add the speed information, vibration information, and current information collected by the same group of sensors to the same state information group, perform sequence value marking according to the multi-source sensor serial number, and integrate multiple state information groups to construct the state information library of the bakelite guide rail.

[0048] Specifically, each group of multi-source sensors integrates three types of monitoring devices: a speed sensor, a vibration sensor, and a current sensor. The speed sensor is a non-contact optical encoder, the vibration sensor is a three-axis MEMS accelerometer, and the current sensor is a closed-loop Hall current sensor. At the location where the edge computing node is deployed, the sensors are configured for wireless communication to ensure that each group of multi-source sensors can establish a stable communication connection with the nearest edge computing node, thus forming a distributed data acquisition network. The formation of this network can ensure continuous state perception of the entire length of the guide rail, covering all working ranges of the guide rail and avoiding perception blind spots. A multi-source sensor time synchronization mechanism is set up, and the IEEE 1588 Precision Time Protocol is used to align the time reference of multi-source data. Through this protocol, it is ensured that the data collected by different sensors are highly synchronized in time. The IEEE 1588 Precision Time Protocol (PTP) is a protocol used to achieve high-precision time synchronization in Ethernet. Its working principle is to establish a master-slave hierarchy and achieve time and frequency synchronization by exchanging various messages. The sensor devices are debugged to receive the time synchronization signal from the master clock source, and their respective sampling clocks are adjusted according to this signal, so that the error in the acquisition of speed, vibration, and current data in time is less than 1 μs. When the bakelite guide rail is running, data is collected at a preset sampling frequency to obtain the original speed information, vibration information, and current information. Operations such as filtering, noise reduction, and amplification are performed on the collected original information. For example, for the speed information collected by the speed sensor, a low-pass filter is used to remove high-frequency noise to make the information smoother and accurately reflect the true speed of the guide rail. For the data collected by the sensors in the same group, the speed information collected by the speed sensor, the vibration information collected by the vibration sensor, and the current information collected by the current sensor are integrated into the same state information group. Each state information group is marked with an ordinal value according to the deployment order of the multi-source sensors on the guide rail, such as the 1st group, the 2nd group, etc. Multiple state information groups with ordinal value marks are integrated to construct the state information database of the bakelite guide rail. The state information database adopts a multi-dimensional matrix structure, with the ordinal value mark of the sensor unit as the row index and the three types of monitoring data of speed, vibration, and current as the column fields, forming a standardized data storage framework. For example, if there are 10 groups of multi-source sensors, the matrix of the state information database will have 10 rows and 3 columns.

[0049] Further, step S3 of this application further includes:

[0050] The isolation forest algorithm is used to process the speed information in the state information database, extract abnormal speed data deviating from the normal mode, and generate an abnormal speed data set;

[0051] Based on the K-Means clustering algorithm, the abnormal vibration data in the state information database is selected to generate an abnormal vibration data set;

[0052] Use the box plot method to define the guide rail current threshold in the normal operating state, screen the abnormal current data in the status information library, and generate an abnormal current data set;

[0053] Obtain the ordinal values corresponding to each abnormal data, link the abnormal data under the same ordinal value, and integrate the abnormal speed data set, abnormal vibration data set, and abnormal current data set into the abnormal status information library.

[0054] Specifically, process the speed information in the status information library based on the Isolation Forest algorithm. The algorithm performs multiple random splitting operations on the distribution of speed information to form multiple isolation trees. Determine whether the data is abnormal speed data based on the path length of the data points in the isolation tree. Divide the speed anomaly types according to the marked abnormal speed data. Analyze the change of speed values in the sequence. Judge whether a mutation occurs by calculating the difference between adjacent speed values. If the absolute value of the speed derivative exceeds 0.25 m / s², define the speed anomaly type as a speed mutation type anomaly. Use the speed mean value within a long time window as the steady-state reference value, and calculate the speed mean value within a short time window. When the difference between the two mean values exceeds the preset threshold of 0.05 m / s, the relevant abnormal speed data is classified as a speed steady-state offset type anomaly. Calculate the volatility of the speed value, set the speed volatility threshold to 3% of the speed mean value. If the volatility exceeds this threshold, it is considered that the speed volatility exceeds the limit, and the corresponding speed data is marked as an abnormal speed volatility exceeding the limit. Finally, extract all the speed data marked as abnormal in the structure of ordinal value, speed value, and speed anomaly type to form an abnormal speed data set.

[0055] Specifically, use the K-Means clustering algorithm to analyze and process the vibration information captured by the vibration sensor. Determine the initial clustering center of the K-Means clustering algorithm based on the structural characteristics and operating mode of the guide rail. According to the K-Means clustering results, determine the vibration frequencies far from the normal clustering center as abnormal vibration data. Divide the vibration anomaly types according to the marked abnormal vibration data. Analyze the frequency values of the vibration information. If the main frequency > 500 Hz, judge the abnormal vibration type as a high-frequency impact anomaly. If the main frequency < 50 Hz, judge the abnormal vibration type as an unbalanced vibration anomaly. Form an abnormal vibration data set with the vibration data determined to be abnormal, including ordinal value, frequency value, and vibration anomaly type.

[0056] Specifically, a box plot is drawn, the lower quartile, upper quartile, and interquartile range are calculated, and the current threshold for the normal operation of the bakelite guide rail is defined. The current information that deviates from the standard threshold is marked as abnormal current data, and the specific type of current abnormality is judged according to the current value. If the current value is lower than the specific threshold, the abnormal current type is judged as undercurrent abnormality. If the current value is higher than the specific threshold, the abnormal current type is judged as overcurrent abnormality. The current data that meets the abnormal judgment criteria, including the sequence value, current value, and current abnormality type, are summarized to generate an abnormal current data set.

[0057] Through data association, the abnormal data under the same sequence value are integrated. For example, an index table is established, with the sequence value as the index, and the corresponding abnormal speed data, abnormal vibration data, and abnormal current data are associated. The integrated abnormal speed data set, abnormal vibration data set, and abnormal current data set are stored in a unified abnormal status information database.

[0058] Furthermore, step S4 of this application further includes:

[0059] According to the abnormal sequence value, the visual sensor group with the same number is called to obtain the image of the faulty section of the bakelite guide rail. Based on the traveling wave positioning method, the specific location of the current fault is determined. The LabelImg annotation tool is used to mark the location on the image, and the images are collected into the guide rail part image set;

[0060] Specifically, based on the abnormal sequence value data in the abnormal status information database, the visual sensor group with the corresponding number is triggered. After receiving the trigger instruction, the visual sensor group performs image acquisition according to the preset parameters to obtain the guide rail part image. When a current fault occurs in the guide rail, a traveling wave will be generated at the fault point and propagate in all directions. According to the arrival time of the traveling wave detected by the current sensors in the sensor group, the time difference of the traveling wave reaching different detection points is obtained. Combining with the propagation speed of the traveling wave, the specific location of the current fault in the guide rail is calculated using the traveling wave positioning formula. For example, for a simple case of two detection points, if the propagation speed of the traveling wave is v, the arrival times of the traveling wave at the two detection points are t1 and t2 respectively, and the distance between the two detection points is d, then the fault location x can be calculated by the formula The calculation is obtained. The LabelImg tool is used to process the guide rail part image, and the selected marking shape is used to accurately mark the current fault location on the guide rail part image. All the guide rail part images are integrated to generate the guide rail part image set.

[0061] Furthermore, step S5 of this application further includes:

[0062] The guide rail part image set is processed by image analysis technology, and feature extraction is performed on the guide rail part image to obtain the guide rail shape feature set and the guide rail texture feature set;

[0063] Combined with the deformation measurement algorithm, perform deformation analysis on the rail shape feature set, obtain the rail deformation parameters, and construct the rail deformation fault cause set;

[0064] Combined with the loss measurement method, perform loss analysis on the rail texture feature set, obtain the rail loss parameters, and construct the rail loss fault cause set;

[0065] Align the rail deformation fault cause set and the rail loss fault cause set according to the sequence value, and construct the rail fault cause library.

[0066] Specifically, preprocess the rail part image set, and perform operations such as grayscaling, noise reduction, and enhancement on the part images. For example, use the Gaussian filtering algorithm to remove the noise in the image, improve the clarity of the image, enhance the contrast through histogram equalization, and highlight the texture features of the rail surface for subsequent feature extraction. Adopt the edge detection algorithm to extract the rail shape features, such as the Canny edge detection algorithm. This algorithm determines the position of the edge by calculating the gradient of the image, thereby obtaining the contour shape of the rail, and then constructs the rail shape feature set. Use the gray-level co-occurrence matrix method to extract texture features. Generate the gray-level co-occurrence matrix by calculating the occurrence frequency of pixel pairs with different gray values in the image, thereby constructing the rail texture feature set. Process the image through the SIFT algorithm, establish an affine transformation model to solve the parameter offset, and at the same time calculate the rail curvature and compression rate by selecting points under the set coordinate system, and accordingly construct the rail deformation fault cause set. Obtain the rail loss degree through the gray-level co-occurrence matrix and wavelet energy analysis, and construct the rail loss fault cause set. Using the sequence value as the row index, take the rail deformation fault cause set and the rail loss fault cause set as two columns respectively, and align them in the order of the sequence value to form a rail fault cause library in a table-like structure for subsequent correlation analysis.

[0067] Furthermore, this application also includes:

[0068] Extract the feature points on the rail part image through the SIFT algorithm, establish an affine transformation model between the reference image and the detected image, solve the deformation parameter matrix, and calculate the local coordinate offset;

[0069] Establish a Cartesian coordinate system with the image center as the origin, the rail extension direction as the x-axis, and the vertical direction as the y-axis. Select three feature points at equal intervals along the longitudinal direction of the rail to calculate the rail curvature. The formula is:

[0070] ;

[0071] where k is the curvature, x1, y1 are the horizontal and vertical coordinates of feature point 1, x2, y2 are the horizontal and vertical coordinates of feature point 2, and x3, y3 are the horizontal and vertical coordinates of feature point 3;

[0072] Based on the selected feature points, continue to calculate the rail compression rate. The formula is:

[0073] ;

[0074] where e is the compression ratio, and L0 represents the initial straight length of the guide rail when there is no external force and no deformation occurs.

[0075] Determine the cause of the deformation fault of the guide rail according to the curvature and compression ratio of the guide rail, integrate the causes of the deformation faults of multiple images, and assign sequence values to construct a set of causes of the deformation faults of the guide rail.

[0076] Specifically, use the SIFT algorithm to extract the feature points in the image of the guide rail parts. The SIFT algorithm can detect representative feature points in different scale spaces. These feature points contain the key information of the image, such as local shape and other features. Based on the extracted feature points, detect the extreme points through the Difference of Gaussian pyramid, and generate a 128-dimensional feature descriptor. Establish the corresponding relationship between the feature points of the reference image and the detected image, and construct an affine transformation model. Affine transformation is a linear transformation that can describe the transformation relationships such as translation, rotation, scaling, and shearing between images. Through this model, the corresponding relationship between the feature points of the two images can be found. Solve the deformation parameter matrix of the affine transformation model. This matrix contains various parameter information about the image transformation, such as the rotation angle, scaling ratio, etc. By solving the matrix, further calculate the offset of the local coordinates. This offset reflects the position change of a certain local area in the image between the reference image and the detected image, and determine whether there is an installation stress release or thermal deformation fault of the guide rail according to the offset.

[0077] Establish a Cartesian coordinate system with the center of the image as the origin, stipulate that the extending direction of the guide rail is the x-axis, and the direction perpendicular to the extending direction of the guide rail is the y-axis. Select three feature points P1(x1, y1), P2(x2, y2), and P3(x3, y3) at equal intervals along the longitudinal direction of the guide rail. Calculate the curvature of the guide rail through the selected feature points. The formula is:

[0078] ;

[0079] where x and y are the horizontal and vertical coordinates of the three feature points. When K > 0.15m -1 it is determined that the guide rail has abnormal bending, and the cause of the fault is defined as foundation settlement or eccentric load.

[0080] Calculate the compression ratio e of the guide rail based on the selected feature points. The formula is:

[0081] ;

[0082] Among them, L0 represents the initial straight length of the guide rail when there is no external force and no deformation occurs, and the numerator represents the actual length of the guide rail after deformation. The principle of the formula is to measure the degree of compression of the guide rail by comparing the ratio of the difference between the initial length and the actual length of the guide rail to the initial length. When e > 0.3%, the cause of the guide rail failure is defined as material creep or impact load.

[0083] Integrate the deformation fault causes corresponding to the images of each part, and construct a set of guide rail deformation fault causes numbered by sequence values.

[0084] Furthermore, this application also includes:

[0085] Use the gray-level co-occurrence matrix to analyze the image contrast and energy, and obtain the wear depth of the guide rail;

[0086] Decompose the texture features of the guide rail according to the wavelet energy analysis method to obtain the sub-band energy and sub-band energy ratio parameters;

[0087] Determine the loss degree of the guide rail according to the wear depth, energy, sub-band energy, and sub-band energy ratio parameters of the guide rail, obtain the cause of the guide rail loss failure, integrate the loss failure causes of multiple images, and assign sequence values to construct a set of guide rail loss failure causes;

[0088] Specifically, based on the previously constructed gray-level co-occurrence matrix, calculate the contrast and energy, and the formulas are as follows:

[0089] Contrast: ;

[0090] Among them, P(i,j) represents the value of the element (i,j) in the gray-level co-occurrence matrix, and n represents the number of gray levels. For every 0.1 increase in the contrast C, the wear depth increases by 12μm. Locate the current fault position marked by the LabelImg tool. If the wear depth at this position is greater than 0.3mm, it is determined that the insulation layer is damaged.

[0091] Energy: ;

[0092] Among them, P(i,j) represents the value of the element (i,j) in the gray-level co-occurrence matrix, and n represents the number of gray levels. If the energy E drops by more than 15%, it is determined that the lubrication of the guide rail fails.

[0093] Select a suitable wavelet basis function, such as the Daubechies wavelet, and perform wavelet decomposition on the guide rail texture image. Use multi-layer wavelet decomposition to decompose the image into a low-frequency sub-band and a high-frequency sub-band. Calculate the energy of each sub-band, and the formula is:

[0094] ;

[0095] Among them, I k(x, y) is the pixel value at coordinates (x, y) in the k-th subband image. This formula indicates that all pixel points in the subband image need to be traversed, and the squared values of each pixel value are accumulated to obtain the subband energy E. k .

[0096] By comparing the sum of the high-frequency subband energies with the low-frequency subband energy, the energy ratio parameter is obtained. Assume that the high-frequency subbands include LH3, HL3, HH3, and the low-frequency subband is LL3. The frequency band energy ratio is:

[0097] ;

[0098] where E LH3 , E HL3 , E HH3 , E LL3 represent the energies of subbands LH3, HL3, HH3, and LL3 respectively. If D > 18%, it is determined that the guide rail loss fault is abrasive wear; if D < 5%, it is determined that the guide rail loss fault is adhesive wear.

[0099] Integrate the loss fault causes corresponding to each part image, and construct a guide rail loss fault cause set numbered by sequence values.

[0100] Furthermore, step S6 of this application also includes:

[0101] Extract the data in the abnormal state information library and the guide rail fault cause library, and align the data according to the sequence values;

[0102] Use the Spearman rank correlation coefficient to conduct a correlation analysis on the abnormal state and the fault cause. The formula is as follows:

[0103] ;

[0104] where n is the sample size, and R xi and R yi are the ranks of the i-th observation values in variables X and Y respectively;

[0105] Obtain the guide rail fault model based on the correlation analysis results.

[0106] Specifically, extract the abnormal speed data set, abnormal vibration data set, and abnormal current data set in the abnormal state information library, and the guide rail deformation fault cause set and the guide rail loss fault cause set in the guide rail fault cause library. Use the sequence value as the core alignment dimension to achieve precise matching of the data sets, ensuring that each abnormal state information data corresponds logically to the corresponding fault cause data. Arrange and combine the abnormal speed data set, abnormal vibration data set, and abnormal current data set with the guide rail deformation fault cause set and the guide rail loss fault cause set respectively to construct abnormal state - fault cause combination pairs, and conduct correlation calculations.

[0107] According to the calculated correlation coefficient r, the degree of correlation between the abnormal state and the cause of the fault can be judged. If r is relatively high and close to 1, it indicates that the change of the abnormal state has a strong correlation with the cause of the fault, and it may be an important indicator of this abnormal state. If r is relatively low and close to 0, it indicates that the correlation between the two is weak, and it may be necessary to further analyze other factors.

[0108] Based on the judgment of the correlation coefficient, the main causes of each abnormal state are obtained. For example, if the abnormal state of this node is a speed mutation type abnormality and a high-frequency impact abnormality, according to the correlation coefficient, it can be judged that the cause of the fault at this stage is installation stress release or thermal deformation fault. An integrated learning algorithm is used to construct a guide rail fault model, which can quickly judge the possible fault types according to the input abnormal state information, greatly shortening the fault detection time.

[0109] Embodiment 2. Based on the same inventive concept as a bakelite guide rail fault monitoring method in the foregoing embodiment, the present application also provides a bakelite guide rail fault monitoring system. Please refer to the attached Figure 2 , the system includes:

[0110] A sensor layout module 11, which is used to establish a digital connection between the monitoring platform and the distributed sensor network. The distributed sensor network includes M groups of multi-source sensors and M groups of visual sensor groups. The multi-source sensors are evenly deployed on the edge of the bakelite guide rail, and the visual sensor groups are arranged around the guide rail in a ring according to the installation nodes of the multi-source sensors. Among them, the multi-source sensors include speed sensors, vibration sensors, and current sensors;

[0111] A state information library construction module 12, which is used to continuously collect multi-dimensional physical quantities during the operation of the guide rail through the multi-source sensors, and construct a state information library. The state information library includes speed information, vibration information, current information, and sequence values;

[0112] An abnormal state information library generation module 13, which is used to judge and extract abnormal data in the state information library based on an anomaly detection algorithm, and generate an abnormal state information library. The abnormal state information library includes an abnormal speed data set, an abnormal vibration data set, an abnormal current data set, and an abnormal sequence value;

[0113] A fault image acquisition module 14, which is used to call the visual sensor group to acquire images of the fault section of the bakelite guide rail according to the abnormal sequence value, and obtain a set of guide rail part images;

[0114] The fault cause analysis module 15 is used to analyze the rail part image set and obtain a rail fault cause library, which includes a rail deformation fault cause set and a rail loss fault cause set.

[0115] The fault model construction module 16 is used to perform a correlation analysis based on the abnormal state information library and the rail fault cause library to obtain a rail fault model.

[0116] The fault repair plan generation and feedback module 17 is used to generate a fault repair plan according to the rail fault model and feedback the fault repair plan to the monitoring platform.

[0117] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0118] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the failure of a bakelite guide rail, characterized in that, The method includes: Establish a digital connection between the monitoring platform and the distributed sensor network. The distributed sensor network includes M groups of multi-source sensors and M groups of visual sensor groups. The multi-source sensors are deployed at equal intervals on the edge of the bakelite guide rail, and the visual sensor groups are arranged around the guide rail according to the installation nodes of the multi-source sensors. Among them, the multi-source sensors include speed sensors, vibration sensors, and current sensors; Continuously collect multi-dimensional physical quantities during the operation of the guide rail through the multi-source sensors, and construct a status information library. The status information library includes speed information, vibration information, current information, and sequence values; Based on the anomaly detection algorithm, judge and extract the abnormal data in the status information library to generate an abnormal status information library. The abnormal status information library includes an abnormal speed data set, an abnormal vibration data set, an abnormal current data set, and an abnormal sequence value; According to the abnormal sequence value, call the visual sensor group to collect images of the faulty section of the bakelite guide rail to obtain a guide rail part image set; Analyze the guide rail part image set to obtain a guide rail fault cause library. The guide rail fault cause library includes a guide rail deformation fault cause set and a guide rail loss fault cause set; Perform a correlation analysis based on the abnormal status information library and the guide rail fault cause library to obtain a guide rail fault model; Generate a fault repair plan according to the guide rail fault model and feedback the fault repair plan to the monitoring platform.

2. The method for monitoring the failure of a bakelite guide rail according to claim 1, characterized in that, Construct a status information library, including: Continuously collect speed information, vibration information, and current information during the operation of the guide rail through the multi-source sensors, and use the IEEE 1588 Precision Time Protocol to ensure that the multi-parameter sampling time synchronization error is less than the set threshold; Add the speed information, vibration information, and current information collected by the same group of sensors to the same status information group, mark the sequence value according to the serial number of the multi-source sensors, and integrate multiple status information groups to construct the status information library of the bakelite guide rail.

3. The method for monitoring the failure of a bakelite guide rail according to claim 1, characterized in that, Generate an abnormal status information library, including: Use the Isolation Forest algorithm to process the speed information in the status information library, extract abnormal speed data deviating from the normal mode, and generate an abnormal speed data set; Based on the K-Means clustering algorithm, select the abnormal vibration data in the status information library to generate an abnormal vibration data set; Use the box plot method to define the guide rail current threshold in the normal operation state, screen the abnormal current data in the status information library, and generate an abnormal current data set; Obtain the sequence values corresponding to each abnormal data, link the abnormal data under the same sequence value, and integrate the abnormal speed data set, abnormal vibration data set, and abnormal current data set into an abnormal status information library.

4. The method for monitoring the failure of a bakelite guide rail according to claim 1, wherein, Obtain a guide rail part image set, including: According to the abnormal sequence value, call the visual sensor group with the same number, obtain the image of the faulty section of the bakelite guide rail, determine the specific location of the current fault based on the traveling wave positioning method, use the LabelImg annotation tool to mark the position of the image, and collect the images into the guide rail part image set.

5. The method for monitoring the failure of a bakelite guide rail according to claim 1, wherein Obtain a guide rail fault cause library, including: Process the guide rail part image set through image analysis technology, extract the features of the guide rail parts images, and obtain a guide rail shape feature set and a guide rail texture feature set; Combine the deformation measurement algorithm to analyze the deformation of the guide rail shape feature set, obtain the guide rail deformation parameters, and construct the guide rail deformation fault cause set; Combine the loss measurement method to analyze the loss of the guide rail texture feature set, obtain the guide rail loss parameters, and construct the guide rail loss fault cause set; Align the guide rail deformation fault cause set and the guide rail loss fault cause set according to the sequence value to construct the guide rail fault cause library.

6. The method for monitoring the failure of a bakelite guide rail according to claim 5, wherein, Construct the guide rail deformation fault cause set, including: Extract the feature points on the guide rail part image through the SIFT algorithm, establish the affine transformation model between the reference image and the detection image, solve the deformation parameter matrix, and calculate the local coordinate offset; A Cartesian coordinate system is established with the center of the image as the origin, the extending direction of the guide rail as the x-axis, and the vertical direction as the y-axis. Three characteristic points are selected at equal intervals along the longitudinal direction of the guide rail to calculate the curvature of the guide rail. The formula is as follows: ; Where k is the curvature, x1, y1 are the horizontal and vertical coordinates of feature point 1, x2, y2 are the horizontal and vertical coordinates of feature point 2, and x3, y3 are the horizontal and vertical coordinates of feature point 3; Continue to calculate the guide rail compression rate based on the selected feature points. The formula is: ； Where e is the compression rate, and L0 represents the initial straight-line length of the guide rail when there is no external force and no deformation occurs; Determine the guide rail deformation fault cause according to the guide rail curvature and compression rate, integrate the deformation fault causes of multiple images, and assign sequence values to construct the guide rail deformation fault cause set.

7. The method for monitoring the failure of a bakelite guide rail according to claim 5, characterized in that, Construct the guide rail loss fault cause set, including: Use the gray-level co-occurrence matrix to analyze the image contrast and energy to obtain the guide rail wear depth; Decompose the guide rail texture features according to the wavelet energy analysis method to obtain the sub-band energy and sub-band energy ratio parameters; Determine the guide rail loss degree according to the guide rail wear depth, energy, sub-band energy, and sub-band energy ratio parameters, obtain the guide rail loss fault cause, integrate the loss fault causes of multiple images, and assign sequence values to construct the guide rail loss fault cause set.

8. The method for monitoring the failure of a bakelite guide rail according to claim 1, characterized in that, Obtain the guide rail fault model, including: Extract the data in the abnormal state information library and the guide rail fault cause library, and align the data according to the sequence value; Use the Spearman rank correlation coefficient to analyze the correlation between the abnormal state and the fault cause. The formula is as follows: ； where n is the sample size, R xi and R yi are the ranks of the i-th observations in variables X and Y, respectively; Obtain the guide rail fault model according to the correlation analysis results.

9. An electrical wood guide rail fault monitoring system, characterized in that, The system includes: The sensor layout module is used to establish a digital connection between the monitoring platform and the distributed sensor network. The distributed sensor network includes M groups of multi-source sensors and M groups of visual sensor groups. The multi-source sensors are evenly deployed on the edge of the bakelite guide rail, and the visual sensor groups are arranged around the guide rail according to the installation nodes of the multi-source sensors. Among them, the multi-source sensors include speed sensors, vibration sensors, and current sensors; The state information library construction module is used to continuously collect multi-dimensional physical quantities during the operation of the guide rail through the multi-source sensors, and construct the state information library. The state information library includes speed information, vibration information, current information, and sequence values; The abnormal state information library generation module is used to judge and extract the abnormal data in the state information library based on the abnormal detection algorithm, and generate the abnormal state information library. The abnormal state information library includes abnormal speed data sets, abnormal vibration data sets, abnormal current data sets, and abnormal sequence values; Fault image acquisition module, which is used to call the vision sensor group according to the abnormal sequence value to acquire images of the faulty section of the bakelite guide rail, and obtain a set of guide rail part images; Fault cause analysis module, which is used to analyze the set of guide rail part images to obtain a guide rail fault cause library, and the guide rail fault cause library includes a guide rail deformation fault cause set and a guide rail loss fault cause set; Fault model construction module, which is used to perform correlation analysis based on the abnormal state information library and the guide rail fault cause library to obtain a guide rail fault model; Fault repair plan generation and feedback module, which is used to generate a fault repair plan based on the guide rail fault model and feedback the fault repair plan to the monitoring platform.

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