Method for generating regular audio detection signal based on equipment vibration characteristic value

Abstract

The invention discloses a method for generating a regular audio detection signal based on an equipment vibration characteristic value, and relates to the technical field of equipment monitoring, and the method comprises the steps: obtaining the detection data of an equipment vibration sensor through a wireless system; performing signal processing on the data of the vibration sensor, and extracting characteristic values; analyzing the extracted characteristic values, and obtaining a difference rule of the vibration signals; transplanting the difference rule of the vibration signal to an audio signal in a frequency range which can be identified by human ears, and generating a regular audio signal; a sound player of the mobile terminal of the EF03 system is used for listening to audio signals; an audio file is sent to a vibration measurement system through an EF03 system and bound to a related sensor, and the audio file is listened at any time in the vibration measurement system according to needs. According to the scheme, the vibration state of the equipment can be monitored in real time, the operation condition of the equipment is visually reflected through the audio signal, and an operator can quickly recognize equipment abnormity.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, and specifically relates to a method for generating regular audio detection signals based on equipment vibration characteristic values. Background Art

[0002] At present, vibration measurement technologies for key equipment and systems based on wired transmission have been widely implemented at multiple nuclear power sites, realizing vibration data monitoring and diagnosis of important equipment and systems through wired data transmission. However, there are still problems with wired transmission vibration measurement, such as complex systems, large workloads, low intelligence, poor flexibility, reliability, and maintainability. Some measurement positions are inaccessible or pose a risk of harm to the human body, resulting in incomplete evaluation of the state of equipment and systems.

[0003] With the development of wireless sensing technology, equipment vibration monitoring based on wireless sensing has been increasingly applied in civil industrial sites. Due to the special environment and layout limitations of nuclear power plants, there is no intelligent vibration measurement system based on wireless transmission in the nuclear power industry, especially the development of miniaturized and lightweight wireless vibration sensors remains to be developed.

[0004] Develop an intelligent vibration measurement and diagnosis system for key equipment and systems in nuclear power plants based on wireless transmission, realize intelligent vibration measurement, diagnosis analysis, vibration traceability and tracking of key equipment and systems in nuclear power plants in wireless mode, and solve problems such as complex vibration measurement systems, large workloads, low flexibility, reliability, and maintainability in nuclear power plants, so as to lay a foundation for realizing condition-based preventive maintenance and further realizing plant-wide intelligence.

[0005] However, for detection based on vibration sensors, special systems and tools are required to obtain sensor data, which greatly affects the convenience and popularity of obtaining information. Summary of the Invention

[0006] To solve the above technical problems and provide a method for generating regular audio detection signals based on equipment vibration characteristic values, the present technical solution solves the problem that for detection based on vibration sensors, special systems and tools are required to obtain sensor data, which greatly affects the convenience and popularity of obtaining information.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for generating regular audio detection signals based on equipment vibration characteristic values, comprising:

[0009] Obtaining detection data of an equipment vibration sensor through a wireless system;

[0010] Performing signal processing on the data of the vibration sensor to extract characteristic values;

[0011] Analyze the extracted eigenvalues to obtain the difference law of the vibration signal;

[0012] Transfer the difference law of the vibration signal to the audio signal within the frequency range recognizable by the human ear to generate a regular audio signal;

[0013] Use the sound player of the mobile terminal of the EF03 system to listen to the audio signal;

[0014] Send the audio file through the EF03 system to the vibration measurement system and bind it to the relevant sensor, and listen to the audio file at any time as needed in the vibration measurement system.

[0015] Preferably, the obtaining of the detection data of the device vibration sensor through the wireless system specifically includes:

[0016] Deploy vibration sensors at at least one monitoring point of the device to collect the vibration signal of the device, and the vibration signal includes vibration frequency and vibration amplitude;

[0017] Use wireless communication technology to transmit the collected vibration signal data of the device to the analysis terminal;

[0018] The vibration signal data of the device is stored in the form of a time series, specifically: x(t) = {x1…x i …x n}, where x i is the vibration value of the i-th sampling point, and n is the total number of sampling points.

[0019] Preferably, the signal processing of the data of the vibration sensor to extract eigenvalues specifically includes:

[0020] Perform wavelet transform on the collected vibration signal data of the device to remove noise and obtain the noise-reduced vibration signal data;

[0021] Based on the noise-reduced vibration signal data, extract the time-domain features and frequency-domain features of the data, where the time-domain features at least include the mean value and variance, and the frequency-domain features include spectral features;

[0022] The calculation formula for the mean value is:

[0023]

[0024] The calculation formula for the variance is:

[0025]

[0026] where is the mean value, and s is the variance;

[0027] The specific spectral features are as follows:

[0028]

[0029] Among them, X(k) is the spectral feature and k is the frequency index.

[0030] Preferably, the method for removing noise from the collected vibration signal data of the device by using wavelet transform specifically includes:

[0031] Performing wavelet transform on the vibration signal data of the device by using the wavelet transform formula to obtain wavelet coefficients;

[0032] Selecting an appropriate threshold λ according to the noise level and signal characteristics;

[0033] Performing threshold processing on the wavelet coefficients;

[0034] Performing inverse wavelet transform on the wavelet coefficients after threshold processing by using the inverse wavelet transform formula to obtain the vibration signal data with reduced noise;

[0035] Among them, the wavelet transform formula is:

[0036]

[0037] Among them, W(t) is the wavelet coefficient, a is the scale parameter, b is the translation parameter, and φ() is the wavelet basis function;

[0038] The inverse wavelet transform formula is the inverse function of the wavelet transform formula;

[0039] The threshold processing of the wavelet coefficients is performed according to the following formula:

[0040]

[0041] Among them, y is the wavelet coefficient after threshold processing and λ is the threshold.

[0042] Preferably, the method for analyzing the extracted eigenvalues to obtain the difference law of the vibration signal specifically includes:

[0043] Using the K-means algorithm to cluster the collected time-domain features and frequency-domain features to obtain several clusters;

[0044] Adopting t-test to analyze the differences between different clusters and performing cluster reconstruction based on the differences between different clusters to obtain several clusters with difference laws;

[0045] Based on deep learning, a classification model is constructed. The classification model takes the vibration signal features corresponding to each cluster with a difference law as the input and the state of the device as the output.

[0046] Preferably, the t-test is used to analyze the differences between different clusters, and based on the differences between different clusters, cluster reconstruction is performed to obtain several clusters with differential rules, specifically including:

[0047] Based on the t-test formula, calculate the difference index between adjacent clusters, and determine whether the difference index between adjacent clusters is greater than the difference threshold. If so, do not make a response. If not, merge the adjacent clusters;

[0048] The specific t-test formula is:

[0049]

[0050] t is the difference index between adjacent clusters, are the means of the samples of adjacent clusters respectively, s1 and s2 are the standard deviations of the samples of adjacent clusters respectively, and n1 and n2 are the sample numbers of adjacent clusters respectively.

[0051] Preferably, the specific steps of transplanting the difference rule of the vibration signal into an audio signal within the frequency range recognizable by the human ear to generate a regular audio signal include:

[0052] Using the frequency mapping formula, linearly map the main frequency components of several vibration signals with differential rules into the audible range of the human ear;

[0053] Use the inverse Fourier transform to generate the audio waveform;

[0054] Save the audio waveform as a WAV or MP3 format file.

[0055] Preferably, the specific steps of using the frequency mapping formula to linearly map the main frequency components of several vibration signals with differential rules into the audible range of the human ear include:

[0056] Obtain the main frequencies of several vibration signals with differential rules, and respectively take the maximum and minimum values among them;

[0057] Then the specific frequency mapping formula is:

[0058]

[0059] In the formula, f audio is the mapped frequency, f vibration is the frequency before mapping, d is the scaling coefficient, the value range of d is 0.5 - 1, f audio has a value range of 20Hz - 20kHz, f max is the maximum value among the main frequencies of several vibration signals with differential rules, f min is the minimum value among the main frequencies of several vibration signals with differential rules.

[0060] Preferably, for the sound player of the mobile terminal using the EF03 system, the specific steps for listening to the audio signal include:

[0061] Integrate a sound player on the mobile terminal of the EF03 system;

[0062] The sound player supports real-time playback of the generated audio file and provides functions for volume and speed adjustment;

[0063] On the mobile terminal of the EF03 system, display the audio waveform and the status of the device.

[0064] Preferably, for the step of sending the audio file through the EF03 system to the vibration measurement system and binding it to the relevant sensor, and listening to the audio file as needed in the vibration measurement system, the specific steps include:

[0065] Transmit the audio file to the vibration measurement system through the wireless communication module of the EF03 system;

[0066] Bind the audio file to the corresponding sensor data and store it in the database of the vibration measurement system;

[0067] Integrate an audio playback function in the vibration measurement system to support listening to the audio file at any time.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] The present invention uses a standard method of listening to the audio signal to obtain various signals (including alarm signals) of the vibration sensor, solving the problems of narrow application range and low monitoring efficiency caused by dedicated tools in the existing methods. At the same time, the key links of the present invention reduce the dependence on dedicated signal analysis tools. With the human ear, the alarm signal of the vibration sensor can be received through the "listening" method, making it possible for on-site workers in a large range to receive the sensor alarm signal at any time. In addition, the present invention effectively binds the sensor monitoring data to the location where the sensor is located, increasing the intuitiveness, convenience, and reliability of obtaining the monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flowchart of the method for generating regular audio detection signals based on the vibration characteristic values of the device proposed in this solution;

[0071] Figure 2 It is a flowchart of the method for obtaining the detection data of the device vibration sensor through the wireless system proposed in this solution;

[0072] Figure 3 It is a flowchart of the method for signal processing of the data of the vibration sensor and extracting characteristic values proposed in this solution;

[0073] Figure 4 It is the flowchart of the method for removing noise by using wavelet transform proposed in this solution;

[0074] Figure 5 It is the flowchart of the method for obtaining the difference law of vibration signals proposed in this solution;

[0075] Figure 6 It is the flowchart of the method for obtaining several clusters with difference laws proposed in this solution;

[0076] Figure 7 It is the flowchart of the method for generating regular audio signals proposed in this solution. Specific implementation manners

[0077] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments in the following description are only examples, and those skilled in the art can think of other obvious variations.

[0078] Refer to Figure 1 As shown, a method for generating regular audio detection signals based on the vibration characteristic values of a device includes:

[0079] Obtain the detection data of the device vibration sensor through a wireless system;

[0080] Perform signal processing on the data of the vibration sensor to extract characteristic values;

[0081] Analyze the extracted characteristic values to obtain the difference law of the vibration signals;

[0082] Transplant the difference law of the vibration signals to the audio signals within the frequency range recognizable by the human ear to generate regular audio signals;

[0083] Use the sound player of the mobile terminal of the EF03 system to listen to the audio signals;

[0084] Send the audio file through the EF03 system to the vibration measurement system and bind it to the relevant sensors, and listen to the audio file at any time as needed in the vibration measurement system.

[0085] The present invention obtains the detection data of the device vibration sensor through a wireless system, performs signal processing on the data of the vibration sensor to extract characteristic values, analyzes the extracted characteristic values to obtain the difference law of the vibration signals, and transplants these laws to the audio signals within the frequency range recognizable by the human ear to generate regular audio signals. This method can monitor the vibration state of the device in real time and intuitively reflect the operation of the device through the audio signals, facilitating the operator to quickly identify device abnormalities.

[0086] Refer to Figure 2As shown, obtaining the detection data of the device vibration sensor through a wireless system specifically includes:

[0087] Deploy vibration sensors at at least one monitoring point of the device to collect the vibration signals of the device. The vibration signals include vibration frequency and vibration amplitude;

[0088] Use wireless communication technology to transmit the collected vibration signal data of the device to the analysis terminal;

[0089] The vibration signal data of the device is stored in the form of a time series, specifically: x(t) = {x1…x i …x n}, where x i is the vibration value at the i-th sampling point, and n is the total number of sampling points.

[0090] Refer to Figure 3 As shown, signal processing of the data of the vibration sensor to extract feature values specifically includes:

[0091] Perform wavelet transform on the collected vibration signal data of the device to remove noise and obtain denoised vibration signal data;

[0092] Based on the denoised vibration signal data, extract the time-domain features and frequency-domain features of the data. The time-domain features at least include mean and variance, and the frequency-domain features include spectral features;

[0093] The calculation formula for the mean is:

[0094]

[0095] The calculation formula for the variance is:

[0096]

[0097] where, is the mean, and s is the variance;

[0098] The spectral feature is specifically:

[0099]

[0100] where, X(k) is the spectral feature and k is the frequency index.

[0101] Refer to Figure 4 As shown, performing wavelet transform on the collected vibration signal data of the device to remove noise specifically includes:

[0102] Use the wavelet transform formula to perform wavelet transform on the vibration signal data of the device to obtain wavelet coefficients;

[0103] Select an appropriate threshold λ according to the noise level and signal characteristics;

[0104] Perform threshold processing on the wavelet coefficients;

[0105] For the wavelet coefficients after threshold processing, use the inverse wavelet transform formula to obtain the denoised vibration signal data;

[0106] Among them, the wavelet transform formula is:

[0107]

[0108] Among them, W(t) is the wavelet coefficient, a is the scale parameter, b is the translation parameter, and φ() is the wavelet basis function;

[0109] The inverse wavelet transform formula is the inverse function of the wavelet transform formula;

[0110] The threshold processing of the wavelet coefficients is carried out according to the following formula:

[0111]

[0112] Among them, y is the wavelet coefficient after threshold processing, and λ is the threshold.

[0113] Due to its characteristics of multi-resolution analysis, local feature extraction, and strong denoising ability, and since vibration signals are usually non-stationary signals (the frequency components change with time), wavelet transform can capture the local characteristics of the signals, and thus can effectively detect the effective features in the vibration signals, extract the multi-scale features of the vibration signals for subsequent analysis.

[0114] Refer to Figure 5 As shown, analyzing the extracted eigenvalues to obtain the difference law of the vibration signals specifically includes:

[0115] Use the K-means algorithm to cluster the collected time-domain features and frequency-domain features to obtain several clusters;

[0116] Adopt t-test to analyze the differences between different clusters, and based on the differences between different clusters, perform cluster reconstruction to obtain several clusters with difference laws;

[0117] Based on deep learning, construct a classification model. The classification model takes the vibration signal features corresponding to each cluster with a difference law as input and the state of the device as output.

[0118] Refer to Figure 6 As shown, adopting t-test to analyze the differences between different clusters, and based on the differences between different clusters, perform cluster reconstruction to obtain several clusters with difference laws specifically includes:

[0119] Calculate the difference index between adjacent clusters based on the t-test formula, and determine whether the difference index between adjacent clusters is greater than the difference threshold. If so, do not respond. If not, merge the adjacent clusters;

[0120] The specific t-test formula is as follows:

[0121]

[0122] t is the difference index between adjacent clusters, are the means of the samples in adjacent clusters respectively, s1 and s2 are the standard deviations of the samples in adjacent clusters respectively, and n1 and n2 are the numbers of samples in adjacent clusters respectively.

[0123] The present invention uses the K-means algorithm to perform clustering analysis on the collected vibration signal eigenvalues. By calculating the Euclidean distance between the sample points and the clustering centers, the vibration signals with similar characteristics are divided into the same cluster, so as to realize the preliminary classification of the vibration signals and the extraction of rules; on this basis, further use the t-test to statistically analyze the differences between different clusters, calculate the ratio of the mean difference to the standard deviation of adjacent clusters, quantify the inter-cluster difference index, and combine the preset difference threshold to judge whether there are significant differences between clusters, so as to optimize and reconstruct the initial clustering results, ensure that the vibration signals within each cluster have high consistency while there are significant differences between different clusters; through this way of combining clustering and difference analysis, not only can the common rules in the vibration signals be effectively identified, but also the difference characteristics under different vibration states can be accurately extracted, thus significantly enhancing the reliability and accuracy of the vibration signal rule extraction, and providing more accurate data support for subsequent equipment state classification and fault diagnosis.

[0124] Refer to Figure 7 As shown, transplanting the difference rule of the vibration signal into the audio signal within the frequency range that the human ear can recognize, generating a regular audio signal specifically includes:

[0125] Using the frequency mapping formula, linearly map the main frequency components of several vibration signals with difference rules into the audible range of the human ear;

[0126] Use the inverse Fourier transform to generate the audio waveform;

[0127] Save the audio waveform as a WAV or MP3 format file.

[0128] Using the frequency mapping formula to linearly map the main frequency components of several vibration signals with difference rules into the audible range of the human ear specifically includes:

[0129] Obtain the main frequencies of several vibration signals with difference rules, and respectively take the maximum and minimum values among them;

[0130] The frequency mapping formula is specifically as follows:

[0131]

[0132] In the formula, f audio is the frequency after mapping, f vibration is the frequency before mapping, d is the scaling factor, the value range of d is 0.5 - 1, and the value range of f audio is 20Hz - 20kHz. f max is the maximum value among the main frequencies of several vibration signals with differential laws, and f min is the minimum value among the main frequencies of several vibration signals with differential laws.

[0133] The present invention linearly maps the main frequency components of the vibration signal to the audible range of the human ear (20Hz - 20kHz) through frequency mapping technology. Specifically, a scaling factor and an offset are used to perform a linear transformation on the original vibration frequency to ensure that the frequency after mapping can not only retain the core features of the vibration signal but also adapt to the auditory perception range of the human ear. On this basis, the frequency components after mapping are converted into a time-domain waveform by combining the inverse Fourier transform to generate an audio waveform corresponding to the characteristics of the vibration signal, so that the vibration signal that originally needed to be identified by professional equipment and analysis tools can be intuitively presented in audio form. This audio processing method can not only convert complex vibration signals into easily understandable auditory information, thus significantly reducing the technical threshold of equipment condition monitoring and fault diagnosis, providing an efficient and intuitive vibration signal analysis means for operators, and further enhancing the practicality and convenience of equipment condition monitoring.

[0134] Among them, since the closer the frequency is to the limit region (20 Hz or 20 kHz) of the audible range of the human ear, the significantly reduced sensitivity and resolution of the human ear to frequency changes. To overcome this limitation, a scaling coefficient is specifically introduced in the frequency mapping process in this solution. By dynamically adjusting the magnitude of the scaling coefficient, while ensuring that the main frequency components of different vibration signals with different differential laws can be accurately mapped into the audible range of the human ear, the frequency distribution is further optimized to make it as concentrated as possible in the middle frequency band with higher human ear sensitivity (usually 500 Hz - 5 kHz); in the specific implementation, the setting of the scaling coefficient comprehensively considers the main frequency range of the vibration signal, the auditory characteristics of the human ear, and the significance requirements of the differential law, and compresses or expands the original frequency distribution to the middle frequency band through linear transformation, so as to maximize the human ear's ability to identify frequency differences while retaining the core characteristics of the vibration signal; this optimized design not only effectively avoids the problem of difficult human ear recognition caused by the frequency distribution being too close to the limit region, but also further enhances the distinguishability and intuitiveness of the audio signal by concentrating the mapping range, enabling the operator to more accurately and quickly identify the differential characteristics of the device vibration state, and significantly improving the practical application effect and user experience of the vibration signal audio processing.

[0135] For the sound player of the mobile terminal using the EF03 system, listening to the audio signal specifically includes:

[0136] Integrate a sound player on the mobile terminal of the EF03 system;

[0137] The sound player supports real-time playback of the generated audio file and provides functions for adjusting the volume and speed;

[0138] On the mobile terminal of the EF03 system, display the audio waveform and the state of the device.

[0139] The above-generated audio file is played in real time by an audio player integrated with a wireless terminal of the EF03 system. Operators can intuitively identify the device vibration signals collected by vibration sensors and their corresponding device status information by listening to the pitch, rhythm, timbre changes, and specific alarm sound effects in the audio file. The audio file not only contains the regular audio waveforms generated by mapping the main frequency components of the vibration signals but also embeds alarm signals for abnormal vibration states. For example, different levels of device failures or abnormal states are distinguished in the form of high-frequency sharp sounds or low-frequency continuous sounds. Through this audio-based signal presentation method, operators do not need to rely on complex professional equipment or data analysis tools and can quickly judge the operating status and vibration characteristics of the device only by hearing, significantly reducing the technical threshold for device status monitoring. At the same time, the wireless terminal of the EF03 system supports functions such as volume adjustment, playback speed control, and audio waveform visualization display, further enhancing the identifiability and operation flexibility of audio signals, making the monitoring and alarm recognition of device vibration signals more efficient, convenient, and user-friendly, and providing an innovative solution for real-time monitoring and fault warning of devices.

[0140] Send the audio file through the EF03 system to the vibration measurement system and bind it to the relevant sensors. As needed in the vibration measurement system, listening to the audio file at any time specifically includes:

[0141] Transmit the audio file to the vibration measurement system through the wireless communication module of the EF03 system;

[0142] Bind the audio file to the corresponding sensor data and store it in the database of the vibration measurement system;

[0143] Integrate an audio playback function in the vibration measurement system to support listening to the audio file at any time.

[0144] Send the above-generated audio file through the EF03 system to the server of the test system. Through the "map upload file" function of the vibration measurement system, upload the audio file transmitted by the EF03 to the attribute folder of the corresponding vibration sensor. Start the vibration measurement system APP through the mobile terminal of the EF03 and directly play and listen to the selected audio file through the APP. Through the web page of the vibration measurement system, the same playback and listening functions can also be realized on the desktop terminal.

[0145] In summary, the advantages of the present invention are as follows: By using the standard method of listening to audio signals, the problems of narrow application range and low monitoring efficiency caused by dedicated tools in the existing methods are solved; the dependence on dedicated signal analysis tools is reduced, and the alarm signal of the vibration sensor can be received by simply listening with the human ear; the possibility for on-site staff within a large range to receive the sensor alarm signal is provided; the monitoring data is effectively bound to the location where the sensor is located, increasing the intuitiveness, convenience and reliability of obtaining the monitoring data.

[0146] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and all these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating a regular audio detection signal based on a device vibration characteristic value, characterized in that: include: Acquire detection data from equipment vibration sensors through a wireless system; Perform signal processing on the data of the vibration sensor and extract characteristic values; Analyze the extracted eigenvalues ​​to obtain the difference rules of vibration signals; Transplant the difference regularity of the vibration signal to the audio signal within the frequency range that the human ear can recognize, and generate a regular audio signal; Use the sound player of the mobile terminal of the EF03 system to listen to the audio signal; The audio file is sent to the vibration measurement system through the EF03 system and bound to the relevant sensor. The audio file can be listened to at any time in the vibration measurement system as needed.

2. A method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 1, characterized in that: The method of obtaining the detection data of the device vibration sensor through the wireless system specifically includes: Deploy a vibration sensor at at least one monitoring point of the device to collect a vibration signal of the device, wherein the vibration signal includes a vibration frequency and a vibration amplitude; Using wireless communication technology to transmit the collected vibration signal data of the equipment to the analysis terminal; The vibration signal data of the equipment is stored in the form of time series, specifically: x(t) = {x1…x i …x n }, where x i is the vibration value of the i-th sampling point, and n is the total number of sampling points.

3. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 2, characterized in that: The signal processing of the vibration sensor data to extract characteristic values ​​specifically includes: The collected vibration signal data of the equipment is subjected to wavelet transformation to remove noise and obtain noise-reduced vibration signal data; Based on the noise reduction vibration signal data, extracting the time domain features and frequency domain features of the data, wherein the time domain features at least include the mean and the variance, and the frequency domain features include the spectrum features; The calculation formula of the mean is: The calculation formula of the variance is: in, is the mean, s is the variance; The spectrum characteristics are specifically: Among them, X(k) is the spectrum feature and k is the frequency index.

4. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 3, characterized in that: The method of removing noise from the collected vibration signal data of the equipment by using wavelet transform specifically includes: The wavelet transform formula is used to transform the vibration signal data of the equipment to obtain the wavelet coefficients; Select an appropriate threshold λ based on the noise level and signal characteristics; Threshold processing is performed on the wavelet coefficients; The wavelet coefficients after threshold processing are subjected to inverse wavelet transform formula to obtain the noise-reduced vibration signal data; Wherein, the wavelet transform formula is: Among them, W(t) is the wavelet coefficient, a is the scale parameter, b is the translation parameter, is the wavelet basis function; The inverse wavelet transform formula is the inverse function of the wavelet transform formula; The threshold processing of the wavelet coefficients is performed according to the following formula: Among them, y is the wavelet coefficient after threshold processing, and λ is the threshold.

5. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 4, characterized in that: The analysis of the extracted characteristic values ​​to obtain the difference rules of the vibration signal specifically includes: Use the K-means algorithm to cluster the collected time domain features and frequency domain features to obtain several clusters; The t-test is used to analyze the differences between different clusters, and cluster reconstruction is performed based on the differences between different clusters to obtain several clusters with different regularities; Based on deep learning, a classification model is constructed. The classification model takes the vibration signal features corresponding to each cluster with a difference regularity as input and takes the status of the equipment as output.

6. A method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 5, characterized in that: The method of using the t-test to analyze the differences between different clusters and reconstructing clusters based on the differences between different clusters to obtain several clusters with different regularities specifically includes: The difference index between adjacent clusters is calculated based on the t-test formula to determine whether the difference index between adjacent clusters is greater than the difference threshold. If so, no response is made; if not, the adjacent clusters are merged. The t-test formula is specifically: t is the difference index between adjacent clusters, are the means of samples in adjacent clusters, s1 and s2 are the standard deviations of samples in adjacent clusters, and n1 and n2 are the numbers of samples in adjacent clusters.

7. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 6, characterized in that: The step of transplanting the difference regularity of the vibration signal to an audio signal within a frequency range recognizable by human ears to generate a regular audio signal specifically includes: The frequency mapping formula is used to linearly map the main frequency components of several vibration signals with different regularities to the audible range of the human ear; Generate audio waveform using inverse Fourier transform; Save audio waveforms as WAV or MP3 format files.

8. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 7, characterized in that: The frequency mapping formula is used to linearly map the main frequency components of several vibration signals with different regularities to the audible range of the human ear, specifically including: Obtain the main frequencies of several vibration signals with different regularities, and take the maximum and minimum values ​​respectively; The frequency mapping formula is as follows: In the formula, f audio is the frequency after mapping, f vibration is the frequency before mapping, d is the scaling factor, the value range of d is 0.5-1, f audio The value range is 20Hz-20kHz, f max is the maximum value among the main frequencies of several vibration signals with different regularities, f min It is the minimum value among the main frequencies of several vibration signals with different regularities.

9. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 8, characterized in that: The audio player of the mobile terminal using the EF03 system and listening to the audio signal specifically includes: Integrate a sound player on the mobile terminal of the EF03 system; The sound player supports real-time playback of generated audio files and provides volume and speed adjustment functions; The audio waveform and device status are displayed on the mobile terminal of the EF03 system.

10. The method for generating a regular audio detection signal based on a device vibration characteristic value according to claim 9, characterized in that: The audio file is sent to the vibration measurement system through the EF03 system and bound to the relevant sensor, and the audio file can be listened to at any time in the vibration measurement system as needed, specifically including: The audio file is transmitted to the vibration measurement system through the wireless communication module of the EF03 system; Bind the audio file with the corresponding sensor data and store it in the database of the vibration measurement system; Integrate audio playback function in the vibration measurement system to support listening to audio files at any time.

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