Gate hoist fault prediction method and system based on data analysis

Abstract

The invention relates to the technical field of gate hoist fault prediction, in particular to a gate hoist fault prediction method and system based on data analysis. The method comprises the following steps: collecting a vibration signal of a gate hoist, carrying out segmented regularization and abnormal value correction, analyzing an opening and closing time sequence, judging a conventional opening and closing period based on the time sequence, evolving a healthy opening and closing track, carrying out normal opening and closing simulation to generate simulation data, and analyzing the abrasion condition of gate movement. And state estimation is carried out on the wear data according to the use duration, the difference between the ideal gate state and the current state is determined, fault backtracking and analysis are carried out through the difference to identify abnormal parameters, and fault prediction is carried out by using a random forest model. According to the invention, active monitoring and early warning of the health state of the equipment are realized, the intelligent level of equipment management is improved, the influence of human factors on equipment maintenance is reduced, and the safety of overall operation of a water conservancy project is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of gate hoist fault prediction, and in particular to a gate hoist fault prediction method and system based on data analysis. Background Art

[0002] Traditional gate hoist fault monitoring methods often rely on manual maintenance and experience judgment, resulting in insufficient real-time control of equipment status. Traditional methods are difficult to cope with complex and dynamic working environments, thus greatly increasing the risk of potential equipment failures, leading to soaring management costs and unstable equipment operation, seriously affecting the safety and efficiency of the project. At the same time, existing vibration monitoring technologies often have signal noise interference, inability to effectively identify abnormal values, and lack of systematic analysis methods in practical applications. The monitoring signal is easily affected by environmental noise, resulting in unstable signal quality, and insufficient abnormal value recognition capabilities, making it difficult to timely and accurately identify sudden abnormal situations. Many monitoring systems are unable to detect abnormal conditions of equipment during operation in a timely manner due to lack of real-time performance, and are unable to provide comprehensive health status assessments, which poses a hidden danger to the safe use of gates. Traditional methods are deficient in both adaptability and accuracy, resulting in many potential problems that have not been effectively dealt with in a timely manner. Summary of the invention

[0003] Based on this, it is necessary to provide a gate hoist fault prediction method and system based on data analysis to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a gate hoist fault prediction method based on data analysis includes the following steps: Step S1: collecting the vibration signal of the gate hoist; performing segmented regularization processing on the vibration signal of the gate hoist, and correcting the abnormal value to obtain a cleaning characteristic signal; analyzing the gate opening and closing timing according to the clear characteristic signal; Step S2: judging the normal opening and closing cycle based on the gate opening and closing sequence; evolving the gate healthy opening and closing trajectory according to the normal opening and closing cycle; Step S3: Perform gate normal opening and closing simulation according to the gate healthy opening and closing trajectory to obtain simulated gate normal opening and closing data; analyze gate movement wear data based on the simulated gate normal opening and closing data; Step S4: obtaining gate status data; estimating gate status based on gate movement wear data according to preset gate usage time, generating an ideal gate status; determining the gate status gap based on the gate status data and the ideal gate status; Step S5: Fault backtracking is performed through the gate state gap, and the hoist transmission gear fault data is analyzed based on the backtracked fault data; abnormal parameters of the gate hoist are identified according to the hoist transmission gear fault data; Step S6: Predict gate hoist failure based on the gate hoist abnormal parameters based on the random forest model to generate predicted gate hoist failure data.

[0005] The present invention collects the vibration signal of the gate opening and closing machine and monitors the operating status of the equipment in real time, making the health assessment of the gate opening and closing machine more reliable. The segmented regularization processing and outlier correction effectively improve the signal quality and ensure the accuracy of subsequent analysis. The extraction of cleaning characteristic signals provides a solid data foundation for accurately identifying the gate opening and closing timing. The analysis based on clear characteristic signals enables timely discovery of potential fault signs, thus creating conditions for maintenance work. Judging the regular opening and closing cycle based on the gate opening and closing timing helps to build a healthy opening and closing trajectory, providing a standard basis for the health management of the equipment. The evolved healthy opening and closing trajectory lays a solid foundation for subsequent fault prediction. The simulation data generated by the normal opening and closing simulation of the gate helps to understand the performance of the equipment under normal operating conditions. The analysis of the gate movement wear data enables better identification of potential performance degradation problems. The function of obtaining the gate status data ensures a comprehensive understanding of the actual status of the equipment. The state is estimated by presetting the usage time. The generated ideal state provides comparison parameters for the actual situation. Based on the comparison between the ideal state and the current data, the state gap of the equipment can be clarified, and the fault can be predicted. The fault backtracking realizes the tracing of the causes of historical faults by analyzing the state gap, and the calculated abnormal parameters provide an important reference for subsequent fault inspection and maintenance. Relying on the analysis of transmission gear fault data, it can accurately identify the specific problems existing in the operation of the equipment. The introduction of the random forest model realizes the intelligent prediction of the abnormal parameters of the gate hoist. The generated prediction data provides a reliable basis for technicians, effectively reduces the risk of equipment failure, and significantly improves the stability and reliability of the equipment. By integrating signal monitoring, data analysis and fault prediction, it realizes active monitoring and early warning of the health status of the equipment, improves the intelligent level of equipment management, reduces the impact of human factors on equipment maintenance, and promotes the safety of the overall operation of water conservancy projects. It establishes a new data-driven management model for the industry, improves the operating efficiency and life of equipment, promotes the in-depth application and expansion of intelligent technology in the water conservancy industry, and provides important technical support for the scientific and effective equipment management in the future. Finally, it realizes the dynamic adaptation and optimization of equipment health management in a complex environment, forming a sustainable intelligent monitoring system.

[0006] The present invention also provides a gate hoist fault prediction system based on data analysis, which is used to execute the gate hoist fault prediction method based on data analysis as described above. The gate hoist fault prediction system based on data analysis includes: A signal cleaning module, which is used to collect the vibration signals of the gate hoist; perform segmented regularization processing on the vibration signals of the gate hoist and correct the outliers to obtain cleaned feature signals; analyze the gate opening and closing timings based on the cleaned feature signals; A period determination module, which is used to judge the regular opening and closing period based on the gate opening and closing timings; evolve the healthy opening and closing trajectory of the gate according to the regular opening and closing period; An opening and closing simulation module, which is used to perform normal opening and closing simulation of the gate according to the healthy opening and closing trajectory of the gate to obtain simulated normal opening and closing data of the gate; analyze the gate movement wear data based on the simulated normal opening and closing data of the gate; A state evaluation module, which is used to obtain the current status data of the gate; perform gate state estimation on the gate movement wear data according to the preset gate usage duration to generate an ideal gate state; determine the gate state gap based on the current status data of the gate and the ideal gate state; A fault backtracking module, which is used to perform fault backtracking through the gate state gap and analyze the hoist drive gear fault data based on the backtracked fault data; identify the abnormal parameters of the gate hoist according to the hoist drive gear fault data; A fault prediction module, which is used to perform hoist fault prediction on the abnormal parameters of the gate hoist based on a random forest model to generate predicted gate hoist fault data.

[0007] The present invention can realize accurate collection and processing of gate hoist vibration signals through the application of signal cleaning module, ensuring that the obtained signal data has high reliability. The segmented regularization processing and outlier correction significantly improve the signal quality, providing an accurate basis for subsequent analysis. The extraction of cleaning characteristic signals can provide necessary data support for effective analysis of gate opening and closing timing. The introduction of cycle determination module can scientifically judge the conventional opening and closing cycle, effectively evolve healthy opening and closing trajectory, and provide standard basis for equipment health management. The simulation data generated by the opening and closing simulation module effectively reflects the characteristics of the equipment under normal operating conditions, which is helpful for in-depth analysis of gate movement wear data, and then identify and solve potential wear problems. The state evaluation module obtains the gate status data to provide a comprehensive perspective for the equipment status. When performing state estimation, the preset usage time is combined with the wear data to generate an ideal gate state, so that the gap between the ideal state and the current state becomes quantifiable. The fault backtracking module can trace back historical faults and extract key data by analyzing the state gap, promote in-depth analysis of the gate hoist transmission gear fault, and identify the gate hoist. The abnormal parameters of the equipment can provide strong support for subsequent fault prediction. The fault prediction module is based on the application of data analysis, which enables highly accurate fault prediction of abnormal parameters, thereby generating reliable fault prediction data, providing a scientific basis for preventive maintenance of equipment, significantly reducing the risk of sudden failures, and improving the overall reliability and safety of the equipment. It integrates various links of signal monitoring, data analysis and fault prediction, realizes active monitoring and early warning of equipment health status, promotes the intelligent level of equipment management, reduces the interference of human factors on equipment maintenance, enhances the predictability of equipment failures, and improves the efficiency of maintenance work. Through the data-driven management model, it lays the foundation for the safe and stable operation of water conservancy projects, which not only improves the operating efficiency of equipment and the scientific nature of life cycle management, but also promotes the widespread application of intelligent technology in the water conservancy industry, forming an effective and sustainable intelligent fault early warning mechanism, and provides valuable experience and foundation for future technological innovation and application promotion of equipment management, promotes the process of modernization of equipment management in the water conservancy field, and finally realizes the dynamic adaptation and optimization of equipment health management in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the steps of a gate hoist fault prediction method based on data analysis; Figure 2 Detailed implementation flow chart of step S2; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0009] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0010] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0012] To achieve this, please refer to Figure 1 to Figure 2 , a gate hoist fault prediction method based on data analysis, comprising the following steps: Step S1: collecting the vibration signal of the gate hoist; performing segmented regularization processing on the vibration signal of the gate hoist, and correcting the abnormal value to obtain a cleaning characteristic signal; analyzing the gate opening and closing timing according to the clear characteristic signal; Step S2: judging the normal opening and closing cycle based on the gate opening and closing sequence; evolving the gate healthy opening and closing trajectory according to the normal opening and closing cycle; Step S3: Perform gate normal opening and closing simulation according to the gate healthy opening and closing trajectory to obtain simulated gate normal opening and closing data; analyze gate movement wear data based on the simulated gate normal opening and closing data; Step S4: obtaining gate status data; estimating gate status based on gate movement wear data according to preset gate usage time, generating an ideal gate status; determining the gate status gap based on the gate status data and the ideal gate status; Step S5: Fault backtracking is performed through the gate state gap, and the hoist transmission gear fault data is analyzed based on the backtracked fault data; abnormal parameters of the gate hoist are identified according to the hoist transmission gear fault data; Step S6: Predict gate hoist failure based on the gate hoist abnormal parameters based on the random forest model to generate predicted gate hoist failure data.

[0013] The present invention collects the vibration signal of the gate opening and closing machine and monitors the operating status of the equipment in real time, making the health assessment of the gate opening and closing machine more reliable. The segmented regularization processing and outlier correction effectively improve the signal quality and ensure the accuracy of subsequent analysis. The extraction of cleaning characteristic signals provides a solid data foundation for accurately identifying the gate opening and closing timing. The analysis based on clear characteristic signals enables timely discovery of potential fault signs, thus creating conditions for maintenance work. Judging the regular opening and closing cycle based on the gate opening and closing timing helps to build a healthy opening and closing trajectory, providing a standard basis for the health management of the equipment. The evolved healthy opening and closing trajectory lays a solid foundation for subsequent fault prediction. The simulation data generated by the normal opening and closing simulation of the gate helps to understand the performance of the equipment under normal operating conditions. The analysis of the gate movement wear data enables better identification of potential performance degradation problems. The function of obtaining the gate status data ensures a comprehensive understanding of the actual status of the equipment. The state is estimated by presetting the usage time. The generated ideal state provides comparison parameters for the actual situation. Based on the comparison between the ideal state and the current data, the state gap of the equipment can be clarified, and the fault can be predicted. The fault backtracking realizes the tracing of the causes of historical faults by analyzing the state gap, and the calculated abnormal parameters provide an important reference for subsequent fault inspection and maintenance. Relying on the analysis of transmission gear fault data, it can accurately identify the specific problems existing in the operation of the equipment. The introduction of the random forest model realizes the intelligent prediction of the abnormal parameters of the gate hoist. The generated prediction data provides a reliable basis for technicians, effectively reduces the risk of equipment failure, and significantly improves the stability and reliability of the equipment. By integrating signal monitoring, data analysis and fault prediction, it realizes active monitoring and early warning of the health status of the equipment, improves the intelligent level of equipment management, reduces the impact of human factors on equipment maintenance, and promotes the safety of the overall operation of water conservancy projects. It establishes a new data-driven management model for the industry, improves the operating efficiency and life of equipment, promotes the in-depth application and expansion of intelligent technology in the water conservancy industry, and provides important technical support for the scientific and effective equipment management in the future. Finally, it realizes the dynamic adaptation and optimization of equipment health management in a complex environment, forming a sustainable intelligent monitoring system.

[0014] In an embodiment of the present invention, the gate hoist fault prediction method based on data analysis includes the following steps: Step S1: collecting the vibration signal of the gate hoist; performing segmented regularization processing on the vibration signal of the gate hoist, and correcting the abnormal value to obtain a cleaning characteristic signal; analyzing the gate opening and closing timing according to the clear characteristic signal; In this embodiment, a dynamic signal test and analysis system of model DH5922 is used, the sampling frequency is set to 5120Hz, and the three-axis acceleration sensor model is selected as PCB-356A16, which are respectively arranged on the main shaft box of the gate opening and closing machine, the motor base and the surface of the reducer box. The three-way vibration signal is synchronously collected by the synchronous signal acquisition module. After the acquisition is completed, the original vibration signal is segmented using the built-in NumPy library of the Python language, and the length of each segment is set to 1024 sampling points. The interval standardization method is used to regularize the data of each segment of the signal. The specific method is to subtract the mean of all points in each segment of the signal from the mean of the segment and divide it by the standard deviation to obtain a regularized signal sequence with a mean of 0 and a variance of 1. The abnormal value correction is performed by 3 Principle execution, calculate the mean of each segment of the regularized signal and standard deviation , will exceed the interval [ , ] is replaced by the median value in the signal segment, and finally the cleaning characteristic signal is formed. The find_peaks function in the scipy library is used to extract the main peak points in the working cycle of the gate hoist, and the time intervals between adjacent peak points are calculated. The integrity of the gate hoist's opening and closing timing is determined based on the interval stability.

[0015] Step S2: judging the normal opening and closing cycle based on the gate opening and closing sequence; evolving the gate healthy opening and closing trajectory according to the normal opening and closing cycle; In this embodiment, after the opening and closing timing is determined, based on the opening and closing cycle analysis, the average cycle value T_avg of all opening and closing cycles is calculated, and the judgment threshold is set to T_avg±5%T_avg, and the cycle data within this threshold range is screened out as the regular opening and closing cycle. According to this regular opening and closing cycle, the opening and closing displacement change curve of the gate opening and closing machine is time-normalized, and the opening and closing data in different cycles are normalized to the same time axis using the cubic spline interpolation method (Cubic Spline Interpolation), and the three key motion parameters of displacement, velocity, and acceleration are extracted. Finally, by taking the average of each normalized cycle curve, the gate healthy opening and closing trajectory curve is constructed. The specific numerical processing is based on the interp1d function in the SciPy library to complete the interpolation, and the numpy.mean function calculates the mean. Each time node of the healthy trajectory contains three parameters: normalized time t, displacement s(t), velocity v(t), and acceleration a(t), and the number of sampling points is set to 1000 points.

[0016] Step S3: Perform gate normal opening and closing simulation according to the gate healthy opening and closing trajectory to obtain simulated gate normal opening and closing data; analyze gate movement wear data based on the simulated gate normal opening and closing data; In this embodiment, according to the generated healthy opening and closing trajectory, the MATLAB Simulink platform is called to build a kinematic simulation model of the gate hoist. The model input uses the displacement-time data in the healthy trajectory, the simulation step is set to 0.001s, the motor speed range is set to 500-900rpm, and the reduction ratio is set to 50. Based on the simulation model, the driving torque, pulley rotational inertia, gate mass and friction resistance change data during the gate opening and closing process are output, and the normal opening and closing data of the simulated gate are sorted and output. The SimscapeMultibody module in MATLAB is used to simulate the pulley mechanism and the gate movement, and the driving torque data output by the simulation is corresponded to the healthy trajectory speed curve, the energy consumed per unit displacement is calculated, and the energy consumption fluctuation data during the whole process of gate opening and closing is recorded. The wear trend is further statistically analyzed, and the motion wear data is obtained by averaging the difference of continuous multi-cycle energy consumption data. The energy consumption unit is set to J / m.

[0017] Step S4: obtaining gate status data; estimating gate status based on gate movement wear data according to preset gate usage time, generating an ideal gate status; determining the gate status gap based on the gate status data and the ideal gate status; In this embodiment, in the stage of obtaining the current status data of the gate, a laser displacement sensor model KEYENCE LK-G5000 is used to measure the displacement curve of the gate opening and closing process, and the displacement sampling frequency is set to 500Hz. At the same time, a vibration sensor is used to collect the vibration signal of the main shaft of the gate hoist, and the energy consumption and displacement data of the whole opening and closing process are recorded. The current status data is aligned with the simulated healthy opening and closing data point by point. According to the cumulative use time of the gate, the motion wear data is corrected in sections. The service life data of the gate hoist is exported from the monitoring management system database, and the wear correction coefficient K_w=(T_u / T_d) is set. T_u is the current cumulative use time of the gate, and T_d is the designed use time. The corrected wear data is used to re-estimate the ideal gate state curve, and the ideal opening and closing state displacement-time curve is subtracted from the current actual opening and closing curve to generate gate state gap data. The difference exceeding 3mm is recorded as an abnormal point, and the corresponding time period is marked.

[0018] Step S5: Fault backtracking is performed through the gate state gap, and the hoist transmission gear fault data is analyzed based on the backtracked fault data; abnormal parameters of the gate hoist are identified according to the hoist transmission gear fault data; In this embodiment, the gate state gap data is used to trace back the vibration signals of the previous 30 opening and closing cycles. Based on the frequency analysis of the vibration signal, the FFT (fast Fourier transform) method is used to convert the time domain signal to the frequency domain, and the amplitude peak frequency point in the frequency range of 0-500Hz is extracted to judge the frequency drift and amplitude abnormality. If the gear meshing frequency deviates from the normal 30Hz by more than ±3Hz, or the amplitude exceeds the conventional standard, it is determined that the transmission gear is abnormal. The abnormal amplitude, frequency and corresponding time are recorded, the abnormal data is analyzed, and the transmission gear fault parameters are identified. The fault parameters include gear meshing frequency, amplitude, meshing offset, and cumulative abnormal number of times. Based on the frequency-amplitude-offset characteristics, the gate opening and closing machine abnormal parameters are extracted and summarized into five columns of data: time, frequency, amplitude, offset, and abnormal category.

[0019] Step S6: Predict gate hoist failure based on the gate hoist abnormal parameters based on the random forest model to generate predicted gate hoist failure data.

[0020] In this embodiment, based on the abnormal parameter data set, the RandomForestClassifier module in the Python sklearn library is called, the number of trees is set to 300, the maximum depth is 20, the random seed value is 42, the training data set is composed of the historical label data in the abnormal parameter data set, and the prediction data set is composed of the latest abnormal parameter set. The model training is performed, and after completion, the prediction is output as the gate hoist fault category label and probability. The prediction result fields include fault_type, probability, and cycle_id. If the predicted probability is higher than 0.7, it is judged to be a fault state, and the predicted gate hoist fault data is saved.

[0021] Preferably, step S1 comprises the following steps: Step S11: collecting vibration signals of the gate hoist in multiple frequency bands; performing wavelet denoising on the vibration signals of the gate hoist to obtain noise-reduced vibration signals; Step S12: performing segmented regularization processing on the noise reduction vibration signal, and performing adaptive segmentation based on a preset segmentation threshold to obtain a segmented vibration signal; Step S13: performing discrete point detection on the segmented vibration signal and identifying abnormal points; performing median filtering replacement on the abnormal points to obtain a cleaning characteristic signal; Step S14: Perform spectrum conversion on the cleaning characteristic signal and extract the signal characteristic envelope; extract the time domain characteristics of the signal characteristic envelope, and identify the opening and closing time nodes based on the time domain characteristics to generate the gate opening and closing timing sequence.

[0022] In this embodiment, when the vibration signal of the gate hoist is collected in multiple frequency bands, the number of frequency bands is set within the range of three to five, the lower limit frequency of the frequency band is set to fifty hertz, and the upper limit frequency of the frequency band is set to one thousand hertz. A multi-channel acceleration sensor is used to simultaneously collect the vibration signals in the X-axis, Y-axis and Z-axis directions during the movement of the gate hoist. The sampling frequency is set to two thousand and forty-eight points per second, and the sampling time is set to one hundred and twenty seconds. After the vibration signal is converted into a set of discrete vibration data, the index is from the first point to the total number of points. After the collection is completed, the vibration data is denoised using the wavelet decomposition method. The wavelet basis function is set to db6 type, and the number of decomposition layers is set to five layers. The vibration signal is decomposed into multiple discrete For sub-signals of the same frequency, all high-frequency signals are removed, and only the lowest frequency coefficients are retained. The retained coefficients are then reconstructed to finally obtain the denoised vibration signal data. The denoised vibration signal is segmented according to the set length, and the length of a single segment is 4,096 points. The maximum and minimum values ​​in each segment are calculated, and the maximum and minimum differences in each segment are used to linearly normalize all data in the segment through the difference. All values ​​fall between zero and one after linear transformation. Subsequently, the normalized signal is adaptively segmented based on a preset segmentation threshold, and the threshold value is set to 0.6. If the numerical difference between two adjacent points exceeds the threshold, the segmentation is performed at that point. Finally, the signal is automatically divided into several sub-segments to obtain To the segmented vibration signal, the average value and standard deviation of the segmented vibration signal are calculated segment by segment, and the abnormal point judgment threshold is set to three times the standard deviation. If the value of a point deviates from the average value in the current segment by more than this threshold, it will be judged as an abnormal point, and the specific location of the abnormal point is recorded. For all detected abnormal points, the median filter replacement method is used to replace the abnormal point with the middle value of the three points before and after the point, after the abnormal point is removed. After the replacement is completed, the cleaned characteristic signal is obtained, and the cleaned characteristic signal is transformed into a spectrum. The fast Fourier transform is used to convert the time domain signal into a frequency domain signal. The frequency domain range is zero to half of the sampling frequency. The frequency resolution is determined by dividing the sampling frequency by the number of sampling points in each segment. Extract The maximum amplitude in the spectrum corresponds to the frequency and its corresponding signal envelope. The spectrum signal envelope is obtained using the Hilbert transform method, and then the envelope signal is inversely transformed back to the time domain to obtain the envelope time domain signal. The mean, root mean square value, maximum value, skewness and kurtosis of the signal are extracted, and the opening and closing action feature recognition criterion is set. When a certain instantaneous value of the envelope signal is higher than the mean value of the signal plus twice the root mean square value, the time position of the point is recorded. If several consecutive points meet the condition, the time of the first point that meets the condition is regarded as the starting point of opening and closing, and the first point that does not meet the condition continuously is regarded as the end point of opening and closing. All opening and closing time points are converted to seconds according to the sampling frequency to obtain the time start and end sequence of the opening and closing action of the hoist.

[0023] Preferably, step S2 comprises the following steps: Step S21: performing time series clustering on the gate opening and closing timing, and identifying the gate opening and closing mode based on the clustered time series; Step S22: performing high-frequency mode statistics on the gate opening and closing mode, and determining a regular opening and closing cycle according to the high-frequency mode; Step S23: Performing time-space mapping evolution according to the conventional opening and closing cycle to obtain an opening and closing trajectory change curve; confirming the opening and closing rhythm node according to the opening and closing trajectory change curve; Step S24: performing multi-cycle trajectory alignment on the opening and closing trajectory change curve based on the opening and closing rhythm nodes to generate a gate healthy opening and closing trajectory.

[0024] In this embodiment, when performing time series clustering on the gate opening and closing timing data, first, the opening and closing start and end time point sequence obtained in the previous stage is formed into an opening and closing cycle sequence in chronological order, and the time length of a single cycle is set as the time difference between the opening and closing start and end points. A multidimensional time series matrix is ​​constructed based on all cycle time lengths, and the distance measurement method is set as the dynamic time warping (DTW) distance. After the DTW distances between all cycle time series are calculated, a distance matrix is ​​constructed. K-Means clustering is selected as the clustering method, and the number of clusters is set to four categories. According to the principle of minimizing the square sum of DTW distances within the class, the clustering process is iteratively executed, and the number of iterations is set to three hundred times. When the change in the DTW distance between the current and the next two cluster centers is less than one times ten to the negative fifth power, clustering is stopped. After clustering is completed, according to the morphological characteristics of the cluster center sequence, four gate opening and closing modes of normal opening and closing, slow opening and closing, fast opening and closing, and abnormal opening and closing are identified. Each mode uses the mean of the opening and closing start and end time of the cluster center and The average rhythm characteristic value indicates that when the high-frequency pattern statistics are performed on the identified gate opening and closing patterns, the number of patterns belonging to all opening and closing cycles is counted, and the statistical period window length is set to thirty days. Zero o'clock is used as the starting point every day. The patterns belonging to each opening and closing cycle within thirty days are recorded in chronological order. According to the frequency of pattern occurrence, in each statistical period, the pattern with the highest frequency is set as the high-frequency pattern in the period. If the number of days the high-frequency pattern appears exceeds twenty days, the high-frequency pattern is determined as the regular opening and closing cycle in the period, and the average opening and closing start and end time, the average opening and closing rhythm, and the opening and closing start and end point interval corresponding to the regular opening and closing cycle are recorded. The mean, all statistical values ​​retain three decimal places, and the time-space mapping evolution is performed according to the mean of the opening and closing start and end time and the rhythm mean of the regular opening and closing cycle. The length of the mapping time axis is set to be equal to the regular opening and closing cycle, and the value range of the spatial axis is zero to one. The opening and closing start and end time are evenly mapped to the time axis, and the opening and closing rhythm mean is mapped to the amplitude value on the spatial axis. The opening and closing trajectory change curve is constructed. The curve is smoothed by the spline interpolation method, and the interpolation node is set to two points per second. After the curve is completed, the inflection point position of the curve is identified according to the change trend of the opening and closing rhythm. If the difference in the amplitude change value within the five points before and after a certain inflection point is greater than 0.2, the inflection point position is marked as the opening and closing. Rhythm nodes, all rhythm nodes are recorded in the form of time points, and the rhythm node interval is measured in seconds. Based on the confirmed opening and closing rhythm nodes, the opening and closing trajectory change curve is aligned for multiple cycles. The alignment benchmark is set as the rhythm node position in the regular opening and closing cycle. All opening and closing trajectory change curves are translated along the time axis to make the corresponding rhythm nodes coincide. The translation step accuracy is set to 0.1 second. After the translation is completed, the aligned trajectory curve group calculates the mean of the spatial axis amplitude at each moment to obtain the healthy opening and closing trajectory of the multi-cycle trajectory. The healthy trajectory is recorded at a resolution of two points per second, and the amplitude value retains four decimal places. The healthy trajectory is saved in a dedicated trajectory sequence database.It will be used for random forest model training and fault prediction data benchmark comparison later.

[0025] Preferably, the normal gate opening and closing simulation according to the gate healthy opening and closing trajectory in step S3 includes: Perform Fourier transform decomposition on the gate opening and closing trajectory and extract the key frequency characteristic spectrum; Construct the opening and closing motion equations based on the key frequency characteristic spectrum; Simulate multiple groups of gate opening and closing trajectory samples through the opening and closing motion equations; Perform morphological screening on gate opening and closing trajectory samples, remove abnormal trajectories, and obtain normal trajectory samples; Trajectory fitting is performed based on the normal trajectory samples, and the normal opening and closing of the gate is simulated to obtain the simulated normal opening and closing data of the gate.

[0026] In this embodiment, the healthy opening and closing trajectory generated in the previous stage is sorted into a one-dimensional discrete signal sequence of the opening and closing amplitude varying with time in chronological order. The sampling frequency is set to two sampling points per second, and the total length of the opening and closing period is set to 600 seconds, obtaining 1200 time series points. The trajectory signal is input into the Fast Fourier Transform (FFT) function to obtain the complex amplitude and phase information in the corresponding frequency domain. The spectral data with a frequency range from 0 Hz to 1 Hz is intercepted, and the frequency points with an amplitude greater than 20% of the amplitude peak are selected as key frequency points. The corresponding frequency values, amplitudes, and phase values of these frequency points are recorded, retaining three decimal places, forming a key frequency feature spectrum. The number of key frequency points is limited to 10. If it is less than 10, the closest frequency points are supplemented in descending order of amplitude. When constructing the opening and closing motion equation based on the key frequency feature spectrum, the frequency values f, amplitudes A, and phases θ of the 10 key frequency points are used as parameters, and the simple harmonic vibration superposition model is adopted. Each frequency component is expressed in the form of A multiplied by the sine function. The vibration period T is taken as 1 divided by the frequency value f, the time variable of the vibration term ranges from 0 to 600 seconds, and the time interval is 0.5 seconds. All key frequency components are accumulated to form the opening and closing motion equation. This equation outputs the amplitude value at each time point, and the amplitude value range is limited to between 0 and 1. When the amplitude exceeds the boundary, the amplitude value is truncated to the boundary value. When simulating multiple groups of gate opening and closing trajectory samples through the opening and closing motion equation, the frequency value f remains unchanged, and the amplitudes A and phases θ are randomly perturbed. The amplitude perturbation range is limited to plus or minus 5% of the original amplitude, and the phase perturbation range is limited to plus or minus π / 10 of the original phase value. Based on the randomly perturbed parameters, 1000 groups of opening and closing trajectory samples are generated, with each trajectory length of 600 seconds and two sampling points per second. All trajectories are saved in the form of a one-dimensional array of the amplitude value varying with time, and the trajectory data accuracy is retained to four decimal places, stored in the trajectory sample database with the naming method of "track_number". When performing morphological screening on the gate opening and closing trajectory samples, the maximum amplitude, minimum amplitude, amplitude standard deviation, and amplitude change rate of each group of trajectories are calculated. The amplitude change rate is the sum of the absolute values of the amplitude differences between adjacent sampling points divided by the total trajectory duration. The screening threshold is set. The maximum amplitude is required to be between 0.95 and 1, the minimum amplitude is required to be between 0 and 0.05, the amplitude standard deviation is required to be between 0.1 and 0.5, and the amplitude change rate is required to be between 0.003 and 0.007, the trajectory samples that do not meet any of the conditions are directly eliminated, and the trajectory samples that meet all the conditions are retained. The number of samples after screening is recorded, and the final number of retained trajectories is not less than 800. If it is less than 800, the amplitude disturbance range is adjusted to plus or minus 3% and the trajectory samples are regenerated to make up for it. When fitting the trajectory based on the normal trajectory samples, the fifth-order B-spline curve fitting method is used, and the number of fitting nodes is set to 20. The node distribution is divided into equal intervals according to the trajectory time axis. Each node takes the mean amplitude of all samples at that moment as the fitting target point. The least squares method is used in the fitting process. The control point position of the fitting curve is solved by the method. After the fitting is completed, the root mean square error between the fitting curve and each normal trajectory sample is calculated. The error is required to be less than 0.02. If the error exceeds the limit, the number of nodes is increased to 25 and the fitting is repeated. Finally, a fitting curve with a fitting residual that meets the requirements is obtained. This curve is used as the benchmark trajectory for the normal opening and closing simulation of the gate. Based on the fitting trajectory, the simulated normal opening and closing data of the gate is generated at two sampling points per second within a 600-second opening and closing cycle. The amplitude value is the amplitude value at the corresponding time point on the fitting curve. All data are retained to four decimal places and written into the opening and closing normal trajectory database. .

[0027] Preferably, analyzing the gate movement wear data based on the simulated gate normal opening and closing data in step S3 includes: Determine the opening and closing speed change rate based on the simulated gate normal opening and closing data; Analyze gate wear changes caused by speed changes based on preset gate physical data and opening and closing speed change rates; The accumulated wear of the gate key points is calculated by superimposing the gate wear changes to obtain the gate key point wear data; Construct a wear distribution network based on the wear data of the gate's key points; Construct the wear gate framework according to the wear distribution network; Identify the kinematic friction wear in the worn gate frame and eliminate the local fluctuation data to obtain the gate kinematic wear data.

[0028] In this embodiment, when determining the opening and closing speed change rate based on the simulated gate normal opening and closing data, firstly, the simulated amplitude data of two sampling points per second in a 600-second period is used as the displacement input, and the displacement difference of two consecutive sampling points is divided by the time interval of 0.5 seconds using the finite difference method to obtain the instantaneous opening and closing speed, and then the difference of two consecutive instantaneous speed values ​​is calculated by the same method and divided by 0.5 seconds to obtain the opening and closing speed change rate. All speed change rate data retain four decimal places, the speed unit is meter per second (m / s), and the speed change rate unit is meter per second squared (m / s²). To ensure data integrity, the speed change rate of the first and last sampling points is padded with zero, and a speed change rate sequence with the same length as the original sampling points is generated and written into the speed change rate database. When analyzing the gate wear change caused by the speed change according to the preset gate physical data and the opening and closing speed change rate, the preset gate physical parameters include the gate mass value of 5000 kg, the friction coefficient value of 0.15, the total length of the gate guide rail and the gate contact surface is set to 5 meters, and the wear coefficient per unit area is 0.0001 millimeters per Newton meter (mm / Nm), based on Newton's second law F=ma, the instantaneous inertial force of each sampling point is calculated using the gate mass and speed change rate sequence, and then the instantaneous inertial force is multiplied by the friction coefficient to obtain the friction force, and the friction force is multiplied by the displacement increment and divided by the contact surface area to calculate the wear amount. The contact surface area is fixed at 5 square meters, and finally the instantaneous wear amount of each sampling point within a 600-second period is obtained in millimeters. All wear amounts retain six decimal places. The accumulated wear of the gate key points is calculated by superimposing the gate wear changes. When the gate key point wear data is obtained, the preset number of key points is 20, and the key point positions are evenly spaced along the 5-meter trajectory direction. The section where each key point is located contains There are 60 sampling points. The cumulative wear is calculated by taking the arithmetic sum of the instantaneous wear in the section. After the calculation is completed, the cumulative wear values ​​of the 20 key points in the 600-second opening and closing cycle are obtained in millimeters. All cumulative wear values ​​retain six decimal places. The cumulative wear data are sorted into a one-dimensional array. When constructing a wear distribution network based on the wear data of the key points of the gate, the cumulative wear values ​​of the 20 key points are used as node weights. The nodes are numbered 1 to 20, and the numbering sequence increases according to the trajectory direction. Directed edges are established between adjacent nodes. The edge weight takes the absolute value of the difference between the cumulative wear of the two nodes. The edge weight unit is millimeters. The network structure is stored as an adjacency matrix with a matrix dimension of 20 rows and 20 columns. The diagonal elements are zero and the non-diagonal elements are edge weights. When constructing the wear gate framework according to the wear distribution network, the wear gate framework is defined as a spatial grid model with key point numbers as nodes and wear distribution network edge weights as connection relationships. The node position coordinates are distributed along the x-axis direction. The x coordinate of each node is an equidistant position from the start point to the end point of the gate trajectory, in meters. The y coordinate is fixed to 0, and the z coordinate is the accumulated wear value of the node. All coordinate data retain four decimal places. The grid model is constructed using a three-dimensional coordinate modeling tool. The grid edge is determined by the spatial distance of adjacent nodes and the wear edge weight. The motion friction wear in the wear gate framework is identified, and the local fluctuation data is eliminated. When obtaining the gate motion wear data, the wear is firstly calibrated according to the gate motion trajectory direction. The gate frame grid model is projected in the direction, and the wear curve of the z coordinate changing with the x coordinate is extracted. The sliding window mean method is used, and the window length is set to 5 key points. The wear curve is smoothed, and the mean wear value in each window is calculated. The original wear value at the center of the window is replaced to generate a smooth wear curve. The wear gradient change rate of the smooth curve is then calculated by difference, and the local fluctuation threshold is set to 0.005 mm. Any section where the absolute value of the wear gradient change rate is lower than the threshold is judged as a local fluctuation, and the wear value of the section is uniformly corrected to the mean of the adjacent sections before and after. Finally, the wear curve after eliminating the local fluctuation is obtained. All corrected wear values ​​retain four decimal places and are organized into a gate motion wear data sequence. .

[0029] It is particularly important to construct a wear gate framework based on the wear distribution network including: Perform structural node aggregation processing on the wear distribution network data to obtain the gate wear node array; Perform regionalized density reconstruction based on the gate wear node array to generate wear density partition data; Perform gate density projection based on the wear density partition data to obtain gate wear projection data; Perform boundary contour fitting on gate wear projection data and extract wear contour curve; Perform three-dimensional geometric mapping on the wear contour curve to generate a three-dimensional wear surface; The three-dimensional wear surface is calibrated based on the gate wear node array to construct the wear gate framework.

[0030] In this embodiment, when the wear distribution network data is processed by structural node aggregation, the wear distribution sensor array is first called to evenly arrange the node points on the surface of the gate structure with 5mm×5mm, and the wear depth value at each point is collected in real time. The unit of wear depth is mm. All the measurement point data are summarized into a two-dimensional coordinate matrix according to the coordinate numbering method using a matrix data structure. The row number and column number of the matrix correspond to the spatial coordinate index of the horizontal and vertical directions of the gate respectively. The element value in the data matrix represents the wear depth value of the corresponding coordinate point. The size of the data matrix is ​​determined by the actual point density. If the point range is 1000mm×2000mm, the node array dimension is set to 200×400. When regional density reconstruction is performed according to the gate wear node array, a method based on kernel density estimation is adopted, and the kernel function type is set to Gaussian kernel. Kernel, the bandwidth parameter is 10mm, according to the wear depth value of each node in the two-dimensional node coordinate matrix, the wear density at each point is estimated according to the spatial density formula, and the wear density unit is mm⁻². After the estimation is completed, the calculation area is divided into equal grids with 50mm×50mm as the grid unit. The average density value of all nodes inside each grid unit is calculated to generate the wear density partition data matrix. The output data format is TIFF (Tagged Image File The matrix dimension is determined by the area range and grid scale. If the area range is 1000mm×2000mm and the number of grid cells is 20×40, the matrix dimension is 20×40. When performing gate density projection based on wear density partition data, the two-dimensional wear density partition data is first projected to a single dimension along the gate height direction. The linear superposition method is used to accumulate the corresponding density values ​​along each column of grid cells to obtain the horizontal projection value. The projection unit is mm⁻¹, and the projection step is set to 50mm. A total of 20 projection values ​​are generated for a gate width of 1000mm. The projection values ​​are arranged in horizontal coordinate order to form a gate wear projection data vector. When fitting the boundary contour of the gate wear projection data, a spline curve is used. The wear contour curve is fitted by the method of fitting the node interpolation curve, and the node interpolation spacing is set to 50mm. According to the projection value and the horizontal coordinate point in the gate wear projection data vector, a continuous smooth curve is fitted in sequence to obtain the wear contour curve. The wear contour curve represents the change trend of the wear boundary of the horizontal section of the gate. When the wear contour curve is subjected to three-dimensional geometric mapping, the surface reconstruction method based on NURBS (Non-Uniform Rational B-Splines) is used. The number of vertical segments of the gate is set to 40, and the segment spacing is 50mm. The horizontal contour curve is copied and stacked at a fixed vertical interval and arranged in sequence along the vertical coordinate. The continuous three-dimensional wear surface is generated by the NURBS surface fitting tool, and the control point spacing is set to 20mm.The surface accuracy error is limited to within 0.5mm. When calibrating the framework section of the three-dimensional wear surface based on the gate wear node array, first set the segment number at equal intervals in the horizontal and vertical directions according to the coordinate points in the wear node array. The number of horizontal segments is 20 and the number of vertical segments is 40. Set the spatial segment reference point corresponding to the same coordinate position on the wear surface. According to the coordinates of the reference point and the corresponding wear depth value, calibrate the spatial position of each segment on the surface to complete the spatial framework section division of the three-dimensional wear surface. After the calibration is completed, the segment calibration result data table is output. The file format is CSV, and the fields include segment number, reference coordinates, wear depth, and corresponding surface control point coordinates. ,

[0031] Preferably, the gate state estimation based on the gate movement wear data according to the preset gate usage time in step S4 includes: Extract the gradient features of gate movement wear data and generate surface morphology gradient features; Wear attenuation analysis is performed based on surface morphology gradient characteristics, and a wear attenuation curve is constructed; The gate wear simulation is performed according to the preset gate usage time and the wear attenuation curve to obtain the simulated gate wear data; The gate wear state is estimated based on the simulated gate wear data and the preset initial gate state to generate the ideal gate state.

[0032] In this embodiment, when extracting the gradient features of the gate movement wear data and generating the surface morphology gradient features, the collected gate movement wear data is first preprocessed to remove the noise and outliers in the data, and the data is smoothed using a sliding window. The window size is set to 10 data points, and the average value of the data in each sliding window is calculated to obtain a smooth wear curve. Next, the first-order derivative (i.e., gradient) of the wear curve in the horizontal direction is calculated. The gradient reflects the wear change rate at different positions. The differential method is used to calculate and obtain the wear gradient value of each position point in millimeters per meter (mm / m). Through these gradient values, the degree of wear and change trend of the gate surface can be described. After obtaining the gradient data of each position, the wear gradient features are formed. When performing wear attenuation analysis based on the surface morphology gradient features, the obtained wear gradient data is regressed using the fitting method. The cubic polynomial regression method is selected to fit the attenuation curve of the wear gradient changing with time. Through fitting, the wear attenuation coefficient can be obtained. This coefficient represents the law of wear changing with time, that is, how the wear attenuation rate changes with the increase of time. In order to conduct a detailed analysis of the attenuation, the changes in all gradient data are plotted into an attenuation curve, with the x-axis being the gate usage time (in hours) and the y-axis being the gradient value of wear. The attenuation curve reveals the attenuation characteristics of gate wear under different usage times. When simulating gate wear according to the preset gate usage time combined with the wear attenuation curve, the gate usage time is first set to 3000 hours, and the usage time is discretized in hours to simulate the wear change hour by hour. According to the wear attenuation curve obtained previously, the relationship between the corresponding usage time and the wear gradient is found, and the linear interpolation method is used to gradually calculate the wear value at each moment according to the different usage times. Specifically, assuming that the gate usage time is t hours, the wear value at that moment is calculated according to the attenuation curve to obtain a new wear value, and then the wear value at the previous moment and the wear value at the current moment are added to obtain the cumulative wear value at the current moment. This operation is repeated until the preset 3000 hours are calculated. Through this hourly simulation method, the wear change of the gate during the entire use period can be obtained. When estimating the gate wear state based on the simulated gate wear data and the preset initial gate state, the initial state of the gate is first set to a completely new state, and the initial wear value is 0 mm. During the simulation process, the calculated wear data sequence is used to calculate the current gate state according to the simulated wear value at each moment. The gate state is determined by the accumulated wear value and the wear gradient, and a threshold is set to distinguish the working state of the gate. For example, when the accumulated wear value exceeds a certain threshold (such as 5 mm), the gate state is considered to be "excessively worn".By calculating the wear state of the gate hour by hour, we can obtain the state change curve of the gate during use, and finally form a wear state curve, which reflects how the wear state of the gate changes with the increase of use time.

[0033] Preferably, determining the gate status gap based on the gate current status data and the ideal gate status in step S4 includes: Phase alignment is performed on the gate status data and the ideal gate status to obtain gate status pairing data; Apply geometric deformation tensor transformation to gate state pairing data and compare gate topology differences; Perform gradient segmentation on gate topology differences to generate gate difference gradients; Determine the gate state development differences based on the gate difference gradient; The gate status gap is determined by integrating the gate topology differences and gate status development differences.

[0034] In this embodiment, when the gate status data and the ideal gate state are phase-aligned, the actually measured gate hoist motion data are first time-aligned with the ideal gate state data. The time alignment method used is dynamic time warping (DTW). This method ensures the optimal time alignment of the gate status data and the ideal gate state data by calculating the minimum matching error between the two sets of time series. During the processing, the two data sets are first pre-processed to remove outliers and noise. Then, based on the displacement or velocity data of each time step, the DTW algorithm is used to pair the time series. Finally, a set of gate status data that is completely aligned with the ideal state is obtained. This set of data will serve as the basis for subsequent analysis. The DTW parameters used are set to a window size of 50 and a distance metric of Euclidean distance. When applying geometric deformation tensor transformation to the gate state paired data, first, the paired data is transformed using a three-dimensional spatial coordinate system, and the gate current state data and ideal state data are represented as three-dimensional point cloud data respectively. Then, the rigid transformation model is applied to transform the current state data through rotation, translation, and scaling to match the ideal state. When applying the tensor transformation, the tensor used is a tensor based on rigid transformation, that is, a transformation that keeps the object's shape unchanged. The goal is to reduce the topological difference between the two. The transformation method used is principal component analysis (PCA). By calculating the covariance matrix between the data points, the most important principal component direction is extracted, and a geometric transformation is applied to the paired data accordingly. Finally, the transformed data is obtained. These data reflect the geometric difference between the gate's current state and ideal state. The topological difference of the gate state paired data is calculated, and the preliminary topological structure difference is obtained by comparing its coordinate difference. When the gate topology difference is gradient segmented, the topology difference data is divided into grids, and the eight-directional grid cutting method is used to divide the topological structure of the gate model into multiple areas. Each area corresponds to a specific part of the gate. Then the gradient value of each area is calculated. The gradient calculation is based on the spatial coordinate difference in the area. The height difference of each grid unit is calculated, and the gradient data is generated based on these height difference values. In the specific operation process, for each area, the difference between adjacent units is calculated by the difference method to obtain the gradient value, and the gradient data of each area is generated. The gradient value of each grid unit represents the deformation or wear degree of the part. When the gate state development difference is determined based on the gate difference gradient, the gradient data is weighted and integrated with the preset weights. The weight distribution is based on the importance of each part of the gate and the degree of influence in the opening and closing process. The gradient value of each grid unit is multiplied by the corresponding weight to obtain the comprehensive difference value of the area. The difference values ​​of all areas are accumulated to obtain the overall state difference of the gate.The weight factors used are set according to the design structure of the gate and the actual workload during use. A larger weight is given to the area with a greater mechanical load on the gate, so as to more accurately calculate the impact of the wear and deformation of this part on the overall state, and finally obtain the overall gate state development difference. When integrating the gate topology difference and the gate state development difference to determine the gate state gap, the weighted summation method is used to integrate the topology difference and the state development difference. The topology difference and state development difference of each area are first weighted, and the weight is determined by the importance of each part in the gate design. All difference values ​​are weighted and accumulated to obtain the overall state gap value of the gate. This gap value represents the difference between the existing gate state and the ideal state. Finally, this gap value is used as one of the input features and combined with other working parameters. It is input into the Random Forest (RF) model for further fault prediction analysis. The weight factor setting in the whole process refers to the working environment and historical fault data of the gate.

[0035] Of particular importance is the determination of gate state development differences based on gate difference gradients including: Extract multi-segment gradient trajectories from gated difference gradients; Perform directional deviation distribution analysis on multi-segment gradient trajectories to generate gradient deviation distribution data; Perform differential evolution block cutting based on gradient offset distribution data to obtain gradient evolution blocks; Merge the trend axes of the gradient evolution blocks to obtain the gate evolution axis; Perform gradient vector fitting on the gate evolution principal axis to generate the gate state gradient vector; The evolution trend is superimposed based on the gate state gradient vector to obtain the gate state development difference.

[0036] In this embodiment, when extracting the multi-segment gradient trajectory in the gate difference gradient, firstly, based on the gate wear node array data, the gate is divided into 40 equidistant segments in the vertical direction, each segment is 50 mm high, and the horizontal direction is divided into 20 equidistant segments, each segment is 50 mm wide. Relying on the two-dimensional wear depth matrix, the wear depth difference between adjacent nodes in the horizontal and vertical directions is calculated, and the absolute value of the difference is taken as the local gradient value. The unit of the local gradient value is mm. The average gradient value is calculated for all nodes in each segment, and the average gradient trajectory value of the corresponding segment is obtained. The trajectory value is composed of a multi-segment gradient trajectory in the order of the spatial segment number. The output matrix format is CSV. The row number represents the vertical segment number, the column number represents the horizontal segment number, and the elements in the matrix represent the average gradient value of the corresponding segment. The value range is limited to between 0 mm and 5 mm. All gradient values ​​exceeding the upper limit are uniformly corrected to 5 mm. When performing directional offset distribution analysis on multi-segment gradient trajectories, the aforementioned gradient trajectory matrix is ​​regarded as vector field data, and the direction angles of the gradient vectors between adjacent segments are calculated respectively. The direction angle calculation is based on the inverse tangent function, and the horizontal gradient value and the vertical gradient value are used as independent variables. The angle value is in degrees, and the value range is limited to 0 degrees to 360 degrees. The gradients in all segments are calculated. Direction angle statistics summary, set the direction angle interval to 10°, divide the angle value into 36 intervals, count the proportion of the number of segments in each interval, generate gradient offset distribution data, the data structure is a two-dimensional table, the fields include direction interval, number of segments, and number proportion, the output data table format is CSV, limit the sum of the number of segments to be equal to the total number of segments, and the sum of the number proportions to be equal to 100%, when performing differential evolution block cutting based on gradient offset distribution data, set the classification thresholds of the front evolution zone, neutral evolution zone, and lagging evolution zone according to the distribution characteristics of the number proportion of segments in the direction interval, and the front evolution zone is defined as the continuous direction with a direction interval proportion higher than 5%. The neutral evolution zone is defined as the zone where the directional interval accounts for 2% to 5%, and the lagging evolution zone is defined as the zone where the directional interval accounts for less than 2%. All the zones in the matrix are classified and marked according to the directional intervals to which they belong, and the gradient evolution block partition matrix is ​​generated. The matrix format is consistent with the original gradient trajectory matrix. The matrix element value types include F (frontier), N (neutral), and L (lagging). The output evolution block matrix format is TXT. When merging the trend axis of the gradient evolution block, the continuous segments in the frontier evolution block are extracted as the frontier block subset. According to the block space coordinate order, a straight line is fitted to each subset. The least squares method is used for fitting, and the upper limit of the fitting residual is set to 0.2mm, the subsets exceeding this threshold are split into smaller subsets for fitting respectively, and all the parameters of the principal axis equation of the fitting line are recorded, including the slope, intercept, and coordinate range of the fitting block, and the principal axes with the same or similar slope directions are merged. The slope difference threshold is set to 5°, and the principal axes with slope differences less than 5° are merged into a gate evolution principal axis. The principal axis number, the starting and ending coordinates of the principal axis, and the fitting slope are recorded. When the gate evolution principal axis is fitted with a gradient vector, the local gradient change corresponding to each segment on the principal axis is calculated according to the starting and ending coordinates of the principal axis. The local gradient change takes the gradient difference of adjacent segments, and the change is converted into a vector form along the principal axis direction. The vector modulus is the absolute value of the change, and the direction is from the direction of increasing gradient to the direction of decreasing gradient. The unit is mm, and the vector sampling spacing is set The length of each main axis is 10 mm. The sampling points are evenly distributed on each main axis, and the corresponding vectors are calculated. After completion, the gate state gradient vector set is generated. When the evolution trend is superimposed based on the gate state gradient vector, all the main axis gradient vectors are superimposed according to the spatial position. The superposition method is the vector sum operation of the vectors with the same coordinate position. The gradient vectors of each main axis at this position are accumulated. The unit of the vector superposition result is mm. After the superposition is completed, the modulus values ​​of all superimposed vectors are normalized. The normalization range is set to 0 to 1. The normalization formula is the original modulus value minus the minimum modulus value and then divided by the difference between the maximum modulus value and the minimum modulus value. After normalization, the gate state development difference data matrix is ​​generated. The matrix dimension is consistent with the original wear node array, and the matrix elements represent the normalized state development difference values ​​of each point. .

[0037] Preferably, step S5 comprises the following steps: Step S51: extracting the state difference feature of the gate state difference; backtracking the use process based on the state difference feature, and inferring the backtracking fault data according to the backtracking use process; mapping the backtracking fault data to the hoist fault data; Step S52: acquiring the gear distribution data of the gate hoist; performing gear stress conversion processing on the gear distribution data of the gate hoist according to the gate hoist fault data to generate the gear force distribution; Step S53: performing force simulation based on the force distribution of the gear, wherein the simulated load range is 50N~3000N, and inferring the gear surface deformation based on the simulated gear force data; Step S54: Gear material fatigue analysis is performed through gear surface deformation, and the hoist transmission gear failure is predicted based on gear fatigue data and retrospective fault data, wherein the fatigue life data range is set to 1×10³ times~1×10 7 Secondary cycle; Step S55: Perform a slip-meshing angle compensation simulation on the gate hoist transmission gear fault according to the preset standard transmission gear data, and record the compensation adjustment data, wherein the modulus range of the standard transmission gear data is 2-8 mm, the number of teeth range is 20-100 teeth, and the slip-meshing compensation angle range is set to -5°~+5°; Step S56: Identify abnormal parameters of the gate hoist according to the compensation adjustment data.

[0038] In this embodiment, when extracting the state difference characteristics of the gate state difference, the historical hoist opening and closing stroke data, the real-time stroke sensor data and the opening and closing speed data are first normalized, and the stroke displacement difference of adjacent time points and the standard displacement curve are differentially calculated after the dimensions are unified. The calculation formula is the difference between the stroke values ​​at two adjacent moments minus the corresponding position difference of the standard stroke curve. The difference sequence obtained is the initial value of the state difference. The difference sequence is divided into an upward segment, a downward segment and a stationary segment according to the opening and closing stroke. Five statistical features, including the mean value, maximum value, minimum value, standard deviation and peak value number of the difference values ​​of each segment, are extracted. Then, these features are used to form a state difference feature vector. The vector distance discrimination method is based on the Euclidean distance (Euclidean distance) to obtain the state difference feature. Distance) method is used to calculate the difference between the current state and the historical healthy state. If the difference exceeds the set threshold of 5mm, it is determined to be an abnormal state. Then, the operation records of the gate hoist, the environmental water level change curve and the operation log are traced back according to the abnormal section time. All data time axes are aligned through the time synchronization method. The abnormal values ​​of the gate resistance, abnormal displacement values ​​and abnormal speed values ​​are extracted based on the 30-minute backtracking window to construct a backtracking fault data set. The fault characteristics are coded with the internal fault type of the gate hoist using the mapping rule table. The coding rules are such as resistance over-limit is F1, displacement lag is F2, and speed fluctuation is F3. The fault code is then mapped to the fault feature coding of the gate hoist gear transmission, bearing, and limiter to form a gate hoist fault data set. When obtaining the distribution data of the gate hoist gear, the FARO Focus 3D laser scanner model is used. S350 performs high-precision modeling on the gear plate and transmission gear shaft, and the scanning resolution is set to 0.2mm. After completing the point cloud data acquisition, the point cloud fitting tool is used to fit the gear addendum circle, root circle, pitch circle and tooth surface profile into the standard gear curve. According to the fitting results, geometric parameters such as the number of teeth, module, tooth height, addendum height, root height, tooth thickness, and tooth spacing are extracted. The module value range is set to 2mm to 8mm, and the number of teeth range is 20 to 100 teeth. Based on the above extracted geometric parameters and the hoist fault data, the gear stress conversion processing is performed. The finite element static analysis module is used to load the load conditions corresponding to each fault type to the gear meshing surface and addendum circle. The gear model material is set to 45# steel, with an elastic modulus of 210GPa and a Poisson's ratio of 0.3. Set different meshing loads according to fault data. Set the load to 1500N for F1 fault type, 2000N for F2 fault type, and 2500N for F3 fault type. Calculate the contact stress of each tooth surface node, generate gear force distribution data, output the maximum stress value and stress distribution cloud map at the tooth top, tooth root, and meshing point of each gear, perform force simulation based on the gear force distribution, set the simulation load range to 50N to 3000N, and set the load level in increments of 50N per gear. Use ANSYS The gear-gear contact model was established on the Mechanical finite element simulation platform. The gear speed was set to 30r / min and the simulation time was set to 60 seconds. The force distribution of the gear was calculated under different load levels. The maximum stress, average stress and stress concentration at the tooth top and tooth root corresponding to each gear load were recorded. The maximum deformation at the gear tooth top circle and tooth root circle was extracted based on the simulation results. The maximum deformation value was derived using the tooth surface displacement extraction tool. The unit was mm. The gear tooth top deformation value under 50N load was 0.002mm, the tooth root deformation value was 0.001mm, the gear tooth top deformation value under 3000N load was 0.420mm, and the tooth root deformation value was 0.320mm. The deformation curves under different loads were sorted out, the deformation trend was fitted, and the gear surface deformation amount was inferred to increase linearly with the load and the nonlinear mutation interval. The gear material fatigue analysis was performed based on the gear surface deformation data. The SN curve (stress-life curve) of 45 steel was selected, and the fatigue life data range was set to 1×10³ times to 1×10. 7 The maximum stress values ​​at the tooth top, tooth root and meshing point are substituted into the SN curve to find the corresponding fatigue life value. If the stress value exceeds the yield limit, the fatigue life is set to 1×10³ times. If the stress value is in the elastic range, the fatigue life is calculated according to the SN curve interpolation method, and the gear fatigue life data is extracted. Combined with the retrospective fault data, the fatigue life range corresponding to the same fault code is matched, and the fatigue life range of the F1 fault is set to 5×10 4 times up to 1×10 6 times, the F2 failure fatigue life interval is 1×10 4 Up to 5×10 5 times, and the F3 failure fatigue life range is 1×10³ times to 1×10 4The fault type and fatigue degree of the gate hoist transmission gear are predicted based on the corresponding relationship, and the fault prediction result is output. The slip-meshing angle compensation simulation of the gate hoist transmission gear fault is performed according to the preset standard transmission gear data. The module range of the standard transmission gear data is set to 2mm to 8mm, the number of teeth range is 20 to 100 teeth, the meshing angle is 20°, and the slip-meshing compensation angle range is set to -5° to +5°. The finite element contact analysis module is used to establish a compensated gear meshing model, and the predicted fault gear stress, deformation, and fatigue life data are input into the model. The meshing angle is adjusted every 0.5°, and the gear force distribution, tooth top deformation, and meshing point contact stress after each adjustment are recorded. The contact stress is lower than the material Yield limit, and extend the fatigue life of the meshing point to a compensation angle within the target life range, record the compensation adjustment data, output the final compensation meshing angle and the corresponding adjusted gear force, deformation, and fatigue life parameters, identify the abnormal parameters of the gate hoist according to the compensation adjustment data, extract the force distribution, deformation value, and fatigue life parameters in the compensated gear model, compare them with the original predicted data item by item, set the abnormal judgment threshold, and mark it as an abnormal parameter when the force increase exceeds 15%, the deformation increase exceeds 10%, and the fatigue life decreases by more than 30%. According to the abnormal parameters, the number of abnormal points, abnormal type, and abnormal degree are counted to form an abnormal parameter set of the gate hoist, and the transmission gear abnormality, bearing abnormality, and limiter abnormality are recorded in categories.

[0039] Preferably, step S6 comprises the following steps: Step S61: Perform interval label mapping processing on the gate hoist abnormal parameters to obtain the gate hoist abnormal classification data, where the abnormal classification interval is set as slight abnormality (0.5mm), moderate abnormality (5.10mm), and severe abnormality (10-20mm); Step S62: Perform feature vector encoding processing on the gate hoist abnormality classification data to obtain the gate hoist feature code, wherein the number of abnormality types is set to 3, and the encoding vector dimension is fixed to [1,0,0], [0,1,0], [0,0,1]; Step S63: Based on the random forest model, the hoist feature code is input adapted and converted to obtain random forest input data, and the input data standardization range is limited to the interval of 0 to 1; Step S64: Perform model prediction based on random forest input data to obtain predicted gate hoist fault data, wherein the predicted probability output range is set between 0 and 1, and a single fault event predicted probability greater than 0.6 is determined as a fault warning.

[0040] In this embodiment, when performing interval label mapping processing on the gate hoist abnormal parameters, first call the compensation adjustment data generated in the previous stage. The compensation adjustment data includes the offset value after gear slip-meshing angle compensation, which is in millimeters. All compensation adjustment data are sorted according to size, and a threshold is set according to a preset abnormal classification interval. Data with a compensation offset value less than 5 mm is mapped as a slight abnormality, data with an offset value in the range of 5 mm to 10 mm is mapped as a moderate abnormality, and data with an offset value in the range of 10 mm to 20 mm is mapped as a severe abnormality. The classification labels are expressed in the form of 0, 1, and 2, corresponding to slight abnormalities, moderate abnormalities, and severe abnormalities, respectively. After the mapping is completed, a gate hoist abnormality classification data table is formed. The data table structure includes three columns: number, original compensation adjustment data, and classification label. Each row of data maintains a one-to-one correspondence with the corresponding gate hoist equipment number. When performing feature vector encoding processing on the gate hoist abnormality classification data, the abnormality type is one-hot encoded (One-Hot) according to the classification label. Encoding), when the classification label is 0, the corresponding encoding is [1,0,0], when the classification label is 1, the corresponding encoding is [0,1,0], when the classification label is 2, the corresponding encoding is [0,0,1]. All classification labels are converted into corresponding encoding vectors one by one to generate a gate hoist feature encoding data set. The encoding vector dimension is fixed to 3, and the position corresponds to the number of abnormal types one by one. There are 3 categories in total. Each feature code corresponds to the original gate hoist number, forming a three-column data table of number, classification label, and feature code. When the gate hoist feature code is input-adapted and converted based on the random forest model, the gate hoist feature code data generated in the previous stage is called, and the feature code is converted into a floating point format according to the model input standard. The dimension is 3, and the value range of each encoding vector is limited to 0 to 1. The One-Hot encoding feature is maintained, and the Min-Max normalization method is used to normalize other compensation adjustment data. The original compensation adjustment data The normalization formula y=(x-xmin) / (xmax-xmin) is used for conversion, and xmin is set to 0 and xmax to 20 to ensure that the converted value of the compensation adjustment data is between 0 and 1. The normalized compensation adjustment data is concatenated with the corresponding feature coding data to generate random forest input data with a dimension of 4, and the arrangement order is [coding 1, coding 2, coding 3, normalized compensation value]. When the model is predicted based on the random forest input data, the trained random forest model is used, the number of model trees is set to 100, the maximum tree depth is set to 10, the minimum number of sample splits is set to 2, and the minimum number of leaf node samples is set to 1. The input data is batched into the model, and the model predicts faults for each group of input data. The prediction output is the predicted probability value, and the output range is set between 0 and 1. Each predicted value represents the probability of failure of the gate hoist. The judgment threshold is set to 0.6. If the predicted probability of a single fault event is greater than 0.6, the device is judged to be in a fault warning state, all prediction results are kept in correspondence with the gate hoist number, and finally a gate hoist fault prediction data table is generated. The data table contains three columns: number, prediction probability, and fault warning state, completing the fault prediction process.

[0041] The present invention also provides a gate hoist fault prediction system based on data analysis, which is used to execute the gate hoist fault prediction method based on data analysis as described above. The gate hoist fault prediction system based on data analysis includes: The signal cleaning module is used to collect the vibration signal of the gate hoist; perform segmented regularization on the vibration signal of the gate hoist and correct the abnormal value to obtain the cleaning characteristic signal; analyze the gate opening and closing timing according to the clear characteristic signal; The cycle determination module is used to determine the normal opening and closing cycle based on the gate opening and closing sequence; the gate healthy opening and closing trajectory is evolved according to the normal opening and closing cycle; The opening and closing simulation module is used to simulate the normal opening and closing of the gate according to the gate's healthy opening and closing trajectory to obtain the simulated normal opening and closing data of the gate; the gate movement wear data is analyzed based on the simulated normal opening and closing data of the gate; The status assessment module is used to obtain the gate status data; estimate the gate status based on the gate movement wear data according to the preset gate usage time, and generate the ideal gate status; determine the gate status gap based on the gate status data and the ideal gate status; The fault backtracking module is used to backtrack faults through the gate status gap, and analyze the gate hoist transmission gear fault data based on the backtracked fault data; identify the gate hoist abnormal parameters based on the gate hoist transmission gear fault data; The fault prediction module is used to predict the gate hoist fault based on the abnormal parameters of the gate hoist based on the random forest model to generate predicted gate hoist fault data.

[0042] The present invention can realize accurate collection and processing of gate hoist vibration signals through the application of signal cleaning module, ensuring that the obtained signal data has high reliability. The segmented regularization processing and outlier correction significantly improve the signal quality, providing an accurate basis for subsequent analysis. The extraction of cleaning characteristic signals can provide necessary data support for effective analysis of gate opening and closing timing. The introduction of cycle determination module can scientifically judge the conventional opening and closing cycle, effectively evolve healthy opening and closing trajectory, and provide standard basis for equipment health management. The simulation data generated by the opening and closing simulation module effectively reflects the characteristics of the equipment under normal operating conditions, which is helpful for in-depth analysis of gate movement wear data, and then identify and solve potential wear problems. The state evaluation module obtains the gate status data to provide a comprehensive perspective for the equipment status. When performing state estimation, the preset usage time is combined with the wear data to generate an ideal gate state, so that the gap between the ideal state and the current state becomes quantifiable. The fault backtracking module can trace back historical faults and extract key data by analyzing the state gap, promote in-depth analysis of the gate hoist transmission gear fault, and identify the gate hoist. The abnormal parameters of the equipment can provide strong support for subsequent fault prediction. The fault prediction module is based on the application of data analysis, which enables highly accurate fault prediction of abnormal parameters, thereby generating reliable fault prediction data, providing a scientific basis for preventive maintenance of equipment, significantly reducing the risk of sudden failures, and improving the overall reliability and safety of the equipment. It integrates various links of signal monitoring, data analysis and fault prediction, realizes active monitoring and early warning of equipment health status, promotes the intelligent level of equipment management, reduces the interference of human factors on equipment maintenance, enhances the predictability of equipment failures, and improves the efficiency of maintenance work. Through the data-driven management model, it lays the foundation for the safe and stable operation of water conservancy projects, which not only improves the operating efficiency of equipment and the scientific nature of life cycle management, but also promotes the widespread application of intelligent technology in the water conservancy industry, forming an effective and sustainable intelligent fault early warning mechanism, and provides valuable experience and foundation for future technological innovation and application promotion of equipment management, promotes the process of modernization of equipment management in the water conservancy field, and finally realizes the dynamic adaptation and optimization of equipment health management in complex environments.

[0043] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0044] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A gate hoist fault prediction method based on data analysis, characterized in that: The following steps are involved: Step S1: collecting the vibration signal of the gate hoist; performing piecewise regularization processing on the vibration signal of the gate hoist, and correcting the abnormal value to obtain a cleaning characteristic signal; Analyze the gate opening and closing timing based on clear characteristic signals; Step S2: judging the normal opening and closing cycle based on the gate opening and closing sequence; evolving the gate healthy opening and closing trajectory according to the normal opening and closing cycle; Step S3: Perform gate normal opening and closing simulation according to the gate healthy opening and closing trajectory to obtain simulated gate normal opening and closing data; analyze gate movement wear data based on the simulated gate normal opening and closing data; Step S4: Obtain gate status data; The gate state is estimated based on the gate movement wear data according to the preset gate usage time to generate the ideal gate state; Determine the gate status gap based on the gate current status data and the ideal gate status; Step S5: Fault backtracking is performed through the gate state gap, and the hoist transmission gear fault data is analyzed based on the backtracked fault data; abnormal parameters of the gate hoist are identified according to the hoist transmission gear fault data; Step S6: Predict gate hoist failure based on the gate hoist abnormal parameters based on the random forest model to generate predicted gate hoist failure data.

2. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting vibration signals of the gate hoist in multiple frequency bands; performing wavelet denoising on the vibration signals of the gate hoist to obtain noise-reduced vibration signals; Step S12: performing segmented regularization processing on the noise reduction vibration signal, and performing adaptive segmentation based on a preset segmentation threshold to obtain a segmented vibration signal; Step S13: performing discrete point detection on the segmented vibration signal and identifying abnormal points; performing median filtering replacement on the abnormal points to obtain a cleaning characteristic signal; Step S14: Perform spectrum conversion on the cleaning characteristic signal and extract the signal characteristic envelope; extract the time domain characteristics of the signal characteristic envelope, and identify the opening and closing time nodes based on the time domain characteristics to generate the gate opening and closing timing sequence.

3. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: performing time series clustering on the gate opening and closing timing, and identifying the gate opening and closing mode based on the clustered time series; Step S22: performing high-frequency mode statistics on the gate opening and closing mode, and determining a regular opening and closing cycle according to the high-frequency mode; Step S23: Performing time-space mapping evolution according to the conventional opening and closing cycle to obtain an opening and closing trajectory change curve; confirming the opening and closing rhythm node according to the opening and closing trajectory change curve; Step S24: performing multi-cycle trajectory alignment on the opening and closing trajectory change curve based on the opening and closing rhythm nodes to generate a gate healthy opening and closing trajectory.

4. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: The normal gate opening and closing simulation according to the gate healthy opening and closing trajectory in step S3 includes: Perform Fourier transform decomposition on the gate opening and closing trajectory and extract the key frequency characteristic spectrum; Construct the opening and closing motion equations based on the key frequency characteristic spectrum; Simulate multiple groups of gate opening and closing trajectory samples through the opening and closing motion equations; Perform morphological screening on gate opening and closing trajectory samples, remove abnormal trajectories, and obtain normal trajectory samples; Trajectory fitting is performed based on the normal trajectory samples, and the normal opening and closing of the gate is simulated to obtain the simulated normal opening and closing data of the gate.

5. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: Analyzing the gate movement wear data based on the simulated gate normal opening and closing data in step S3 includes: Determine the opening and closing speed change rate based on the simulated gate normal opening and closing data; Analyze gate wear changes caused by speed changes based on preset gate physical data and opening and closing speed change rates; The accumulated wear of the gate key points is calculated by superimposing the gate wear changes to obtain the gate key point wear data; Construct a wear distribution network based on the wear data of the gate's key points; Construct the wear gate framework according to the wear distribution network; Identify the kinematic friction wear in the worn gate frame and eliminate the local fluctuation data to obtain the gate kinematic wear data.

6. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: The gate state estimation based on the gate movement wear data according to the preset gate usage time in step S4 includes: Extract the gradient features of gate movement wear data and generate surface morphology gradient features; Wear attenuation analysis is performed based on surface morphology gradient characteristics, and a wear attenuation curve is constructed; The gate wear simulation is performed according to the preset gate usage time and the wear attenuation curve to obtain the simulated gate wear data; The gate wear state is estimated based on the simulated gate wear data and the preset initial gate state to generate the ideal gate state.

7. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: Determining the gate status gap based on the gate current status data and the ideal gate status in step S4 includes: Phase alignment is performed on the gate status data and the ideal gate status to obtain gate status pairing data; Apply geometric deformation tensor transformation to gate state pairing data and compare gate topology differences; Perform gradient segmentation on gate topology differences to generate gate difference gradients; Determine the gate state development differences based on the gate difference gradient; The gate status gap is determined by integrating the gate topology differences and gate status development differences.

8. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: extracting the state difference feature of the gate state difference; backtracking the use process based on the state difference feature, and inferring the backtracking fault data according to the backtracking use process; mapping the backtracking fault data to the hoist fault data; Step S52: acquiring the gear distribution data of the gate hoist; performing gear stress conversion processing on the gear distribution data of the gate hoist according to the gate hoist fault data to generate the gear force distribution; Step S53: performing force simulation based on the force distribution of the gear, wherein the simulated load range is 50N~3000N, and inferring the gear surface deformation based on the simulated gear force data; Step S54: Gear material fatigue analysis is performed through gear surface deformation, and the hoist transmission gear failure is predicted based on gear fatigue data and retrospective fault data, wherein the fatigue life data range is set to 1×10³ times~1×10 7 Secondary cycle; Step S55: Perform a slip-meshing angle compensation simulation on the gate hoist transmission gear fault according to the preset standard transmission gear data, and record the compensation adjustment data, wherein the modulus range of the standard transmission gear data is 2-8 mm, the number of teeth range is 20-100 teeth, and the slip-meshing compensation angle range is set to -5°~+5°; Step S56: Identify abnormal parameters of the gate hoist according to the compensation adjustment data.

9. The gate hoist fault prediction method based on data analysis according to claim 1 is characterized in that: Step S6 includes the following steps: Step S61: Perform interval label mapping processing on the gate hoist abnormal parameters to obtain the gate hoist abnormal classification data, where the abnormal classification interval is set as slight abnormality (0.5mm), moderate abnormality (5.10mm), and severe abnormality (10-20mm); Step S62: Perform feature vector encoding processing on the gate hoist abnormality classification data to obtain the gate hoist feature code, wherein the number of abnormality types is set to 3, and the encoding vector dimension is fixed to [1,0,0], [0,1,0], [0,0,1]; Step S63: Based on the random forest model, the hoist feature code is input adapted and converted to obtain random forest input data, and the input data standardization range is limited to the interval of 0 to 1; Step S64: Perform model prediction based on random forest input data to obtain predicted gate hoist fault data, wherein the predicted probability output range is set between 0 and 1, and a single fault event predicted probability greater than 0.6 is determined as a fault warning.

10. A gate hoist fault prediction system based on data analysis, characterized in that: Used to execute the gate hoist fault prediction method based on data analysis as claimed in claim 1, the gate hoist fault prediction system based on data analysis includes: The signal cleaning module is used to collect the vibration signal of the gate hoist; perform segmented regularization on the vibration signal of the gate hoist and correct the abnormal value to obtain the cleaning characteristic signal; analyze the gate opening and closing timing according to the clear characteristic signal; The cycle determination module is used to determine the normal opening and closing cycle based on the gate opening and closing sequence; the gate healthy opening and closing trajectory is evolved according to the normal opening and closing cycle; The opening and closing simulation module is used to simulate the normal opening and closing of the gate according to the gate's healthy opening and closing trajectory to obtain the simulated normal opening and closing data of the gate; the gate movement wear data is analyzed based on the simulated normal opening and closing data of the gate; The status assessment module is used to obtain the gate status data; estimate the gate status based on the gate movement wear data according to the preset gate usage time, and generate the ideal gate status; determine the gate status gap based on the gate status data and the ideal gate status; The fault backtracking module is used to backtrack faults through the gate status gap, and analyze the gate hoist transmission gear fault data based on the backtracked fault data; identify the gate hoist abnormal parameters based on the gate hoist transmission gear fault data; The fault prediction module is used to predict the gate hoist fault based on the abnormal parameters of the gate hoist based on the random forest model to generate predicted gate hoist fault data.

Images