A Fault Diagnosis Method for Crane Based on Multivariate Adaptive Singular Spectrum Analysis

Abstract

This application relates to a fault diagnosis method for cranes based on multivariate adaptive singular spectrum analysis. The method includes: First, collect multi-channel vibration data; Then, perform multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and residual signals; Then, calculate the complexity index of the multi-channel modal components to determine the feature matrix of multi-channel fault information; Finally, based on the feature matrix, use a convolutional neural network for fault mode recognition to determine the crane fault type. By preprocessing multi-channel signals through multivariate adaptive singular spectrum decomposition, extracting modal components with discriminative features, calculating the complexity index of multi-channel modal components, and combining a convolutional neural network to train the complexity index, representative fault features can be extracted, a feature matrix containing multi-channel fault information can be automatically obtained, automatic fault mode recognition can be realized, and the diagnosis efficiency and accuracy can be greatly improved.

Description

Technical Field

[0001] This application relates to the technical fields of artificial intelligence and mechanical engineering, and particularly to a fault diagnosis method for lifting machinery based on multivariate adaptive singular spectrum analysis. Background Art

[0002] With the continuous advancement of the industrialization process, lifting machinery plays a crucial role in various engineering constructions. The lifting machinery system is widely used in fields such as equipment hoisting, material handling, and transportation, and particularly plays an important role in industries such as construction, mining, ports, and logistics. However, during long-term and high-load operation, due to the complexity and harshness of its working environment, the lifting machinery system is prone to various fault problems, seriously affecting the normal operation and working efficiency of the equipment, and even may lead to equipment damage or casualties, bringing huge potential safety hazards and economic losses to production. Therefore, in order to improve the safety and stability of equipment operation, it is of great necessity and significance to carry out research on the fault diagnosis of the lifting machinery system.

[0003] Currently, many methods have been successfully applied to the fault diagnosis of the lifting system, including wavelet transform, maximum correlation kurtosis deconvolution, spectral kurtosis, morphological filtering, and singular spectrum analysis, etc. However, each of these methods has certain limitations. For example, due to the limitation of the Heisenberg uncertainty principle, the resolution and aggregation of the time-frequency trajectory obtained by wavelet transform are insufficient. In addition, when wavelet transform is used to analyze actual non-stationary vibration signals, it lacks adaptive ability. Maximum correlation kurtosis deconvolution can reveal continuous pulses related to bearing or gear faults, but it requires manual selection of appropriate deconvolution periods and filter lengths. Spectral kurtosis can describe a series of transient characteristics in the original vibration signal, but its two key parameters (i.e., the center frequency of the passband and the resonance bandwidth) have a greater impact on its detection performance. Morphological filtering matches fault information in the vibration signal through a structure element detector, but it is difficult to effectively select the type and size of the structure element. The embedding dimension and time delay of phase space reconstruction in singular spectrum analysis cannot be automatically determined.

[0004] In addition, some adaptive signal decomposition methods (such as empirical mode decomposition, local mean decomposition, intrinsic time-scale decomposition, empirical wavelet transform, and variational mode decomposition) have also been developed for fault diagnosis of mechanical equipment. However, the above signal decomposition methods mainly focus on single-channel data analysis, ignoring the fault information contained in other channels of the crane multi-sensor system. When key parts of the lifting equipment are damaged, the inherent fault characteristics usually spread in the multi-channel signals obtained through multi-sensors. Therefore, in order to obtain more comprehensive fault information, some multi-channel data processing methods (such as multivariate empirical mode decomposition (MEMD), multivariate intrinsic time-scale decomposition (MITD), and multivariate variational mode decomposition (MVMD)) have been gradually proposed to mine the fault information in the multi-sensor data of mechanical equipment. However, the existing multi-channel signal decomposition methods still have the same disadvantages as the traditional single-channel signal decomposition methods. For example, multivariate empirical mode decomposition has problems of mode mixing and end effects; due to the use of linear transformation, multivariate intrinsic time-scale decomposition will show local data fluctuations and curve distortion phenomena when processing mechanical vibration data; the selection of two important parameters (i.e., the penalty factor and the modulus) of multivariate variational mode decomposition highly depends on professional knowledge and experience, and the selection of these parameters directly affects its decomposition performance.

[0005] Therefore, in the related art, there is an urgent need for a method that can improve the accuracy and adaptability of various fault diagnoses of cranes. Summary of the Invention

[0006] Based on this, in view of the above technical problems, it is necessary to provide a fault diagnosis method for cranes based on multivariate adaptive singular spectrum analysis that can improve the accuracy and adaptability of various fault diagnoses of cranes.

[0007] In a first aspect, the present application provides a fault diagnosis method for cranes based on multivariate adaptive singular spectrum analysis. The method includes:

[0008] Collect multi-channel vibration data;

[0009] Perform multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and residual signals;

[0010] Calculate the complexity index of the multi-channel modal components to determine the feature matrix of multi-channel fault information;

[0011] Based on the feature matrix, use a convolutional neural network for fault mode recognition to determine the fault type of the crane.

[0012] Optionally, in an embodiment of the present application, after collecting the multi-channel vibration data, it further includes:

[0013] Initialize the decomposition parameters of the multivariate adaptive singular spectrum analysis, including the sampling frequency, the decomposition stop threshold, and the maximum number of layers into which the signal can be decomposed.

[0014] Optionally, in an embodiment of the present application, the objective function for performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data is:

[0015]

[0016] where J is the objective function, x i (t) is the channel signal, and y ik (t) is the modal component; ‖·‖2 represents the two-norm, which is used to measure the error; λ is the regularization parameter, which is used to control the complexity of signal decomposition; ‖y ik (t)‖ p is the p-norm of the modal component y ik (t), which is used to constrain the complexity of the modal component.

[0017] Optionally, in an embodiment of the present application, the calculation formula of the complexity index is:

[0018]

[0019] where D(y ik (t)) is the improved Kolmogorov complexity, P i is the probability that the observation system is in the i-th unit at the τ-th moment, ε is the number of symbols, N represents the length of the symbol time series, and y ik (t) is the modal component.

[0020] Optionally, in an embodiment of the present application, the fault mode recognition based on the feature matrix using a convolutional neural network includes:

[0021] Using the cross-entropy loss function to optimize the training process of the convolutional neural network.

[0022] Optionally, in an embodiment of the present application, the method further includes:

[0023] Using the accuracy, recall rate, and F1 measurement value metrics to verify the fault recognition performance.

[0024] Optionally, in an embodiment of the present application, the method further includes:

[0025] Using the within-class distance, between-class distance, and ratio of between-class to within-class distance metrics to evaluate the effectiveness of the multi-channel fault information extraction.

[0026] In a second aspect, the present application further provides a fault diagnosis device for a lifting machine based on multivariate adaptive singular spectrum analysis. The device includes:

[0027] A data acquisition module for acquiring multi-channel vibration data;

[0028] A multivariate adaptive singular spectrum analysis module for performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and a residual signal;

[0029] A multi-channel fault information determination module for calculating complexity indexes of the multi-channel modal components and determining a feature matrix of multi-channel fault information;

[0030] A crane fault diagnosis module for performing fault mode recognition based on the feature matrix using a convolutional neural network to determine the crane fault type.

[0031] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the steps of the methods in the above various embodiments.

[0032] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the methods in the above various embodiments are implemented.

[0033] For the above crane fault diagnosis method based on multivariate adaptive singular spectrum analysis, first, multi-channel vibration data is acquired; then, multivariate adaptive singular spectrum decomposition is performed on the multi-channel vibration data to obtain multi-channel modal components and a residual signal; then, complexity indexes of the multi-channel modal components are calculated to determine a feature matrix of multi-channel fault information; finally, fault mode recognition is performed based on the feature matrix using a convolutional neural network to determine the crane fault type. That is to say, by performing preprocessing of multivariate adaptive singular spectrum decomposition on multi-channel signals, extracting modal components with discriminative features, calculating complexity indexes of multi-channel modal components, and combining a convolutional neural network (CNN) to train the complexity indexes, noise components in the signals can be effectively separated, representative fault features can be extracted, a feature matrix containing multi-channel fault information can be automatically obtained, automatic fault mode recognition of the crane system can be realized, the problem of difficult feature selection is solved, the robustness of the system is significantly improved, and the diagnostic efficiency and accuracy are greatly improved. Description of the Drawings

[0034] Figure 1 It is an application environment diagram of a crane fault diagnosis method based on multivariate adaptive singular spectrum analysis in an embodiment;

[0035] Figure 2Schematic diagram of a fault diagnosis method for a lifting machine based on multivariate adaptive singular spectrum analysis in an embodiment;

[0036] Figure 3 Schematic diagrams of the time-domain waveform, Fourier spectrum, and envelope spectrum of multi-channel fault signals collected by the core components in an embodiment;

[0037] Figure 4 Schematic diagram of the structure of the multivariate adaptive singular spectrum decomposition result in an embodiment;

[0038] Figure 5 Block diagram of the structure of a fault diagnosis device for a lifting machine based on multivariate adaptive singular spectrum analysis in an embodiment;

[0039] Figure 6 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0040] In order to make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] A fault diagnosis method for a lifting machine based on multivariate adaptive singular spectrum analysis provided by an embodiment of the present application can be applied to an application environment as shown in Figure 1 . Among them, the terminal communicates with the server through a network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server, or placed in the cloud or other network servers. Among them, the terminal can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0042] In one embodiment, as shown in Figure 2 , a fault diagnosis method for a lifting machine based on multivariate adaptive singular spectrum analysis is provided. Taking the method applied to the server in Figure 1 as an example, the method includes the following steps:

[0043] S201: Collect multi-channel vibration data.

[0044] In the embodiment of the present application, first, a multi-sensor device is installed on the lifting machinery system to collect multi-channel vibration data under different health states. The collected multi-channel vibration data is a multi-channel signal X(t) = {x1(t), x2(t), ..., x m (t)}, where x m (t)=[p(1),p(2),…,p(N)] T ,m=1,2,… represents the mth variable signal, p(N) is the signal x m (t) is the Nth data point. Figure 3 Shown are the time domain waveforms, Fourier spectra and envelope spectra of multi-channel fault signals collected from four core components (i.e., equalizing beam, A-frame, flying arm and lifting arm).

[0045] Afterwards, in one embodiment of the present application, the collecting of multi-channel vibration data further includes:

[0046] Initialize the decomposition parameters of multivariate adaptive singular spectrum analysis, including sampling frequency, decomposition stop threshold, and the maximum number of layers that the signal can be decomposed.

[0047] S203: performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and residual signals.

[0048] In the embodiment of the present application, multivariate adaptive singular spectrum decomposition is performed on the collected multi-channel vibration data, i.e., the multi-channel signal X(t), to obtain the multi-channel modal components containing the discriminant information. Specifically, for each channel signal x i (t), the goal of its multivariate adaptive singular spectrum decomposition is to decompose it into multiple modal components y ik (t), and remove noise and unnecessary information as much as possible. The decomposition process is optimized by minimizing the relevant objective function.

[0049] Specifically, in one embodiment of the present application, the objective function of performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data is:

[0050]

[0051] Among them, J is the objective function, x i (t) is the channel signal, y ik (t) is the modal component; ‖·‖2 represents the bi-norm, which is used to measure the error; λ is the regularization parameter, which is used to control the complexity of signal decomposition; ‖y ik (t)‖ p is the modal component y ik The p-norm of (t) is used to constrain the complexity of the modal components.

[0052] Regarding whether the decomposition result meets the expectation, it is usually determined by judging whether the stopping decomposition criterion is satisfied, that is, when the normalized mean square error between the residual signal V (k) (t) and the multi-channel signal X(t) is less than the preset decomposition stop threshold θ, it is proved that the decomposition result meets the expectation and the signal decomposition process stops. At this time, K multi-channel modal components and K residual signals can be obtained. If the stopping decomposition criterion is not satisfied, the extracted components are subtracted from the current residual signal to obtain a new residual signal, and then the new residual signal is used as the input signal to continue the decomposition until the stopping decomposition criterion is satisfied. Finally, the result of the multivariate adaptive singular spectrum analysis of the multi-channel signal is represented as a set of K multi-channel modal components and the corresponding residual signals, which is expressed by the following formula.

[0053]

[0054] where y ik (t) can be intuitively understood as a spatial vector with three directions (i.e., the X-axis, Y-axis, and Z-axis). The X-axis direction represents the number of multi-channel modal components, the Y-axis direction represents the length of the multi-channel modal components, and the Z-axis direction represents the number of channels of the input multi-channel signal. As Figure 4 shown, it is an example of the decomposition result, (a) balance beam, (b) A-frame, (c) fly arm, (d) boom.

[0055] S205: Calculate the complexity index of the multi-channel modal components and determine the characteristic matrix of the multi-channel fault information.

[0056] In the embodiment of the present application, after obtaining the multi-channel modal components, the dynamic characteristics of the signal are captured by calculating the complexity index of the modal component y ik (t), and the characteristic matrix containing the multi-channel fault information is determined based on this. Optionally, the rate of change of the modal component is measured by calculating the improved Kolmogorov complexity D(y ik (t)).

[0057] Specifically, in an embodiment of the present application, the calculation formula of the complexity index is:

[0058]

[0059] where D(y ik (t)) is the improved Kolmogorov complexity, P i is the probability that the observation system is located in the i-th unit at the τ-th moment, ε is the number of symbols, N represents the length of the symbol time series, and y ik (t) is the modal component.

[0060] By calculating the value of D(y ik (t)), a feature matrix F containing multi-channel fault information can be obtained as follows.

[0061]

[0062] where l is the number of channels, R is the number of modal components, and D ik is the complexity index of the k-th modal component of the i-th channel. By calculating the feature matrix F, rich multi-channel fault information can be provided for subsequent fault mode recognition.

[0063] S207: Based on the feature matrix, a convolutional neural network is used for fault mode recognition to determine the fault type of the crane.

[0064] In the embodiment of the present application, the features containing multi-channel fault information extracted are randomly divided into training samples and test samples according to a ratio of 1:1. Among them, the training samples are used to train the convolutional neural network (CNN) model, and the test samples are input into the trained CNN model to realize the automatic recognition of different fault modes of the crane system and determine the fault type of the crane.

[0065] Specifically, in an embodiment of the present application, the using a convolutional neural network for fault mode recognition based on the feature matrix includes:

[0066] Using a cross-entropy loss function to optimize the training process of the convolutional neural network.

[0067] In an embodiment of the present application, the training process of the convolutional neural network (CNN) model is optimized by minimizing the loss function, and the loss function uses a cross-entropy loss function, which is expressed as follows:

[0068]

[0069] where y i is the actual label (fault type), is the predicted value output by the CNN model. The backpropagation algorithm of stochastic gradient descent is used to learn the fault mode in the feature matrix, thereby completing the recognition of the fault mode.

[0070] In an embodiment of the present application, the method further includes:

[0071] Using accuracy, recall rate, and F1 measurement value metrics to verify the fault recognition performance.

[0072] In an embodiment of the present application, by calculating the predicted value and the actual label y iThe precision p, recall Re, and F1 measurement values are used to verify the fault identification performance, and the specific calculation formulas are as follows:

[0073]

[0074] Among them, T p represents the number of true positives, B p represents the number of false positives, B N represents the number of false negatives, β is the weighting parameter, and the higher the F1 measurement value, the better the recognition result, usually ranging from 0 to 1.

[0075] In an embodiment of the present application, the method further includes:

[0076] Evaluating the effectiveness of the multi-channel fault information extraction by using the within-class distance, between-class distance, and the ratio of between-class to within-class distance indicators.

[0077] In an embodiment of the present application, by calculating the within-class distance W d , between-class distance C d , and the ratio of between-class to within-class distance C d / W d , to quantitatively evaluate the effectiveness of the multi-channel fault information extraction. The specific calculation method is as follows:

[0078]

[0079] Among them, u() represents the Euclidean distance between two vectors, and respectively represent the k-th and l-th samples of the i-th category, represents the l-th sample of the j-th category, N i and N j respectively represent the number of samples of the i-th category and the j-th category.

[0080] In the above-mentioned fault diagnosis method for lifting machinery based on multivariate adaptive singular spectrum analysis, first, multi-channel vibration data is collected; then, the multi-channel vibration data is subjected to multivariate adaptive singular spectrum decomposition to obtain multi-channel modal components and residual signals; then, the complexity index of the multi-channel modal components is calculated to determine the feature matrix of multi-channel fault information; finally, based on the feature matrix, a convolutional neural network is used for fault mode recognition to determine the fault type of the lifting machinery. That is to say, by performing preprocessing of multivariate adaptive singular spectrum decomposition on multi-channel signals, extracting modal components with discriminative features, calculating the complexity index of multi-channel modal components, and combining a convolutional neural network (CNN) to train the complexity index, the noise components in the signal can be effectively separated, representative fault features can be extracted, a feature matrix containing multi-channel fault information can be automatically obtained, automatic fault mode recognition of the lifting machinery system can be realized, the problem of difficult feature selection is solved, the robustness of the system is significantly improved, and the diagnostic efficiency and accuracy are greatly improved.

[0081] It should be understood that although the steps in the flowcharts involved in the above-mentioned embodiments are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps does not have a strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-mentioned embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0082] Based on the same inventive concept, an embodiment of the present application further provides a fault diagnosis device for lifting machinery based on multivariate adaptive singular spectrum analysis for implementing the above-mentioned fault diagnosis method for lifting machinery based on multivariate adaptive singular spectrum analysis. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following fault diagnosis device for lifting machinery based on multivariate adaptive singular spectrum analysis can refer to the limitations on the fault diagnosis method for lifting machinery based on multivariate adaptive singular spectrum analysis in the above text, and will not be repeated here.

[0083] In one embodiment, as Figure 5As shown, a fault diagnosis device 500 for a lifting machine based on multivariate adaptive singular spectrum analysis is provided, including: a data acquisition module 501, a multivariate adaptive singular spectrum analysis module 503, a multi-channel fault information determination module 505, and a lifting machine fault diagnosis module 507, where:

[0084] The data acquisition module 501 is used to acquire multi-channel vibration data.

[0085] The multivariate adaptive singular spectrum analysis module 503 is used to perform multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and a residual signal.

[0086] The multi-channel fault information determination module 505 is used to calculate the complexity index of the multi-channel modal components and determine the feature matrix of the multi-channel fault information.

[0087] The lifting machine fault diagnosis module 507 is used to perform fault mode recognition based on the feature matrix using a convolutional neural network to determine the fault type of the lifting machine.

[0088] In an embodiment of the present application, the multivariate adaptive singular spectrum analysis module is further used to:

[0089] Initialize the decomposition parameters of the multivariate adaptive singular spectrum analysis, including the sampling frequency, the decomposition stop threshold, and the maximum number of layers into which the signal can be decomposed.

[0090] In an embodiment of the present application, the objective function for performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data is:

[0091]

[0092] where J is the objective function, x i (t) is the channel signal, y ik (t) is the modal component; ‖·‖2 represents the second norm, which is used to measure the error; λ is the regularization parameter, which is used to control the complexity of the signal decomposition; ‖y ik (t)‖ p is the p-norm of the modal component y ik (t), which is used to constrain the complexity of the modal component.

[0093] In an embodiment of the present application, the calculation formula for the complexity index is:

[0094]

[0095] where D(y ik (t)) is the improved Kolmogorov complexity, P iis the probability that the observation system is located in the i-th unit at the τ-th moment, ε is the number of symbols, N represents the length of the symbol time series, and y ik (t) is the modal component.

[0096] In an embodiment of the present application, the lifting machinery fault diagnosis module is further configured to:

[0097] Optimize the training process of the convolutional neural network by using the cross-entropy loss function.

[0098] In an embodiment of the present application, the method further includes:

[0099] Verify the fault identification performance by using accuracy, recall rate, and F1 measurement value metrics.

[0100] In an embodiment of the present application, the method further includes:

[0101] Evaluate the effectiveness of the multi-channel fault information extraction by using the within-class distance, between-class distance, and ratio of between-class to within-class distance metrics.

[0102] Each module in the above-mentioned lifting machinery fault diagnosis device based on multivariate adaptive singular spectrum analysis can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0103] In an embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near-field communication), or other technologies. When the computer program is executed by the processor, it implements a lifting machinery fault diagnosis method based on multivariate adaptive singular spectrum analysis. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0104] Those skilled in the art can understand that Figure 6 The structure shown in Figure 6 is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0105] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0106] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0107] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0108] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0109] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0110] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0111] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A fault diagnosis method for lifting machinery based on multivariate adaptive singular spectrum analysis, characterized in that, The method includes: Collecting multi-channel vibration data; Performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and a residual signal; Calculating the complexity index of the multi-channel modal components to determine the feature matrix of multi-channel fault information; Performing fault mode recognition using a convolutional neural network based on the feature matrix to determine the fault type of the crane; After collecting the multi-channel vibration data, it further includes: Initializing the decomposition parameters of the multivariate adaptive singular spectrum analysis, including the sampling frequency, the decomposition stop threshold, and the maximum number of layers into which the signal can be decomposed; The objective function for performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data is: Among them, is the objective function, is the channel signal, is the modal component; represents the two-norm, which is used to measure the error; is the regularization parameter, which is used to control the complexity of signal decomposition; is the modal component of p- norm, which is used to constrain the complexity of the modal component; The calculation formula for the complexity index is: wherein, is the improved Kolmogorov complexity, is the probability that the observation system is located in the th unit at the th moment, is the number of symbols, represents the length of the symbol time series, is the modal component.

2. The fault diagnosis method for a hoisting machine based on multivariate adaptive singular spectrum analysis according to claim 1, characterized in that The performing fault mode recognition using a convolutional neural network based on the feature matrix includes: Optimizing the training process of the convolutional neural network using a cross-entropy loss function.

3. A fault diagnosis method for a hoisting machine based on multivariate adaptive singular spectrum analysis according to claim 1, characterized in that The method further includes: Verifying the fault recognition performance using accuracy, recall rate, and F1 measurement value metrics.

4. A fault diagnosis method for a lifting machine based on multivariate adaptive singular spectrum analysis according to claim 1, characterized in that, The method further includes: Evaluating the effectiveness of the extraction of the multi-channel fault information using within-class distance, between-class distance, and the ratio of between-class to within-class distance metrics.

5. An apparatus for implementing the fault diagnosis method of a crane based on multivariate adaptive singular spectrum analysis as described in claim 1, characterized in that, The device includes: A data acquisition module for collecting multi-channel vibration data; A multivariate adaptive singular spectrum analysis module for performing multivariate adaptive singular spectrum decomposition on the multi-channel vibration data to obtain multi-channel modal components and a residual signal; A multi-channel fault information determination module for calculating the complexity index of the multi-channel modal components to determine the feature matrix of multi-channel fault information; A crane fault diagnosis module for performing fault mode recognition using a convolutional neural network based on the feature matrix to determine the fault type of the crane.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 4.

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