Anti-interference vibration sensor signal processing method and related system

Abstract

The invention provides an anti-interference vibration sensor signal processing method and a related system, and belongs to the technical field of vibration sensors. Comprising the following steps that a vibration sensor collects a mechanical vibration signal and converts the mechanical signal into an electric signal; amplifying, filtering and linearizing the electric signal, and converting the processed electric signal into a digital signal; performing filtering, denoising and signal segmentation operation on the digital signal to provide a high-quality digital signal; calculating feature parameters of the high-quality digital signals through a time domain feature extraction model and a frequency domain feature extraction model; analyzing and processing the characteristic parameters to obtain a fault diagnosis result; and displaying the characteristic parameters and the fault diagnosis result through a graphical interface, and storing the characteristic parameters and the fault diagnosis result. By filtering the electric signal and the digital signal, residual noise and interference are eliminated, the accuracy of characteristic parameters is ensured, and the accuracy of signal analysis and fault diagnosis detection is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vibration sensors, and in particular relates to an anti-interference vibration sensor signal processing method and a related system. Background Art

[0002] In modern industrial production, the condition monitoring and fault diagnosis of mechanical equipment are of great significance to ensure production continuity and equipment safety. Traditional mechanical equipment monitoring methods often rely on manual experience, which is not only inefficient but also difficult to ensure accuracy. With the development of sensor technology and signal processing technology, vibration sensors and signal processing systems are increasingly used in mechanical equipment condition monitoring and fault diagnosis.

[0003] Although the existing vibration sensors and signal processing systems can monitor the equipment status to a certain extent, the existing vibration sensor systems often contain a lot of noise in the collected vibration signals due to external environmental interference and the working noise of the equipment itself during the signal acquisition stage. These noises have an adverse effect on subsequent signal analysis and fault diagnosis. At the same time, the accuracy and real-time performance of fault diagnosis in the existing signal processing systems still need to be improved, and it is difficult to meet the needs of fast and accurate diagnosis in industrial production. Summary of the invention

[0004] The purpose of the present invention is to overcome the problem that the vibration signal collected by the existing vibration sensor system during the signal acquisition stage contains a large amount of noise, thereby causing adverse effects on subsequent signal analysis and fault diagnosis, and to provide an anti-interference vibration sensor signal processing method and related system.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides an anti-interference vibration sensor signal processing method, comprising the following steps: Collect mechanical vibration signals in real time and convert them into electrical signals; Amplify, filter and linearize the electrical signal, and convert the processed electrical signal into a digital signal; Filter, remove noise and segment digital signals to obtain high-quality digital signals; Extract characteristic parameters of high-quality digital signals through time domain feature extraction models and frequency domain feature extraction models; Analyze and process characteristic parameters and diagnose faults to obtain fault diagnosis results; Characteristic parameters and fault diagnosis results are displayed and stored through a graphical interface.

[0006] The method of converting the electrical signal into a digital signal is as follows: The electrical signal is amplified, and the high-frequency noise and low-frequency drift in the electrical signal are removed by using a filter to retain the vibration information within the required frequency range; the nonlinear distortion of the electrical signal is corrected through the linearization processing of the signal conditioning circuit; the processed electrical signal is converted into a digital signal through the ADC.

[0007] The method of operating the digital signal to obtain a high-quality digital signal is as follows: Digital filtering technology is used to filter digital signals, and the main components of the signal are extracted through signal denoising algorithms to reduce the impact of noise; time series analysis of the signal is performed, including autocorrelation function analysis, to reveal the periodicity and trend of the signal, and the continuous signal is divided into data segments of equal length to obtain high-quality digital signals.

[0008] The time domain feature extraction model uses the root mean square algorithm to analyze the high-quality digital signal in the time domain, obtains the time domain signal waveform, and extracts the frequency and amplitude of the waveform as feature parameters.

[0009] The specific steps of using the RMS algorithm are: First, obtain a high-quality digital signal sequence, denoted as x[n], where n=0,1,...,N−1, and N is the total number of high-quality digital signal points; Then calculate the RMS value, the calculation formula is as follows: ;in It is the root mean square value, which reflects the energy of high-quality digital signals; It is the total number of points of high-quality digital signal; It is The signal value at each sampling point.

[0010] The frequency domain feature extraction model converts the time domain signal into the frequency domain signal through Fourier transform and extracts the feature parameters from it. Specifically, it includes the following steps: Data preparation: Obtaining time domain signal sequence ; Perform FFT: Perform fast Fourier transform to obtain frequency domain signal ; Extract frequency domain features: It is represented as a spectrum graph, and the required frequency, amplitude and phase are extracted from the spectrum graph as characteristic parameters.

[0011] The calculation formula of fast Fourier transform is as follows: ;in is the frequency domain signal Points, ; is the time domain signal points; is the total number of points of the signal; is an imaginary unit; is the kernel function of Fourier transform; Each point in the frequency domain The corresponding frequency Calculated by the following formula: ;in is the sampling period; Frequency domain signal The magnitude of can be obtained by calculating its absolute value: ;That and They are The real and imaginary parts of Frequency domain signal The phase of can be obtained by calculating the inverse tangent of its imaginary part and its real part: ;in It is the frequency domain signal The phase angle of a point indicates the phase offset of the frequency component relative to the reference point; Is the inverse tangent function, which is used to calculate the angle corresponding to the ratio of two values.

[0012] In a second aspect, the present invention provides an anti-interference vibration sensor signal processing system, including a vibration sensor module, a signal preprocessing module, a feature extraction module, a signal processing and analysis module, and a result display and storage module; The vibration sensor module is used to collect mechanical vibration signals and convert them into electrical signals; The signal conditioning module is used to perform preliminary amplification, filtering and linearization processing on the electrical signal, and convert the processed electrical signal into a digital signal; The signal preprocessing module is used to filter, remove noise and perform signal segmentation operations on the digital signal to obtain a high-quality digital signal; The feature extraction module includes a time domain feature extraction model and a frequency domain feature extraction model, which are used to extract feature parameters of high-quality digital signals; The signal processing and analysis module includes a signal identification module and a fault diagnosis module, which are used to analyze and process characteristic parameters and perform fault diagnosis to obtain fault diagnosis results; The result display and storage module is used to display the characteristic parameters and fault diagnosis results to the user through a graphic interface and store them.

[0013] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of an anti-interference vibration sensor signal processing method when executing the computer program.

[0014] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of an anti-interference vibration sensor signal processing method.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an anti-interference vibration sensor signal processing method and related system, including the following steps: the vibration sensor collects mechanical vibration signals in real time and converts the mechanical signals into electrical signals; amplifies, filters and linearizes the electrical signals and converts the processed electrical signals into digital signals; filters the digital signals, denoises and performs signal segmentation operations to obtain high-quality digital signals; extracts characteristic parameters of high-quality digital signals through time domain feature extraction models and frequency domain feature extraction models; analyzes and processes the characteristic parameters and performs fault diagnosis to obtain fault diagnosis results; displays the characteristic parameters and fault diagnosis results through a graphical interface and stores them. By collecting vibration signals in real time, converting the vibration signals into electrical signals, filtering the electrical signals and digital signals, eliminating external environmental noise and residual noise interference, the accuracy of extracted characteristic parameters is ensured, and the real-time and accuracy of signal analysis and fault diagnosis detection are improved.

[0016] Furthermore, the feature extraction module includes two models: time domain feature extraction and frequency domain feature extraction. The time domain feature extraction model provides the system with the basic statistical characteristics of the signal by calculating the root mean square value, mean, variance and other parameters of the signal. These parameters help to identify the strength and stability of the signal, which is particularly important for early fault detection. The frequency domain feature extraction model converts the time domain signal into a frequency domain signal through the FFT algorithm, and extracts characteristic parameters such as frequency, amplitude and phase from it. These parameters can reveal the frequency component and energy distribution of the signal, and have a significant effect on identifying faults caused by specific frequencies. Through the collaborative work of these two models, the feature extraction module can effectively filter out fault-related information from complex vibration signals, reduce the complexity of subsequent signal processing and analysis, and improve the accuracy and efficiency of fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the present invention; Figure 2 It is the overall system block diagram of the present invention; Figure 3 This is a system schematic diagram of Example 3. DETAILED DESCRIPTION

[0018] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0019] Embodiment 1: like Figure 1 As shown, a method for processing anti-interference vibration sensor signals comprises the following steps: S1: collect mechanical vibration signals in real time and convert them into electrical signals; S2: amplify, filter and linearize the electrical signal, and convert the processed electrical signal into a digital signal; S3: De-noise, filter again and segment the digital signal to obtain a high-quality digital signal; S4: Extract characteristic parameters of high-quality digital signals through time domain feature extraction model and frequency domain feature extraction model; S5: Analyze and process the characteristic parameters and perform fault diagnosis to obtain a fault diagnosis result; S6: Display characteristic parameters and fault diagnosis results through a graphical interface and store them.

[0020] Specifically, in S1, first, select a suitable vibration sensor according to the monitoring requirements and environmental conditions. When installing the sensor, ensure that it is in close contact with the surface of the object to be measured to reduce signal attenuation and noise interference. When vibration occurs, the sensitive elements inside the sensor will produce slight deformation or relative movement due to the vibration, thereby causing changes in voltage or current. These changes are the electrical manifestations of the vibration signal, which are very weak and need to be amplified by high-precision circuits. During the entire process, the sensor module is also responsible for maintaining a stable measurement environment, including temperature compensation and anti-interference measures to ensure the accuracy and reliability of the signal.

[0021] Specifically, in S2, first, the electrical signal output by the sensor is preliminarily amplified by a low-noise amplifier to enhance the signal strength and reduce the quantization noise in subsequent processing; then, a filter is used to remove high-frequency noise and low-frequency drift in the signal to retain the vibration information within the frequency range of interest; the cutoff frequency and order of the filter need to be designed according to the specific vibration characteristics and application requirements; then, the nonlinear distortion of the sensor output signal is corrected through the linearization processing of the signal conditioning circuit to ensure the authenticity of the signal; finally, the processed electrical signal is converted into a digital signal through the ADC. This step requires the selection of an appropriate sampling rate and resolution to avoid aliasing and maintain the quality of the signal; the entire signal conditioning process also needs to consider the stability, anti-interference ability and power consumption of the circuit.

[0022] Specifically, in S3, first, digital filtering technology is used to filter the digital signal to eliminate the quantization noise and interference caused by environmental factors that may be introduced during the ADC conversion process. This usually involves designing IIR or FIR filters and selecting appropriate filter parameters; secondly, through signal denoising algorithms such as singular value decomposition (SVD) or principal component analysis (PCA), the main components of the signal are further extracted to reduce the impact of noise; then, time series analysis of digital signals, such as autocorrelation function analysis, is performed to reveal the periodicity and trend of the signal. Signal segmentation and synchronization is another important task of the preprocessing module, which divides the continuous signal into data segments of equal length to facilitate subsequent feature extraction and classification processing.

[0023] Specifically, in S4, the time domain feature extraction model uses a root mean square value algorithm to analyze the high-quality digital signal in the time domain, obtains a time domain signal waveform, and extracts the frequency and amplitude of the waveform as feature parameters; First, obtain a high-quality digital signal sequence, denoted as x[n], where n=0,1,...,N−1, N is the total number of signal points, and then calculate the RMS value: The formula for calculating the RMS value is as follows: ;in It is the root mean square value, which reflects the energy of high-quality digital signals; It is the total number of points of high-quality digital signal; It is The signal value at each sampling point.

[0024] The frequency domain feature extraction model converts the time domain signal into the frequency domain signal through Fourier transform and extracts the feature parameters from it. Specifically, it includes the following steps: Data preparation: Obtaining time domain signal sequence .

[0025] Perform FFT: Perform fast Fourier transform to obtain frequency domain signal .

[0026] Extract frequency domain features: It is represented as a spectrum graph, and the required frequency, amplitude and phase are extracted from the spectrum graph as characteristic parameters; The calculation formula of fast Fourier transform is as follows: ;in is the frequency domain signal Points, . is the time domain signal points. is the total number of points of the signal; Is an imaginary unit. is the kernel function of the Fourier transform.

[0027] Each point in the frequency domain The corresponding frequency Calculated by the following formula: ;in is the sampling period; Frequency domain signal The magnitude of can be obtained by calculating its absolute value: ;That and They are The real and imaginary parts of Frequency domain signal The phase of can be obtained by calculating the inverse tangent of its imaginary part and its real part: ;in It is the frequency domain signal The phase angle of a point indicates the phase offset of the frequency component relative to the reference point (usually 0 Hz). Is the inverse tangent function, which is used to calculate the angle corresponding to the ratio of two values.

[0028] Specifically, in S5, the signal recognition module is responsible for classifying and identifying the characteristic parameters, with the purpose of distinguishing normal signals from fault signals, so as to realize the monitoring of equipment status and fault warning. This module usually adopts machine learning or pattern recognition technology, such as support vector machine (SVM), artificial neural network (ANN), decision tree or deep learning model. During operation, the signal recognition module first needs to go through the training phase, use the data set with known labels to train the recognition model and determine the model parameters. After the training is completed, the module will enter the recognition phase, receive the characteristic parameters output by the feature extraction module, and classify and judge them through the trained model. For example, when a set of characteristic parameters is detected, the signal recognition module will compare it with the characteristic parameters under normal working state. If a significant difference is found, it may indicate that there is a fault. The module will output a fault warning, indicating the specific fault type and possible location.

[0029] The fault diagnosis module determines the type and severity of the fault based on the abnormal characteristic parameters, combined with domain knowledge and fault mode library, and obtains the fault diagnosis result.

[0030] Specifically, in S6, the result display and storage module is the terminal of the signal processing system, which is responsible for presenting the processed data to the user in an intuitive and persistent manner. The detailed operation method and content of this module are as follows: First, through the graphical user interface (GUI) design, the time domain waveform, frequency domain spectrogram and fault diagnosis results of the vibration signal are visually displayed to the user. These charts can be customized according to the user's needs, such as adjusting the display range, scaling and color mapping. At the same time, the GUI also provides interactive functions, allowing users to adjust parameters, select display modes and perform simple data analysis in real time. In terms of data storage, the module uses a database management system (DBMS) to organize and store processed data. The design of the database includes defining the data table structure, indexes and relationships to ensure fast retrieval and efficient management of data. The stored data includes but is not limited to the original signal, feature vectors, classification results and system logs. In addition, the module also provides a data export function, which supports exporting data in multiple formats (such as CSV, XML, HDF5) for data exchange and further analysis. The design of the entire result display and storage module aims to provide a user-friendly operation interface and a reliable data management solution to enhance the practicality and user experience of the system.

[0031] Embodiment 2: like Figure 1 As shown, an anti-interference vibration sensor signal processing system includes: The vibration sensor module is used to collect mechanical vibration signals and convert the collected mechanical vibration signals into electrical signals, and its output is directly connected to the signal conditioning circuit module; The signal conditioning circuit module is used to perform preliminary amplification, filtering and linearization processing on the electrical signal, convert the processed electrical signal into a digital signal, and output it to the signal preprocessing module; The signal preprocessing module is used to further optimize and prepare the signal in the digital signal processing chain, and provide high-quality digital signals for the feature extraction module through denoising, re-filtering and signal segmentation operations; The feature extraction module includes a time domain feature extraction model and a frequency domain feature extraction model, which are used to extract feature parameters of high-quality digital signals. These feature parameters are passed to the signal processing and analysis module. The signal processing and analysis module includes a signal recognition module and a fault diagnosis module, which is used to analyze and process characteristic parameters and diagnose faults to obtain fault diagnosis results; The result display and storage module is used to display characteristic parameters and fault diagnosis results to users through a graphical user interface and store them.

[0032] Embodiment 3: like Figure 3As shown, the present invention also provides an electronic device 100 for an anti-interference vibration sensor signal processing method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0033] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the anti-interference vibration sensor signal processing method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0034] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0035] The memory 101 in the electronic device 100 stores a plurality of instructions to implement an anti-interference vibration sensor signal processing method, and the processor 102 can execute the plurality of instructions to implement: Collect mechanical vibration signals and convert them into electrical signals; Amplify, filter and linearize the electrical signal, and convert the processed electrical signal into a digital signal; Filter, remove noise and segment digital signals to obtain high-quality digital signals; Extract characteristic parameters of high-quality digital signals through time domain feature extraction models and frequency domain feature extraction models; Analyze and process characteristic parameters and diagnose faults to obtain fault diagnosis results; Characteristic parameters and fault diagnosis results are displayed and stored through a graphical interface.

[0036] Embodiment 4: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0037] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0038] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0039] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0040] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for processing anti-interference vibration sensor signals, characterized in that: The steps include: Collect mechanical vibration signals in real time and convert them into electrical signals; Amplify, filter and linearize the electrical signal, and convert the processed electrical signal into a digital signal; Filter, remove noise and segment digital signals to obtain high-quality digital signals; Extract characteristic parameters of high-quality digital signals through time domain feature extraction models and frequency domain feature extraction models; Analyze and process characteristic parameters and diagnose faults to obtain fault diagnosis results; Characteristic parameters and fault diagnosis results are displayed and stored through a graphical interface.

2. The anti-interference vibration sensor signal processing method according to claim 1 is characterized in that: The method of converting the electrical signal into a digital signal is as follows: The electrical signal is amplified, and the high-frequency noise and low-frequency drift in the electrical signal are removed by using a filter to retain the vibration information within the required frequency range; the nonlinear distortion of the electrical signal is corrected through the linearization processing of the signal conditioning circuit; the processed electrical signal is converted into a digital signal through the ADC.

3. The anti-interference vibration sensor signal processing method according to claim 1 is characterized in that: The method of operating the digital signal to obtain a high-quality digital signal is as follows: Digital filtering technology is used to filter digital signals, and the main components of the signal are extracted through signal denoising algorithms to reduce the impact of noise; time series analysis of the signal is performed, including autocorrelation function analysis, to reveal the periodicity and trend of the signal, and the continuous signal is divided into data segments of equal length to obtain high-quality digital signals.

4. The anti-interference vibration sensor signal processing method according to claim 1 is characterized in that: The time domain feature extraction model uses the root mean square algorithm to analyze the high-quality digital signal in the time domain, obtains the time domain signal waveform, and extracts the frequency and amplitude of the waveform as feature parameters.

5. The anti-interference vibration sensor signal processing method according to claim 4 is characterized in that: The specific steps of using the RMS algorithm are: First, obtain a high-quality digital signal sequence, denoted as x[n], where n=0,1,...,N−1, and N is the total number of high-quality digital signal points; Then calculate the RMS value, the calculation formula is as follows: ;in It is the root mean square value, which reflects the energy of high-quality digital signals; It is the total number of points of high-quality digital signal; It is The signal value at each sampling point.

6. The anti-interference vibration sensor signal processing method according to claim 5 is characterized in that: The frequency domain feature extraction model converts the time domain signal into the frequency domain signal through Fourier transform and extracts the feature parameters from it. Specifically, it includes the following steps: Data preparation: Obtaining time domain signal sequence ; Perform FFT: Perform fast Fourier transform to obtain frequency domain signal ; Extract frequency domain features: It is represented as a spectrum graph, and the required frequency, amplitude and phase are extracted from the spectrum graph as characteristic parameters.

7. The anti-interference vibration sensor signal processing method according to claim 6 is characterized in that: The calculation formula of fast Fourier transform is as follows: ;in is the frequency domain signal Points, ; is the time domain signal points; is the total number of points of the signal; is an imaginary unit; is the kernel function of Fourier transform; Each point in the frequency domain The corresponding frequency Calculated by the following formula: ;in is the sampling period; Frequency domain signal The magnitude of can be obtained by calculating its absolute value: ;That and They are The real and imaginary parts of Frequency domain signal The phase of can be obtained by calculating the inverse tangent of its imaginary part and its real part: ;in It is the frequency domain signal The phase angle of a point indicates the phase offset of the frequency component relative to the reference point; Is the inverse tangent function, which is used to calculate the angle corresponding to the ratio of two values.

8. An anti-interference vibration sensor signal processing system, characterized in that: It includes a vibration sensor module, a signal preprocessing module, a feature extraction module, a signal processing and analysis module, and a result display and storage module; The vibration sensor module is used to collect mechanical vibration signals and convert them into electrical signals; The signal conditioning module is used to perform preliminary amplification, filtering and linearization processing on the electrical signal, and convert the processed electrical signal into a digital signal; The signal preprocessing module is used to filter, remove noise and perform signal segmentation operations on the digital signal to obtain a high-quality digital signal; The feature extraction module includes a time domain feature extraction model and a frequency domain feature extraction model, which are used to extract feature parameters of high-quality digital signals; The signal processing and analysis module includes a signal identification module and a fault diagnosis module, which are used to analyze and process characteristic parameters and perform fault diagnosis to obtain fault diagnosis results; The result display and storage module is used to display the characteristic parameters and fault diagnosis results to the user through a graphic interface and store them.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the anti-interference vibration sensor signal processing method described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the anti-interference vibration sensor signal processing method described in any one of claims 1 to 7 are implemented.

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