Temperature vibration data processing and monitoring system and method based on device vibration signal change

Abstract

The invention discloses a device vibration signal change-based temperature vibration data processing and monitoring system and method, and belongs to the technical field of data processing, a data acquisition module acquires and preprocesses a multi-modal signal to obtain a preprocessed multi-modal signal, a feature extraction module performs feature extraction on the preprocessed multi-modal signal to obtain a feature extraction result, and the feature extraction result is a feature extraction result. The method comprises the following steps: training a health assessment model to obtain a time domain feature, a frequency domain feature and a time-frequency domain feature, performing data fusion on the time domain feature, the frequency domain feature and the time-frequency domain feature by a data fusion module to obtain a fusion feature, inputting the fusion feature into the trained health assessment model by a health assessment module to obtain a health state assessment result, judging whether to trigger an early warning mechanism, and if so, generating early warning information. According to the method, the vibration signals and the temperature signals of the equipment can be continuously monitored, potential fault signs can be recognized earlier, more comprehensive signal representation is obtained by fusing the time domain features, the frequency domain features and the time-frequency domain features, the fault detection accuracy is improved, and early warning is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a temperature-vibration data processing and monitoring system and method based on changes in device vibration signals. Background Art

[0002] With the development of industrial technology, mechanical equipment is increasingly being used in various fields. For example, mechanical equipment is widely used in industrial manufacturing. In industrial workshops, these devices need to operate for long periods of time in harsh environments such as high temperature and high pressure. Therefore, higher performance requirements are required. To ensure the safe and stable operation of the equipment, real-time monitoring and management of the equipment's temperature and vibration are necessary. Traditional equipment temperature and vibration monitoring methods mainly rely on manual periodic inspections and maintenance, which is time-consuming and labor-intensive, and makes it difficult to achieve real-time monitoring of the equipment.

[0003] In recent years, with the advancement of sensor and communication technologies, companies have begun adopting intelligent detection systems to replace traditional manual monitoring methods. However, existing intelligent detection systems rely solely on monitoring a single parameter, such as temperature or vibration. Many equipment failures in their early stages do not cause noticeable temperature changes, but rather subtle changes in vibration patterns. Existing intelligent detection systems are unable to detect these early signs, and the accuracy of fault detection based on monitoring a single parameter is low. Therefore, based on the above description, there is an urgent need to provide an effective technical solution to address the problems of inability to detect these early signs and the low detection accuracy. Summary of the Invention

[0004] The purpose of the present invention is to provide a temperature-vibration data processing and monitoring system and method based on changes in device vibration signals, so as to solve the above-mentioned problems existing in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a temperature-vibration data processing and monitoring system based on changes in device vibration signals, comprising a data acquisition module, a feature extraction module, a data fusion module, and a health assessment module; The data acquisition module is used to collect multimodal signals, wherein the multimodal signals include vibration signals of wired devices, vibration signals of wireless devices, and temperature signals, preprocess the multimodal signals to obtain preprocessed multimodal signals, and upload the preprocessed multimodal signals to the feature extraction module; The feature extraction module is used to extract features from the preprocessed multimodal signal to obtain time domain features, frequency domain features and time-frequency domain features, and upload the time domain features, frequency domain features and time-frequency domain features to the data fusion module; The data fusion module is used to fuse the time domain features, frequency domain features and time-frequency domain features to obtain fused features, and upload the fused features to the health assessment module; The health assessment module is used to obtain historical vibration signals, historical temperature signals and historical fault data, train a health assessment model based on the historical vibration signals, historical temperature signals and historical fault data, obtain a trained health assessment model, input the fusion features into the trained health assessment model, obtain a health status assessment result, and determine whether the health status assessment result triggers an early warning mechanism. If so, generate early warning information.

[0006] In one possible design, preprocessing the multimodal signal to obtain a preprocessed multimodal signal includes: Performing a time alignment operation on the multimodal signal to obtain an aligned multimodal signal; Perform data cleaning and missing value filling on the aligned multimodal signals; The filled multimodal signal is subjected to wavelet denoising, and the denoised multimodal signal is normalized to obtain a preprocessed multimodal signal.

[0007] In one possible design, feature extraction is performed on the preprocessed multimodal signal to obtain time domain features, frequency domain features, and time-frequency domain features, including: Decomposing the preprocessed multimodal signal based on the optimized variational mode decomposition to obtain a multimodal decomposition signal; A convolutional neural network is used to extract features from the multimodal decomposition signal to obtain initial vibration features and initial temperature features. The initial vibration features and initial temperature features are then mapped into a high-dimensional space to obtain high-dimensional vibration feature vectors and high-dimensional temperature feature vectors. Perform entropy separation on the high-dimensional vibration feature vector to obtain the optimal entropy dimension. Based on the optimal entropy dimension, the vibration feature is obtained. Feature selection is performed on the high-dimensional temperature feature vector to obtain the temperature feature. Based on the vibration characteristics and temperature characteristics, time domain characteristics, frequency domain characteristics and time-frequency domain characteristics are obtained.

[0008] In one possible design, based on the vibration characteristics and temperature characteristics, time domain characteristics, frequency domain characteristics, and time-frequency domain characteristics are obtained, including: Based on the vibration characteristics and temperature characteristics, the time domain characteristics are obtained; Performing Fourier transform on the vibration characteristics and temperature characteristics to obtain transformed characteristics, and obtaining frequency domain characteristics based on the transformed characteristics; Wavelet transform is used to process the vibration characteristics and temperature characteristics, and the time-frequency domain characteristics are obtained based on the vibration characteristics and temperature characteristics after wavelet transform.

[0009] In one possible design, entropy separation is performed on the high-dimensional vibration feature vector to obtain the optimal entropy dimension. Based on the optimal entropy dimension, vibration features are obtained, including: Calculate the information entropy of each high-dimensional vibration eigenvector and the mutual information between any two high-dimensional vibration eigenvectors; Construct an entropy separation matrix based on information entropy and mutual information; Based on the entropy separation matrix, the optimal entropy dimension is selected; The high-dimensional vibration features are extracted according to the optimal entropy dimension to obtain the vibration features.

[0010] In one possible design, the variational mode decomposition is optimized based on a snake optimization algorithm; and the preprocessed multimodal signal is decomposed based on the optimized variational mode decomposition to obtain a multimodal decomposition signal, including: Initialize the intrinsic mode function and corresponding center frequency of the preprocessed multimodal signal; A fitness function is defined based on the intrinsic mode function, and the parameters of the fitness function are updated by the snake optimization algorithm to obtain an updated fitness function. Variational mode decomposition is performed based on the updated fitness function to obtain an optimized intrinsic mode function and the corresponding optimized center frequency; The optimized intrinsic mode function and the corresponding optimized center frequency are iteratively updated until the preset iteration termination condition is reached to obtain a multimodal decomposition signal.

[0011] In one possible design, determining whether the health status assessment results trigger an early warning mechanism includes: Pre-constructing a health status rating table, wherein the health status levels in the health status rating table include excellent, good, fair, and critical; matching the health status level in the health status level table according to the health status assessment result; Determine whether the matched health status level triggers the early warning mechanism. When the matched health status level is excellent or good, the early warning mechanism is not triggered; when the matched health status level is general or critical, the early warning mechanism is triggered.

[0012] In a second aspect, the present invention provides a method for processing and monitoring temperature and vibration data based on changes in device vibration signals, comprising: Collecting a multimodal signal, the multimodal signal including a vibration signal of a wired device and a vibration signal and a temperature signal of a wireless device, and preprocessing the multimodal signal to obtain a preprocessed multimodal signal; Perform feature extraction on the preprocessed multimodal signal to obtain time domain features, frequency domain features and time-frequency domain features; Perform data fusion on time domain features, frequency domain features and time-frequency domain features to obtain fusion features; Obtain historical vibration signals, historical temperature signals, and historical fault data, train a health assessment model based on the historical vibration signals, historical temperature signals, and historical fault data, obtain a trained health assessment model, input the fusion features into the trained health assessment model, obtain a health status assessment result, determine whether the health status assessment result triggers an early warning mechanism, and if so, generate early warning information.

[0013] In a third aspect, the present invention provides a computer device comprising a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the temperature and vibration data processing and monitoring method based on changes in device vibration signals as described in the second aspect above.

[0014] In a fourth aspect, the present invention provides a computer program product comprising instructions, which, when executed on a computer, causes the computer to execute any one of the above-described methods for processing and monitoring temperature and vibration data based on changes in device vibration signals.

[0015] The beneficial effects of the present invention are as follows: The present invention provides a temperature-vibration data processing and monitoring system and method based on changes in device vibration signals, comprising a data acquisition module, a feature extraction module, a data fusion module and a health assessment module. The data acquisition module acquires multimodal signals and preprocesses the multimodal signals to obtain preprocessed multimodal signals. The feature extraction module extracts features from the preprocessed multimodal signals to obtain time domain features, frequency domain features and time-frequency domain features. The data fusion module fuses the time domain features, frequency domain features and time-frequency domain features to obtain fused features. The health assessment module obtains historical vibration signals, historical temperature signals and historical fault data, trains a health assessment model based on the historical vibration signals, historical temperature signals and historical fault data to obtain a trained health assessment model, inputs the fused features into the trained health assessment model to obtain a health status assessment result, determines whether the health status assessment result triggers an early warning mechanism, and generates early warning information if so. The present invention can continuously monitor the vibration signals and temperature signals of the equipment. By processing the vibration signals and temperature signals, it can identify potential fault signs earlier. By fusing time domain features, frequency domain features and time-frequency domain features, a more comprehensive signal representation is obtained, thereby improving the accuracy of fault detection and achieving early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A module block diagram of a temperature-vibration data processing and monitoring system based on device vibration signal changes provided in the first aspect of this embodiment; Figure 2This is a flow chart of a method for processing and monitoring temperature and vibration data based on changes in device vibration signals provided in the second aspect of this embodiment. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0018] Example: like Figure 1 As shown, the first aspect of this embodiment provides a temperature-vibration data processing and monitoring system based on changes in device vibration signals, including a data acquisition module, a feature extraction module, a data fusion module, and a health assessment module; The data acquisition module is used to collect multimodal signals, wherein the multimodal signals include vibration signals of wired devices, vibration signals of wireless devices, and temperature signals, preprocess the multimodal signals to obtain preprocessed multimodal signals, and upload the preprocessed multimodal signals to the feature extraction module; The feature extraction module is used to extract features from the preprocessed multimodal signal to obtain time domain features, frequency domain features and time-frequency domain features, and upload the time domain features, frequency domain features and time-frequency domain features to the data fusion module; The data fusion module is used to fuse the time domain features, frequency domain features and time-frequency domain features to obtain fused features, and upload the fused features to the health assessment module; The health assessment module is used to obtain historical vibration signals, historical temperature signals and historical fault data, train a health assessment model based on the historical vibration signals, historical temperature signals and historical fault data, obtain a trained health assessment model, input the fusion features into the trained health assessment model, obtain a health status assessment result, and determine whether the health status assessment result triggers an early warning mechanism. If so, generate early warning information.

[0019] Furthermore, wired devices include fan devices, pump devices, axial flow motor devices and air compressor devices that are based on wired carrier transmission using cables or optical cables, and wireless devices include fan devices, pump devices, axial flow motor devices and air compressor devices that are based on 5G / 4G wireless signal transmission due to long distances, inconvenience in wiring or special environmental requirements.

[0020] In one possible design, preprocessing the multimodal signal to obtain a preprocessed multimodal signal includes: Performing a time alignment operation on the multimodal signal to obtain an aligned multimodal signal; Perform data cleaning and missing value filling on the aligned multimodal signals; The filled multimodal signal is subjected to wavelet denoising, and the denoised multimodal signal is normalized to obtain a preprocessed multimodal signal.

[0021] Specifically, the collected multimodal signals include time information. The multimodal signals are time-aligned according to the time signals to obtain aligned multimodal signals, so that the signals of different modes can ensure data consistency and facilitate subsequent signal processing.

[0022] In one possible design, feature extraction is performed on the preprocessed multimodal signal to obtain time domain features, frequency domain features, and time-frequency domain features, including: Decomposing the preprocessed multimodal signal based on the optimized variational mode decomposition to obtain a multimodal decomposition signal; A convolutional neural network is used to extract features from the multimodal decomposition signal to obtain initial vibration features and initial temperature features. The initial vibration features and initial temperature features are then mapped into a high-dimensional space to obtain high-dimensional vibration feature vectors and high-dimensional temperature feature vectors. Perform entropy separation on the high-dimensional vibration feature vector to obtain the optimal entropy dimension. Based on the optimal entropy dimension, the vibration feature is obtained. Feature selection is performed on the high-dimensional temperature feature vector to obtain the temperature feature. Based on the vibration characteristics and temperature characteristics, time domain characteristics, frequency domain characteristics and time-frequency domain characteristics are obtained.

[0023] Variational mode decomposition (VMD) is an adaptive signal processing method that decomposes signals into modal components with limited bandwidth using a variational framework. In this embodiment, VMD is optimized using the Snake Optimizer (SO) algorithm. The SO algorithm simulates the crawling and searching behavior of snakes. When searching for food, snakes twist their bodies and explore their surroundings to find their target. The SO algorithm abstracts this process into a mathematical model and finds the optimal solution to the optimization problem by simulating the crawling and searching behavior of snakes.

[0024] In one possible design, based on the vibration characteristics and temperature characteristics, time domain characteristics, frequency domain characteristics, and time-frequency domain characteristics are obtained, including: Based on the vibration characteristics and temperature characteristics, the time domain characteristics are obtained; Performing Fourier transform on the vibration characteristics and temperature characteristics to obtain transformed characteristics, and obtaining frequency domain characteristics based on the transformed characteristics; Wavelet transform is used to process the vibration characteristics and temperature characteristics, and the time-frequency domain characteristics are obtained based on the vibration characteristics and temperature characteristics after wavelet transform.

[0025] Among them, Fourier transform is an integral transform, the principle of which is to decompose complex time domain functions into a superposition of sine waves or cosine waves of different frequencies, thereby revealing the frequency components of the signal; the principle of wavelet transform is to perform time-frequency localization analysis of the signal by scaling and translating the mother wavelet.

[0026] In one possible design, entropy separation is performed on the high-dimensional vibration feature vector to obtain the optimal entropy dimension. Based on the optimal entropy dimension, vibration features are obtained, including: Calculate the information entropy of each high-dimensional vibration eigenvector and the mutual information between any two high-dimensional vibration eigenvectors; Construct an entropy separation matrix based on information entropy and mutual information; Based on the entropy separation matrix, the optimal entropy dimension is selected; The high-dimensional vibration features are extracted according to the optimal entropy dimension to obtain the vibration features.

[0027] In one possible design, decomposing the preprocessed multimodal signal based on the optimized variational modal decomposition to obtain a multimodal decomposition signal includes: Initialize the intrinsic mode function and corresponding center frequency of the preprocessed multimodal signal; A fitness function is defined based on the intrinsic mode function, and the parameters of the fitness function are updated by the snake optimization algorithm to obtain an updated fitness function. Variational mode decomposition is performed based on the updated fitness function to obtain an optimized intrinsic mode function and the corresponding optimized center frequency; The optimized intrinsic mode function and the corresponding optimized center frequency are iteratively updated until the preset iteration termination condition is reached to obtain a multimodal decomposition signal.

[0028] Specifically, the fitness function is updated by the snake optimization algorithm to obtain the optimal intrinsic mode function and the corresponding optimized center frequency, so that the parameters of the variational mode decomposition are adaptively adjusted to improve the flexibility and accuracy of the decomposition.

[0029] In one possible design, determining whether the health status assessment results trigger an early warning mechanism includes: Pre-constructing a health status rating table, wherein the health status levels in the health status rating table include excellent, good, fair, and critical; matching the health status level in the health status level table according to the health status assessment result; Determine whether the matched health status level triggers the early warning mechanism. When the matched health status level is excellent or good, the early warning mechanism is not triggered; when the matched health status level is general or critical, the early warning mechanism is triggered.

[0030] In one possible design, the health assessment model is built based on a deep learning model.

[0031] In a possible design, a display module is also included, which is in communication with the health assessment module. The display module is used to display early warning information so that staff can find the fault point, find the faulty equipment, and maintain and repair the faulty equipment.

[0032] This embodiment provides a temperature and vibration data processing and monitoring system based on changes in device vibration signals, comprising a data acquisition module, a feature extraction module, a data fusion module, and a health assessment module. The data acquisition module acquires and preprocesses multimodal signals to obtain preprocessed multimodal signals. The feature extraction module extracts features from the preprocessed multimodal signals to obtain time domain features, frequency domain features, and time-frequency domain features. The data fusion module fuses the time domain features, frequency domain features, and time-frequency domain features to obtain fused features. The health assessment module trains a health assessment model to obtain a trained health assessment model. Based on the trained health assessment model and the fused features, a health status assessment result is obtained. Whether an early warning mechanism is triggered is determined based on the health status assessment result. If so, an early warning message is generated. This embodiment uses optimized variational mode decomposition and convolutional neural networks to extract features from multimodal signals and perform data fusion to obtain more comprehensive and complete features, thereby improving the accuracy of fault detection and diagnosis and facilitating equipment maintenance and repair by personnel.

[0033] like Figure 2 As shown, the second aspect of this embodiment provides a method for processing and monitoring temperature and vibration data based on changes in device vibration signals, including but not limited to the following steps: S1. Collecting multimodal signals, the multimodal signals include vibration signals of wired devices and vibration signals of wireless devices, temperature signals, and preprocessing the multimodal signals to obtain preprocessed multimodal signals; S2. extracting features from the preprocessed multimodal signal to obtain time domain features, frequency domain features, and time-frequency domain features; S3. Fusing the time domain features, frequency domain features, and time-frequency domain features to obtain fused features; S4. Obtain historical vibration signals, historical temperature signals, and historical fault data, train a health assessment model based on the historical vibration signals, historical temperature signals, and historical fault data, obtain a trained health assessment model, input the fusion features into the trained health assessment model, obtain a health status assessment result, determine whether the health status assessment result triggers an early warning mechanism, and if so, generate early warning information.

[0034] The third aspect of this embodiment provides a computer device for executing the temperature vibration data processing and monitoring method as described in the second aspect, comprising a memory, a processor, and a transceiver that are sequentially connected in communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the temperature vibration data processing and monitoring method as described in the second aspect. For example, the memory may include, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a flash memory, a first-in-first-out memory (FIFO), and / or a first-in-last-out memory (FILO), etc.; the processor may include, but is not limited to, a microprocessor of the STM32F105 series. In addition, the computer device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0035] The working process, working details and technical effects of the aforementioned computer equipment provided in the third aspect of this embodiment can be found in the temperature vibration data processing and monitoring method described in the second aspect, and will not be repeated here.

[0036] In a fourth aspect of this embodiment, there is provided a computer-readable storage medium storing instructions including the temperature vibration data processing and monitoring method as described in the second aspect, that is, the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the temperature vibration data processing and monitoring method as described in the second aspect is executed. The computer-readable storage medium refers to a carrier for storing data, and may include, but is not limited to, computer-readable storage media such as a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive, and / or a memory stick. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0037] The working process, working details and technical effects of the aforementioned computer-readable storage medium provided in the fourth aspect of this embodiment can be referred to the temperature vibration data processing and monitoring method described in the second aspect, and will not be repeated here.

[0038] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A temperature vibration data processing and monitoring system based on device vibration signal changes, characterized in that: It includes data acquisition module, feature extraction module, data fusion module and health assessment module; The data acquisition module is used to collect multimodal signals, wherein the multimodal signals include vibration signals of wired devices, vibration signals of wireless devices, and temperature signals, preprocess the multimodal signals to obtain preprocessed multimodal signals, and upload the preprocessed multimodal signals to the feature extraction module; The feature extraction module is used to extract features from the preprocessed multimodal signal to obtain time domain features, frequency domain features and time-frequency domain features, and upload the time domain features, frequency domain features and time-frequency domain features to the data fusion module; The data fusion module is used to fuse the time domain features, frequency domain features and time-frequency domain features to obtain fused features, and upload the fused features to the health assessment module; The health assessment module is used to obtain historical vibration signals, historical temperature signals and historical fault data, train a health assessment model based on the historical vibration signals, historical temperature signals and historical fault data, obtain a trained health assessment model, input the fusion features into the trained health assessment model, obtain a health status assessment result, and determine whether the health status assessment result triggers an early warning mechanism. If so, generate early warning information.

2. The temperature-vibration data processing and monitoring system based on device vibration signal changes according to claim 1, characterized in that: Preprocessing the multimodal signal to obtain a preprocessed multimodal signal includes: Performing a time alignment operation on the multimodal signal to obtain an aligned multimodal signal; Perform data cleaning and missing value filling on the aligned multimodal signals; The filled multimodal signal is subjected to wavelet denoising, and the denoised multimodal signal is normalized to obtain a preprocessed multimodal signal.

3. The temperature-vibration data processing and monitoring system based on device vibration signal changes according to claim 1, characterized in that: Perform feature extraction on the preprocessed multimodal signal to obtain time domain features, frequency domain features, and time-frequency domain features, including: Decomposing the preprocessed multimodal signal based on the optimized variational mode decomposition to obtain a multimodal decomposition signal; A convolutional neural network is used to extract features from the multimodal decomposition signal to obtain initial vibration features and initial temperature features. The initial vibration features and initial temperature features are then mapped into a high-dimensional space to obtain high-dimensional vibration feature vectors and high-dimensional temperature feature vectors. Perform entropy separation on the high-dimensional vibration feature vector to obtain the optimal entropy dimension. Based on the optimal entropy dimension, the vibration feature is obtained. Feature selection is performed on the high-dimensional temperature feature vector to obtain the temperature feature. Based on the vibration characteristics and temperature characteristics, time domain characteristics, frequency domain characteristics and time-frequency domain characteristics are obtained.

4. The temperature-vibration data processing and monitoring system based on device vibration signal changes according to claim 3, characterized in that: Based on the vibration characteristics and temperature characteristics, the time domain characteristics, frequency domain characteristics and time-frequency domain characteristics are obtained, including: Based on the vibration characteristics and temperature characteristics, the time domain characteristics are obtained; Performing Fourier transform on the vibration characteristics and temperature characteristics to obtain transformed characteristics, and obtaining frequency domain characteristics based on the transformed characteristics; Wavelet transform is used to process the vibration characteristics and temperature characteristics, and the time-frequency domain characteristics are obtained based on the vibration characteristics and temperature characteristics after wavelet transform.

5. The temperature-vibration data processing and monitoring system based on device vibration signal changes according to claim 3, characterized in that: Perform entropy separation on the high-dimensional vibration feature vector to obtain the optimal entropy dimension. Based on the optimal entropy dimension, the vibration characteristics are obtained, including: Calculate the information entropy of each high-dimensional vibration eigenvector and the mutual information between any two high-dimensional vibration eigenvectors; Construct an entropy separation matrix based on information entropy and mutual information; Based on the entropy separation matrix, the optimal entropy dimension is selected; The high-dimensional vibration features are extracted according to the optimal entropy dimension to obtain the vibration features.

6. The temperature-vibration data processing and monitoring system based on device vibration signal changes according to claim 3, characterized in that: The variational modal decomposition is optimized based on a snake optimization algorithm; the preprocessed multimodal signal is decomposed based on the optimized variational modal decomposition to obtain a multimodal decomposition signal, including: Initialize the intrinsic mode function and corresponding center frequency of the preprocessed multimodal signal; A fitness function is defined based on the intrinsic mode function, and the parameters of the fitness function are updated by the snake optimization algorithm to obtain an updated fitness function. Variational mode decomposition is performed based on the updated fitness function to obtain an optimized intrinsic mode function and the corresponding optimized center frequency; The optimized intrinsic mode function and the corresponding optimized center frequency are iteratively updated until the preset iteration termination condition is reached to obtain a multimodal decomposition signal.

7. The temperature-vibration data processing and monitoring system based on device vibration signal changes according to claim 1, characterized in that: Determine whether the health status assessment results trigger the early warning mechanism, including: Pre-constructing a health status rating table, wherein the health status levels in the health status rating table include excellent, good, fair, and critical; matching the health status level in the health status level table according to the health status assessment result; Determine whether the matched health status level triggers the early warning mechanism. When the matched health status level is excellent or good, the early warning mechanism is not triggered; when the matched health status level is general or critical, the early warning mechanism is triggered.

8. A temperature vibration data processing and monitoring method based on device vibration signal changes, characterized in that: include: Collecting a multimodal signal, the multimodal signal including a vibration signal of a wired device and a vibration signal and a temperature signal of a wireless device, and preprocessing the multimodal signal to obtain a preprocessed multimodal signal; Perform feature extraction on the preprocessed multimodal signal to obtain time domain features, frequency domain features and time-frequency domain features; Perform data fusion on time domain features, frequency domain features and time-frequency domain features to obtain fusion features; Obtain historical vibration signals, historical temperature signals, and historical fault data, train a health assessment model based on the historical vibration signals, historical temperature signals, and historical fault data, obtain a trained health assessment model, input the fusion features into the trained health assessment model, obtain a health status assessment result, determine whether the health status assessment result triggers an early warning mechanism, and if so, generate early warning information.

9. A computer device, characterized in that: It includes a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the temperature vibration data processing and monitoring method based on the change of the device vibration signal as described in claim 8.

10. A computer program product comprising a computer program or instructions, characterized in that When executed by a computer, the computer program or the instructions implements the temperature-vibration data processing and monitoring method based on device vibration signal changes as claimed in claim 8.

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