An abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor

Abstract

The application discloses an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor, and relates to the technical field of vibration monitoring.The method comprises the following steps: attaching the flexible acoustic vibration sensor to the key part of the GIS equipment of a transformer substation, capturing the vibration signal and converting it into an electric signal in real time; selecting a suitable wavelet mother function, comparing the amplitude error and the residual percentage through wavelet transform, and selecting the optimal wavelet mother function; performing wavelet packet transform on the vibration data, calculating the frequency band energy, and selecting the frequency band with prominent abnormal energy; performing empirical mode decomposition and Hilbert transform on the abnormal frequency band signal, forming a characteristic vector, and comparing the characteristic vector with a preset characteristic library to identify the equipment state.The application realizes accurate diagnosis, discovers potential faults in a timely manner, improves the accuracy and real-time performance of fault diagnosis, and reduces the equipment downtime and maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of vibration monitoring technology, and in particular to an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor. Background Technology

[0002] Gas-insulated switchgear (GIS) equipment is one of the core components in building new power systems, playing a vital role in protecting and controlling circuits and ensuring the normal operation of the power system. However, many early-commissioned GIS devices are facing increasingly serious insulation aging problems, with frequent equipment failures. This not only jeopardizes the reliable operation of the entire power grid system but also causes considerable inconvenience and significant economic losses to social production and daily life. Furthermore, GIS equipment has numerous electrical components and a complex structure, with a wide variety of internal insulation defects. The formation and evolution mechanisms of these defects are currently unclear, and the numerous state variables representing insulation faults exhibit uncertainty and ambiguity in their information. Therefore, it is difficult to reveal the abnormal vibration mechanisms of substation GIS equipment using existing defect evolution laws and redundant state variables.

[0003] Traditional GIS equipment abnormal vibration monitoring technologies mostly utilize rigid mechanical devices that cannot conform to irregularly shaped equipment. Since GIS equipment often has curved surfaces, sensors cannot fit snugly against these surfaces, resulting in poor sensor signal quality, low sensitivity, and a low signal-to-noise ratio. This is one reason why traditional technologies struggle to acquire early abnormal vibration signals in the time and frequency domains, and also prevent real-time monitoring at the early stages of a fault. Flexible acoustic vibration sensors, typically made of flexible polymer materials or composite materials, possess excellent flexibility and high sensitivity, enabling them to conform to complex curved surfaces and detect minute vibration signals. Furthermore, flexible acoustic vibration sensors are usually designed based on piezoelectric materials or capacitive structures. Piezoelectric materials generate electrical signals when subjected to external mechanical vibrations (such as acceleration or pressure changes), while capacitive structures sense vibrations through changes in capacitance. Therefore, when GIS equipment vibrates, flexible sensors can convert the mechanical energy of the vibration into electrical signals or capacitance changes, capturing information such as the vibration frequency and amplitude. Their high sensitivity allows for real-time capture of minute vibrations, facilitating further abnormal signal decomposition and analysis.

[0004] To achieve accurate vibration monitoring of GIS equipment, it is necessary to accurately acquire vibration signals and then effectively and fully process and analyze them to obtain characteristic quantities that reflect fault information. Wavelet packet transform can effectively separate noise from characteristic information, decomposing the vibration signal into different time-scale spaces in the form of wavelet coefficients. By calculating the energy changes of the signal at each scale component, the relationship between frequency band energy and operating status can be established to diagnose whether there are abnormalities in the equipment. However, since wavelet packets only use the energy distribution and changes of each frequency band as feature vectors, it is insufficient, ignoring the frequency information of the iron core vibration. Moreover, wavelet packets are a time-scale analysis method, resulting in low frequency resolution. In the literature on GIS vibration monitoring using wavelet packets or wavelet theory, the selection of the wavelet mother function has been neglected, which may lead to distortion in the wavelet decomposition results. Compared with wavelet theory, the Hilbert-Huang Transform (HHT) can not only obtain the energy characteristics of the frequency band of interest, but also obtain the local instantaneous frequency of the signal, effectively locating the characteristic frequencies in the vibration signal, with higher frequency resolution and more accurate spectral structure. However, the inherent limitations of decomposition can significantly impact the effectiveness of subsequent Hilbert transforms. For example, decomposition may generate spurious eigenmode functions or introduce mode aliasing problems, both of which can affect the performance of diagnostic equipment and lead to false or missed fault reports.

[0005] To address the aforementioned issues, a time-scale-frequency analysis method based on wavelet packet transform and Hilbert-Huang transform is proposed for analyzing and diagnosing equipment status. The aim is to adapt to signal variations under complex operating conditions of GIS equipment, improve the accuracy and reliability of signal analysis, and achieve real-time monitoring of abnormal vibrations in GIS equipment. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the aforementioned existing problems, the present invention is proposed.

[0008] Therefore, the present invention provides an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor, which can solve the problems mentioned in the background art.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0010] In a first aspect, the present invention provides an abnormal vibration monitoring method based on a flexible acoustic vibration sensor, which includes attaching a flexible acoustic vibration sensor to a key part of a substation GIS equipment, capturing vibration signals during equipment operation through the flexible acoustic vibration sensor, and converting the vibration signals into electrical signals in real time to generate vibration data.

[0011] Select a suitable wavelet mother function, perform wavelet transform on the vibration data under different wavelet mother functions, compare the obtained amplitude error and residual percentage, and select the wavelet mother function with the smallest error and the lowest residual percentage.

[0012] The vibration data is subjected to wavelet packet transform using the wavelet mother function to decompose the vibration signal into multiple frequency bands. The energy of each frequency band is calculated, and the frequency band with prominent abnormal energy is selected as the object of analysis.

[0013] Empirical mode decomposition (EMD) is performed on the frequency band signal with prominent abnormal energy to separate multiple intrinsic mode functions (IMFs). A Hilbert transform is then performed on each IMF to obtain instantaneous frequency and amplitude information.

[0014] A feature vector is generated, and the current status of the device is identified by comparing the feature vector with a preset database of normal and fault status features.

[0015] As a preferred embodiment of the abnormal vibration monitoring method based on a flexible acoustic vibration sensor described in this invention, the generated vibration data includes:

[0016] The flexible acoustic and vibration sensor captures vibration signals generated during equipment operation, and the vibration signals include equipment operating status information.

[0017] The vibration signal is converted into an electrical signal. This process is accomplished by the piezoelectric material or capacitive structure inside the flexible acoustic vibration sensor. The piezoelectric material generates an electric charge signal when subjected to mechanical vibration, and the capacitive structure senses the vibration through changes in capacitance.

[0018] The vibration data includes the frequency and amplitude of the vibration, which are used for subsequent signal processing and analysis.

[0019] As a preferred embodiment of the abnormal vibration monitoring method based on a flexible acoustic vibration sensor described in this invention, the wavelet mother function with the smallest selection error and the lowest residual percentage includes:

[0020] Choose a suitable generating function to minimize the error in signal decomposition and reconstruction, and calculate the amplitude error E.

[0021]

[0022] Where, x iThis represents the amplitude of the original signal at the i-th sampling point. The value represents the amplitude of the reconstructed signal at the i-th sampling point, N represents the number of sampling points of the signal, and R represents the residual percentage. P The calculation formula is as follows:

[0023]

[0024] Where x represents the vector of the original signal, Let ||·|| represent the vector of the reconstructed signal, and ||·|| represent the L2 norm.

[0025] As a preferred embodiment of the abnormal vibration monitoring method based on a flexible acoustic vibration sensor described in this invention, wherein: the calculation of the energy of each frequency band includes,

[0026] Wavelet packet transform is used to decompose a signal into multiple frequency bands, resulting in signals with different frequency components. If the number of decomposition levels is j, then 2j will be generated. j The formula for calculating wavelet packet coefficients for each frequency band is as follows:

[0027]

[0028] Among them, W j，k (n) represents the discrete wavelet packet coefficients of the j-th layer and k-th frequency band, where n represents the sampling point number, ψ j，k (t) represents the wavelet mother function of the k-th frequency band in the j-th layer;

[0029] The energy of each frequency band is calculated using the following formula:

[0030]

[0031] Among them, E j，k This represents the coefficient of the k-th frequency band in the j-th layer, where n represents the sampling point number and N represents the number of sampling points in the signal.

[0032] Based on the energy level, select the frequency band with prominent abnormal energy as the object of further analysis.

[0033] As a preferred embodiment of the abnormal vibration monitoring method based on a flexible acoustic sensor described in this invention, the step of performing a Hilbert transform on each of the intrinsic mode functions includes:

[0034] The selected frequency band signal is decomposed into a series of IMFs through empirical mode decomposition:

[0035]

[0036] Where, x j，k (t) represents the signal frequency band after wavelet packet decomposition and filtering, C i(t) represents the i-th IMF in the Empirical Mode Decomposition, m represents the total number of IMF components, and r m (t) represents the residual term after empirical mode decomposition;

[0037] The analytic signal of each IMF component is obtained by performing a Hilbert transform, and the calculation formula is as follows:

[0038]

[0039] in, Indicate C i The Hilbert transform of (t) is calculated using the following formula:

[0040]

[0041] Among them, Z i (t) represents the IMF component C i The analytic signal (t) is used to calculate the instantaneous frequency and instantaneous amplitude. PV represents the principal value integral, which is used to calculate the singular point integral in the Hilbert transform.

[0042] As a preferred embodiment of the abnormal vibration monitoring method based on a flexible acoustic vibration sensor described in this invention, the acquisition of instantaneous frequency and instantaneous amplitude information includes,

[0043] The formula for calculating instantaneous frequency is as follows:

[0044]

[0045] Where, ω i (t) represents the frequency of the IMF component at time t, arg(Z) i (t) represents Z i The phase angle of (t);

[0046] The formula for calculating the instantaneous amplitude is as follows:

[0047]

[0048] Among them, A i (t) represents the vibration intensity of the IMF component at time t.

[0049] As a preferred embodiment of the abnormal vibration monitoring method based on a flexible acoustic vibration sensor described in this invention, the formation of the feature vector includes the following steps:

[0050] Plot a three-dimensional Hilbert plot of time-frequency-amplitude to show the time-frequency characteristics of the signal using the time, frequency, and amplitude axes;

[0051] Key information is extracted from the 3D Hilbert plot, including statistical characteristics of major fault frequencies, average instantaneous frequencies, and instantaneous amplitudes, to form a feature vector, i.e.,

[0052]

[0053] in, σ represents the average instantaneous frequency. f The standard deviation of the instantaneous frequency is represented by E, and the total energy is represented by A. max This represents the maximum instantaneous amplitude.

[0054] Secondly, the present invention provides an abnormal vibration monitoring system based on a flexible acoustic vibration sensor, which includes: a data acquisition module, a function selection module, a frequency band analysis module, and an anomaly identification module;

[0055] The data acquisition module is used to attach flexible acoustic and vibration sensors to key parts of the substation GIS equipment, capture vibration signals during equipment operation through the flexible acoustic and vibration sensors, and convert the vibration signals into electrical signals in real time to generate vibration data.

[0056] The function selection module is used to select a suitable wavelet mother function, perform wavelet transform on the vibration data under different wavelet mother functions, compare the obtained amplitude error and residual percentage, and select the wavelet mother function with the smallest error and the lowest residual percentage.

[0057] The frequency band analysis module is used to perform wavelet packet transform on the vibration data using the wavelet mother function, decompose the vibration signal into multiple frequency band levels, calculate the energy of each frequency band, and select the frequency band with prominent abnormal energy as the object of analysis.

[0058] The anomaly identification module is used to perform empirical mode decomposition on the frequency band signal with prominent abnormal energy, separate multiple intrinsic mode functions, perform Hilbert transform on each intrinsic mode function to obtain instantaneous frequency and instantaneous amplitude information, form a feature vector, and identify the current state of the device by comparing the feature vector with a preset normal and fault state feature library.

[0059] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an abnormal vibration monitoring method based on a flexible acoustic vibration sensor.

[0060] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements the steps of an abnormal vibration monitoring method based on a flexible acoustic vibration sensor.

[0061] Compared with existing technologies, the advantages of this invention are as follows: By attaching flexible acoustic and vibration sensors to key parts of substation GIS equipment, a close fit to complex curved structures is achieved, improving the sensor's installation flexibility and signal acquisition accuracy, and enhancing the ability to capture minute vibration signals and improve signal quality. By selecting appropriate wavelet mother functions, the highest accuracy of signal decomposition and reconstruction is ensured, reducing signal distortion and improving the reliability and accuracy of subsequent analysis. Wavelet packet transform is used to decompose the vibration signal into multiple frequency bands, and frequency bands with prominent anomalous energy are selected, improving the resolution of signal analysis and the efficiency of fault detection. By performing empirical mode decomposition and Hilbert transform on the signals in the frequency bands with prominent anomalous energy, instantaneous frequency and amplitude information are obtained, forming feature vectors. These vectors are then compared with a preset database of normal and fault state features, enabling accurate diagnosis of equipment status, timely detection of potential faults, improved accuracy and real-time performance of fault diagnosis, and facilitating early maintenance measures, reducing equipment downtime and repair costs. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 A flowchart illustrating an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor, provided as an embodiment of the present invention;

[0064] Figure 2 This is an internal structural diagram of a computer device for an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor, provided as an embodiment of the present invention. Detailed Implementation

[0065] To make the above-mentioned objects, features, and advantages of the present invention more readily understood, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0066] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0067] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0068] Example 1, referring to Figures 1-2 This is the first embodiment of the present invention, which provides an abnormal vibration monitoring method based on a flexible acoustic vibration sensor, comprising:

[0069] This application provides a method for effectively solving the problems mentioned above. The following will describe in detail how to implement the abnormal vibration monitoring method based on a flexible acoustic sensor using multiple embodiments.

[0070] Figure 1 A flowchart of an abnormal vibration monitoring method and system based on a flexible acoustic vibration sensor is shown, including:

[0071] S1: Attach flexible acoustic and vibration sensors to key parts of the GIS equipment in the substation. The sensors capture vibration signals during equipment operation and convert the vibration signals into electrical signals in real time to generate vibration data.

[0072] Furthermore, flexible acoustic and vibration sensors are attached to key parts of the substation GIS equipment to ensure close contact between the sensors and the equipment surface.

[0073] The flexible acoustic and vibration sensor captures vibration signals generated during equipment operation, and the vibration signals include equipment operating status information.

[0074] The vibration signal is converted into an electrical signal in real time. This process is accomplished by the piezoelectric material or capacitive structure inside the flexible acoustic vibration sensor. The piezoelectric material generates an electric charge signal when subjected to mechanical vibration, and the capacitive structure senses the vibration through changes in capacitance.

[0075] Vibration data is generated, including but not limited to information such as vibration frequency and amplitude, for subsequent signal processing and analysis.

[0076] S2: Select a suitable wavelet mother function. Based on the amplitude error and residual percentage obtained after wavelet transforming the vibration data under different wavelet mother functions, select the wavelet mother function with the smallest error and the lowest residual percentage.

[0077] Furthermore, the wavelet mother function with the smallest selection error and the lowest residual percentage includes:

[0078] Choose a suitable generating function to minimize the error in signal decomposition and reconstruction. Calculate the amplitude error E.

[0079]

[0080] Where, x i This represents the amplitude of the original signal at the i-th sampling point. The value represents the amplitude of the reconstructed signal at the i-th sampling point, N represents the number of sampling points of the signal, and R represents the residual percentage. P The calculation formula is as follows:

[0081]

[0082] Where x represents the vector of the original signal, Let ||·|| represent the vector of the reconstructed signal, and ||·|| represent the L2 norm.

[0083] S3: Use the wavelet mother function to perform wavelet packet transform on the vibration data, decompose the vibration signal into multiple frequency bands, calculate the energy of each frequency band, and select the frequency band with prominent abnormal energy as the object of analysis.

[0084] Furthermore, the steps for calculating the energy of each frequency band include,

[0085] Wavelet packet transform is used to decompose a signal into multiple frequency bands, resulting in signals with different frequency components. If the number of decomposition levels is j, then 2j will be generated. j The formula for calculating wavelet packet coefficients for each frequency band is as follows:

[0086]

[0087] Among them, W j，k (n) represents the discrete wavelet packet coefficients of the j-th layer and k-th frequency band, where n represents the sampling point number, ψ j，k (t) represents the wavelet mother function of the k-th frequency band in the j-th layer;

[0088] The energy of each frequency band is calculated using the following formula:

[0089]

[0090] Among them, E j，k This represents the coefficient of the k-th frequency band in the j-th layer, where n represents the sampling point number and N represents the number of sampling points in the signal.

[0091] Based on the energy level, select the frequency band with prominent abnormal energy as the object of further analysis.

[0092] S4: Perform empirical mode decomposition on the frequency band signal with prominent abnormal energy to separate multiple intrinsic mode functions. Perform Hilbert transform on each intrinsic mode function to obtain instantaneous frequency and instantaneous amplitude information, form a feature vector, and compare the feature vector with a preset normal and fault state feature library to identify the current state of the device.

[0093] Furthermore, obtaining instantaneous frequency and instantaneous amplitude information includes the following steps:

[0094] The selected frequency band signal is decomposed into a series of IMFs through empirical mode decomposition:

[0095]

[0096] Where, x j，k (t) represents the signal frequency band after wavelet packet decomposition and filtering, C i (t) represents the i-th IMF in the Empirical Mode Decomposition, m represents the total number of IMF components, and r m (t) represents the residual term after empirical mode decomposition;

[0097] The analytic signal of each IMF component is obtained by performing a Hilbert transform, and the calculation formula is as follows:

[0098]

[0099] in, Indicate C i The Hilbert transform of (t) is calculated using the following formula:

[0100]

[0101] Among them, Z i (t) represents the IMF component C i The analytic signal (t) is used to calculate the instantaneous frequency and instantaneous amplitude. PV represents the principal value integral, which is used to calculate the singular point integral in the Hilbert transform.

[0102] The formula for calculating instantaneous frequency is as follows:

[0103]

[0104] Where, ω i (t) represents the frequency of the IMF component at time t, arg(Z) i (t) represents Z i The phase angle of (t);

[0105] The formula for calculating the instantaneous amplitude is as follows:

[0106]

[0107] Among them, A i (t) represents the vibration intensity of the IMF component at time t.

[0108] Furthermore, forming a feature vector includes the following steps:

[0109] Plot a three-dimensional Hilbert plot of time-frequency-amplitude to show the time-frequency characteristics of the signal using the time, frequency, and amplitude axes;

[0110] Key information is extracted from the 3D Hilbert plot, including statistical characteristics of major fault frequencies, average instantaneous frequencies, and instantaneous amplitudes, to form a feature vector, i.e.,

[0111]

[0112] in, σ represents the average instantaneous frequency. f The standard deviation of the instantaneous frequency is represented by E, and the total energy is represented by A. max This represents the maximum instantaneous amplitude.

[0113] Furthermore, this embodiment also provides an abnormal vibration monitoring system based on a flexible acoustic vibration sensor, including: a data acquisition module, a function selection module, a frequency band analysis module, and an anomaly identification module;

[0114] The data acquisition module is used to attach flexible acoustic and vibration sensors to key parts of the substation GIS equipment. The flexible acoustic and vibration sensors capture vibration signals during equipment operation and convert the vibration signals into electrical signals in real time to generate vibration data.

[0115] The function selection module is used to select a suitable wavelet mother function, perform wavelet transform on the vibration data under different wavelet mother functions, compare the obtained amplitude error and residual percentage, and select the wavelet mother function with the smallest error and the lowest residual percentage.

[0116] The frequency band analysis module is used to perform wavelet packet transform on the vibration data using the wavelet mother function, decompose the vibration signal into multiple frequency band levels, calculate the energy of each frequency band, and select the frequency band with prominent abnormal energy as the object of analysis.

[0117] The anomaly identification module performs empirical mode decomposition on the frequency band signal with prominent abnormal energy, separating multiple intrinsic mode functions (IMFs). A Hilbert transform is then performed on each IMF to obtain instantaneous frequency and amplitude information, forming a feature vector. This feature vector is then compared with a preset database of normal and fault state features to identify the current device status.

[0118] This embodiment also provides a computer device, which may be a terminal, and its internal structure diagram may be as follows. Figure 2 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements an abnormal vibration monitoring method based on a flexible acoustic vibration sensor. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0119] This embodiment also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the following steps: attaching a flexible acoustic vibration sensor to a key part of the substation GIS equipment, capturing vibration signals during equipment operation through the flexible acoustic vibration sensor, and converting the vibration signals into electrical signals in real time to generate vibration data.

[0120] Select a suitable wavelet mother function, perform wavelet transform on the vibration data under different wavelet mother functions, compare the obtained amplitude error and residual percentage, and select the wavelet mother function with the smallest error and the lowest residual percentage.

[0121] The vibration data is subjected to wavelet packet transform using the wavelet mother function to decompose the vibration signal into multiple frequency bands. The energy of each frequency band is calculated, and the frequency band with prominent abnormal energy is selected as the object of analysis.

[0122] Empirical mode decomposition is performed on the frequency band signal with prominent abnormal energy to separate multiple intrinsic mode functions. Hilbert transform is performed on each intrinsic mode function to obtain instantaneous frequency and instantaneous amplitude information, forming a feature vector. The feature vector is then compared with a preset normal and fault state feature library to identify the current state of the device.

[0123] Example 2, refer to Figure 1 - Figure 2This is the second embodiment of the present invention, which provides an abnormal vibration monitoring method based on a flexible acoustic vibration sensor. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0124] To verify the effectiveness and innovation of the abnormal vibration monitoring method for substation GIS equipment based on flexible acoustic vibration sensors, the following detailed experimental preparation and implementation process was carried out:

[0125] Select a typical substation GIS equipment as the test object. This equipment includes key components such as circuit breakers, disconnect switches, and current transformers.

[0126] Prepare multiple flexible acoustic vibration sensors. These sensors are made of flexible polymer materials, which have good flexibility and high sensitivity, and can conform to complex curved surfaces and sense subtle vibration signals.

[0127] Flexible acoustic and vibration sensors are attached to key parts of the substation GIS equipment (such as circuit breaker contacts, disconnector switch connections, and current transformer housings) to ensure close contact between the sensors and the equipment surface.

[0128] Connect the sensor to the data acquisition system and set the sampling frequency to 10kHz to ensure that high-frequency vibration signals can be captured.

[0129] Vibration signal acquisition: The substation GIS equipment is activated, and flexible acoustic and vibration sensors are used to capture vibration signals generated during equipment operation in real time. These vibration signals are then converted into electrical signals to generate vibration data. The vibration data includes the frequency and amplitude information of the vibration.

[0130] Choosing a wavelet mother function: Using MATLAB software, perform wavelet transform on the acquired vibration data, select different wavelet mother functions (such as db4, sym8, coiflet, etc.), and calculate the amplitude error and residual percentage for each wavelet mother function. By comparing these error indices, select the wavelet mother function with the smallest error and the lowest residual percentage.

[0131] Wavelet packet transform: The vibration data is subjected to wavelet packet transform using a selected wavelet mother function, decomposing the signal into multiple frequency bands and calculating the energy of each band. Based on the energy magnitude, the frequency bands with prominent anomalous energy are selected as the objects of further analysis.

[0132] Empirical Mode Decomposition (EMD) and Hilbert Transform: Empirical mode decomposition is performed on frequency band signals with prominent anomalous energy to separate multiple intrinsic mode functions (IMFs). A Hilbert transform is then performed on each IMF to obtain instantaneous frequency and amplitude information, forming an eigenvector.

[0133] Feature vector comparison: The feature vectors are compared with a preset feature library of normal and fault states to identify the current status of the device. The preset feature library includes feature vectors of normal states and various common fault states.

[0134] To verify the effectiveness of the method, experiments were conducted under normal equipment operation and simulated fault conditions, and the corresponding vibration data and analysis results were recorded.

[0135] Record test data using tables, including parameters such as the name of different test objects, vibration frequency, vibration amplitude, amplitude error, residual percentage, abnormal energy band, instantaneous frequency, and instantaneous amplitude.

[0136] Table 1. Vibration Signal Acquisition and Wavelet Mother Function Selection Data Table

[0137]

[0138] Table 2. Wavelet Packet Transform and Eigenvector Analysis Data Table

[0139]

[0140]

[0141] As shown in Table 1, there are significant differences in vibration frequency and amplitude under normal and fault conditions. The vibration frequency is lower and the vibration amplitude is smaller under normal conditions, while the vibration frequency is higher and the vibration amplitude is larger under fault conditions.

[0142] By selecting different wavelet mother functions for wavelet transform, the impact of different mother functions on the error of signal decomposition and reconstruction can be observed. Three wavelet mother functions, db4, sym8, and coiflet, were selected. The results show that db4 and sym8 have lower amplitude errors and residual percentages under normal conditions, while coiflet performs better under fault conditions. This indicates that selecting an appropriate wavelet mother function is crucial to the accuracy of signal processing.

[0143] As shown in Table 2, decomposing the signal into multiple frequency bands using wavelet packet transform and selecting frequency bands with prominent abnormal energy for further analysis can effectively identify the sources of abnormal vibration in the equipment. For example, the abnormal energy frequency band under normal conditions is concentrated in the 500-600Hz range, while the abnormal energy frequency band under fault conditions is concentrated in the 600-800Hz range.

[0144] Empirical mode decomposition and Hilbert transform are performed on the frequency band signal with prominent abnormal energy to obtain instantaneous frequency and instantaneous amplitude information, forming a feature vector. By comparing the feature vectors under different states, it can be clearly seen that the instantaneous frequency and instantaneous amplitude under fault conditions are significantly higher than those under normal conditions, and the total energy and maximum instantaneous amplitude are also significantly increased.

[0145] In particular, the vibration characteristics of the equipment can be further quantified by calculating the average instantaneous frequency and the standard deviation of the instantaneous frequency. The average instantaneous frequency and standard deviation under fault conditions are both higher than those under normal conditions, indicating that the vibration of the equipment is more severe and unstable under fault conditions.

[0146] By using flexible acoustic and vibration sensors, it is possible to closely conform to the complex curved surfaces of substation GIS equipment, thereby improving the accuracy and reliability of signal acquisition. Experimental data shows significant differences in vibration frequency and amplitude between normal and fault conditions, indicating that the flexible acoustic and vibration sensors can effectively capture minute vibration signals from the equipment.

[0147] By comparing the performance of different wavelet mother functions in terms of amplitude error and residual percentage, selecting the optimal wavelet mother function can significantly improve the accuracy of signal decomposition and reconstruction. Experimental data show that db4 and sym8 perform well under normal conditions, while coiflet performs better under fault conditions, indicating that selecting a suitable wavelet mother function is crucial to the accuracy of signal processing.

[0148] By decomposing the signal into multiple frequency bands using wavelet packet transform and selecting bands with prominent abnormal energy for further analysis, the source of abnormal vibration in equipment can be effectively identified. Experimental data shows that there are significant differences in the abnormal energy frequency bands between normal and fault states, indicating that this method can improve the resolution and accuracy of fault detection.

[0149] By performing empirical mode decomposition and Hilbert transform on frequency band signals with prominent abnormal energy, instantaneous frequency and amplitude information are obtained to form a feature vector. This vector is then compared with a pre-set feature library of normal and fault states, enabling accurate diagnosis of equipment status. Experimental data shows that the instantaneous frequency and amplitude in the fault state are significantly higher than in the normal state, and the total energy and maximum instantaneous amplitude also increase significantly, indicating that this method can effectively identify equipment fault states.

[0150] In summary, this invention provides a method for monitoring abnormal vibrations in substation GIS equipment based on flexible acoustic and vibration sensors. By optimizing sensor installation and signal processing methods, the accuracy and reliability of fault detection are significantly improved. This method not only effectively captures minute vibration signals from the equipment but also achieves accurate diagnosis of equipment status through scientific signal processing methods, providing strong technical support for the maintenance and management of substation GIS equipment.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0152] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0153] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0156] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0157] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of monitoring abnormal vibration based on a flexible acoustic vibration sensor, characterized in that: The method comprises the following steps: attaching a flexible acoustic vibration sensor to a key part of a GIS device in a substation, capturing a vibration signal during operation of the device by the flexible acoustic vibration sensor, and converting the vibration signal into an electric signal in real time to generate vibration data; selecting a suitable wavelet mother function, performing wavelet transform on the vibration data under different wavelet mother functions, comparing the amplitude error and residual percentage obtained, and selecting the wavelet mother function with the smallest error and the lowest residual percentage; performing wavelet packet transform on the vibration data using the wavelet mother function, decomposing the vibration signal into multiple frequency band levels, calculating the energy of each frequency band, and selecting a frequency band with abnormal energy as an analysis object; performing empirical mode decomposition on the frequency band signal with abnormal energy, separating multiple intrinsic mode functions, performing Hilbert transform on each intrinsic mode function to obtain instantaneous frequency and instantaneous amplitude information, forming a feature vector, and comparing the feature vector with a pre-set normal and fault state feature library to identify the state of the current device; the calculation of the energy of each frequency band comprises, The wavelet packet transform is used to decompose the signal into multiple frequency band levels to obtain signals of different frequency components. If the number of decomposition levels is , then frequency bands are generated, and the wavelet packet coefficient calculation formula is as follows: ； wherein, denotes the layer the discrete wavelet packet coefficient of the denotes the number of the sampling point, denotes the layer the wavelet mother function of the the calculation of the energy of each frequency band is as follows: ； wherein, denotes the layer the coefficient of the denotes the number of denotes the number of samples of the signal; selecting a frequency band with abnormal energy according to the energy size as a further analysis object; the Hilbert transform performed on each intrinsic mode function comprises, decomposing the selected frequency band signal into a series of IMFs through empirical mode decomposition: ； wherein, represents the signal band filtered by wavelet packet decomposition, represents the first IMF of empirical mode decomposition, represents the total number of IMF components, represents the residual term after empirical mode decomposition; performing Hilbert transform on each IMF component to obtain its analytic signal, and the calculation formula is as follows: ； wherein represents the Hilbert transform of the function f(t), the calculation formula is as follows: ； wherein represents the analytic signal of the IMF component for the calculation of instantaneous frequency and instantaneous amplitude, represents the principal value integral for the calculation of the singularity integral in the Hilbert transform. 2.The flexible acoustic vibration sensor based abnormal vibration monitoring method of claim 1, wherein: the generation of vibration data comprises, capturing the vibration signal generated during the operation of the device by the flexible acoustic vibration sensor, and the vibration signal comprises device operation state information; converting the vibration signal into an electric signal, which is completed by a piezoelectric material or a capacitive structure inside the flexible acoustic vibration sensor, the piezoelectric material generates an electric charge signal when subjected to mechanical vibration, and the capacitive structure senses vibration through capacitance change; the vibration data comprises the frequency and amplitude of vibration, which are used for subsequent signal processing and analysis. 3.The abnormal vibration monitoring method based on the flexible acoustic vibration sensor according to claim 2, wherein: the selection of the wavelet mother function with the smallest error and the lowest residual percentage comprises, The appropriate mother function is selected to minimize the error of signal decomposition and reconstruction, amplitude error The calculation formula is as follows: ； wherein, represents the amplitude of the original signal at the th sampling point, represents the amplitude of the reconstructed signal at the th sampling point, represents the number of sampling points of the signal, the residual percentage The calculation formula is as follows: ； wherein denotes a vector of the original signal, denotes a vector of the reconstructed signal, denotes a two-norm.

4. The abnormal vibration monitoring method based on a flexible acoustic vibration sensor according to claim 3, characterized in that: the acquisition of the instantaneous frequency and instantaneous amplitude information comprises, the calculation formula of the instantaneous frequency is as follows: ； wherein, denotes the frequency of the IMF component at time denotes the phase angle of​ the calculation formula of the instantaneous amplitude is as follows: ； wherein, represents the intensity of vibration of the IMF component at time t.

5. The flexible acoustic vibration sensor based abnormal vibration monitoring method of claim 4, wherein: the formation of the feature vector comprises the following steps, drawing a time-frequency-amplitude three-dimensional Hilbert graph to display the time-frequency characteristics of the signal through the time, frequency and amplitude axes; extracting key information from the three-dimensional Hilbert graph, including the main fault frequency, the average instantaneous frequency, and the statistical characteristics of the instantaneous amplitude, to form a feature vector, i.e., ； wherein, represents the average instantaneous frequency, represents the standard deviation of the instantaneous frequency, represents the total energy, represents the maximum instantaneous amplitude.

6. A flexible acoustic vibration sensor based abnormal vibration monitoring system based on the flexible acoustic vibration sensor based abnormal vibration monitoring method according to any one of claims 1 to 5, characterized in that: comprises a data acquisition module, a function selection module, a frequency band analysis module, and an abnormality identification module; the data acquisition module is used to attach a flexible acoustic vibration sensor to a key part of a GIS device in a substation, capture a vibration signal during operation of the device by the flexible acoustic vibration sensor, and convert the vibration signal into an electric signal in real time to generate vibration data; The function selection module is configured to select a suitable wavelet mother function, perform wavelet transform on the vibration data under different wavelet mother functions, compare the amplitude error and residual percentage obtained, and select a wavelet mother function with the minimum error and the lowest residual percentage; The frequency band analysis module is configured to perform wavelet packet transform on the vibration data using the wavelet mother function, decompose the vibration signal into multiple frequency band levels, calculate the energy of each frequency band, and select a frequency band with abnormal energy as an analysis object; The abnormality identification module is configured to perform empirical mode decomposition on the frequency band signal with abnormal energy, separate multiple intrinsic mode functions, perform Hilbert transform on each intrinsic mode function to obtain instantaneous frequency and instantaneous amplitude information, form a feature vector, and compare the feature vector with a pre-set normal and fault state feature library to identify the state of the current device. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the abnormal vibration monitoring method based on the flexible acoustic vibration sensor according to any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the abnormal vibration monitoring method based on the flexible acoustic vibration sensor according to any one of claims 1-5.

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