Method and apparatus for processing vibration signals of circuit breaker, device, and storage medium

Abstract

Disclosed in the present invention are a method and apparatus for processing vibration signals of a circuit breaker, a device, and a storage medium. The method comprises: inputting vibration data of a circuit breaker into a modified Laplacian of Gaussian filter, to enhance the transition between background noise and signal features in the vibration data, and output filtered vibration data; and squaring the filtered vibration data, extracting the signal features corresponding to the vibration data, and then identifying an abnormal state of the circuit breaker on the basis of the extracted signal features, wherein the generation of the modified Laplacian of Gaussian filter comprises: separately performing first-order differentiation and second-order differentiation on a Gaussian filter to obtain a corresponding LoG filter; and normalizing the LoG filter, and enabling the sum of taps of the LoG filter to be zero to obtain the modified Laplacian of Gaussian filter. By means of the present invention, filtering of environmental noise and effective distinguishing of signal features in vibration data can be achieved.

Description

A circuit breaker vibration signal processing method, device, equipment and storage medium TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical equipment fault diagnosis, and particularly relates to a circuit breaker vibration signal processing method, device, equipment and storage medium. BACKGROUND

[0002] In order to ensure the normal operation of various power equipment and meet the safety and stability of power transmission, the vibration signals of mechanical equipment or structures such as circuit breakers need to be deeply processed and analyzed. However, at present, how to extract key feature parameters reflecting the state or performance of mechanical equipment from the collected circuit breaker vibration signals is a big difficulty in the field of fault diagnosis. Because the circuit breaker vibration signal has strong nonlinearity and transience, the accuracy of mechanism action parameter extraction is affected by the installation position of the sensor and some key parameters in the algorithm, resulting in poor stability of the data feature extraction result, and the irregularity or randomness of the signal seriously affects the effective identification of the abnormal state or fault mode of the circuit breaker. SUMMARY

[0003] The present application provides a circuit breaker vibration signal processing method, device, equipment and storage medium to solve the technical problem that environmental noise, irregularity or randomness of the signal seriously affects the effective identification of the abnormal state or fault mode of the circuit breaker.

[0004] In order to solve the above technical problem, the present application provides a circuit breaker vibration signal processing method, comprising:

[0005] obtaining vibration data of a circuit breaker;

[0006] inputting the extracted vibration data into a preset modified Laplace-Gaussian filter, so that the modified Laplace-Gaussian filter strengthens the transition between background noise and signal features in the vibration data, and outputs filtered vibration data;

[0007] squares processing the filtered vibration data, extracting signal features corresponding to the vibration data from the squared processed vibration data, and then identifying the abnormal state of the circuit breaker according to the extracted signal features;

[0008] wherein the generation of the modified Laplace-Gaussian filter comprises:

[0009] first-order derivation of a preset Gaussian filter to obtain the first-order derivative of the Gaussian filter;

[0010] Secondly, the first derivative is differentiated to obtain a second derivative of the Gaussian filter, and the second derivative is taken as a LoG filter;

[0011] The LoG filter is normalized and the sum of taps of the LoG filter is zero, and the modified Laplace-Gaussian filter is obtained.

[0012] As a preferred solution, before the extracted vibration data is input into a preset modified Laplace-Gaussian filter, it further includes:

[0013] Extracting the information entropy of the vibration data;

[0014] According to the extracted information entropy, it is judged whether the dimension of the vibration data is greater than a preset threshold value;

[0015] When the dimension of the vibration data is greater than the preset threshold value, the vibration data is input into the modified Laplace-Gaussian filter, and when the dimension of the vibration data is not greater than the preset threshold value, the corresponding signal features of the vibration data are directly extracted.

[0016] As a preferred solution, before the information entropy of the vibration data is extracted, it further includes:

[0017] Pretreating the vibration data; wherein the pretreatment includes data filtering, data denoising and data cleaning.

[0018] As a preferred solution, the information entropy of the vibration data is extracted by the following formula:

[0019] Wherein, H(x) is the information entropy of the vibration data, i=1, 2, 3, …n; p(x i ) is the probability of occurrence of each discrete point in the vibration data, x is a vibration random variable, is the energy sum of the frequency band signal, χ i is the amplitude corresponding to the discrete point of the frequency band signal.

[0020] As a preferred solution, the modified Laplace-Gaussian filter is:

[0021] Wherein, G(n) is the Gaussian filter, G'(n) is the first derivative of the Gaussian filter, G"(n) is the LoG filter, LoG ～ is the normalized LoG filter, MLoG is the modified Laplace-Gaussian filter, σ is the standard deviation of the Gaussian filter, n is the Gaussian index, and C is the number of taps of the modified Laplace-Gaussian filter.

[0022] As a preferred solution, the square processed vibration data is:

[0023] wherein X n (i) is a length set of the vibration data, W(i) is an average value taken on the set X n (i) of the vibration data with a length of w. 2 (i) is a square signal filtered by using a modified Laplacian of Gaussian filter, and w is a length of the vibration data.

[0024] On the basis of the above embodiment, another embodiment of the application provides a circuit breaker vibration signal processing device, comprising: a vibration data acquisition module, a Gaussian filter module and a signal feature extraction module.

[0025] The vibration data acquisition module is configured to acquire vibration data of a circuit breaker.

[0026] The Gaussian filter module is configured to input the extracted vibration data to a preset modified Laplacian of Gaussian filter, so that the modified Laplacian of Gaussian filter strengthens the transition between background noise and signal features in the vibration data, and outputs filtered vibration data.

[0027] The signal feature extraction module is configured to square process the filtered vibration data, extract signal features corresponding to the vibration data from the square processed vibration data, and then identify an abnormal state of the circuit breaker according to the extracted signal features.

[0028] As a preferred solution, the circuit breaker vibration signal processing device further comprises an information entropy extraction module.

[0029] The information entropy extraction module is configured to extract information entropy of the vibration data, judge whether a dimension of the vibration data is greater than a preset threshold according to the extracted information entropy, input the vibration data to the modified Laplacian of Gaussian filter when the dimension of the vibration data is greater than the preset threshold, and directly extract corresponding signal features of the vibration data when the dimension of the vibration data is not greater than the preset threshold.

[0030] On the basis of the above-mentioned embodiments, a further embodiment of the application provides an electronic device, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the processing method of the circuit breaker vibration signal according to the above-mentioned embodiments of the application when executing the computer program.

[0031] On the basis of the above-mentioned embodiments, a further embodiment of the application provides a storage medium, which comprises a stored computer program, wherein the device where the storage medium is located executes the processing method of the circuit breaker vibration signal according to the above-mentioned embodiments of the application when the computer program runs.

[0032] Compared with the prior art, the embodiments of the application have the following beneficial effects:

[0033] The application provides a processing method of a circuit breaker vibration signal, vibration data of a circuit breaker is input into a preset modified Laplacian of Gaussian filter, so that the modified Laplacian of Gaussian filter strengthens the transition between background noise and signal characteristics in the vibration data, and outputs filtered vibration data; then the filtered vibration data is subjected to square processing, signal characteristics corresponding to the vibration data are extracted from the vibration data after square processing, and then the abnormal state of the circuit breaker is identified according to the extracted signal characteristics.

[0034] The modified Laplacian of Gaussian filter is obtained by respectively performing first-order derivation and second-order derivation on a preset Gaussian filter to obtain a corresponding LoG filter, then performing normalization processing on the LoG filter, and making the sum of taps of the LoG filter zero. Using the modified Laplacian of Gaussian filter in the application to filter the vibration data of the circuit breaker can make the transition between the background noise and the signal of the vibration data clearer and sharper, and further realize the filtering processing of the environmental noise of the vibration data and the effective distinction of the signal characteristics. Then, according to the extracted signal characteristics, the abnormal state of the circuit breaker is identified, which can still realize the effective identification of the abnormal state or fault mode of the circuit breaker under the influence of the irregularity or randomness degree of the environmental noise and the signal. BRIEF DESCRIPTION OF DRAWINGS

[0035] Fig. 1 is a flowchart of a processing method of a circuit breaker vibration signal according to an embodiment of the application;

[0036] Fig. 2 is a total flowchart of vibration data processing;

[0037] Fig. 3 is a schematic diagram of sensor layout positions and data sampling;

[0038] Fig. 4 is a comparison diagram of information entropy extraction of different sensor positions;

[0039] Figure 5 is a comparison chart of information entropy extraction under different working conditions;

[0040] Figure 6 is a structural schematic diagram of a processing device for a circuit breaker vibration signal according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] For the purpose of making the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative work fall within the scope of protection of the present application.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above description of drawings are intended to cover non-exclusive inclusion.

[0043] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly and specifically limited.

[0044] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive or alternative to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0045] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0046] In the description of the embodiments of the present application, the term "multiple" refers to two or more (including two), and similarly, "multiple groups" refers to two or more groups (including two groups), and "multiple pieces" refers to two or more pieces (including two pieces).

[0047] In the description of the embodiments of the present application, unless otherwise explicitly specified and limited, the technical terms "mounting", "connecting", "connecting", "fixing" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0048] Embodiment one

[0049] Please refer to Fig. 1, which is a flowchart of a circuit breaker vibration signal processing method provided by an embodiment of the present application, including the following specific steps:

[0050] S1, obtaining vibration data of a circuit breaker;

[0051] Preferably, before extracting the information entropy of the vibration data, it further includes: pre-processing the vibration data; wherein the pre-processing includes: data filtering, data noise reduction and data cleaning.

[0052] Specifically, please refer to Fig. 2, which is a general flowchart of vibration data processing. First, place vibration sensors at different positions of the circuit breaker to be tested, and then use a collection system to extract sensor vibration data. The data of the opening or closing condition, normal condition and abnormal condition of a certain opening or closing can be collected. The sensitivity of the sensor is controlled at 0.5mV / m·s -2 , the range is 10000m·s -2 , the sampling frequency is controlled at 15000Hz or above, and the voltage range of the collection device is 10V.

[0053] Further, the collected vibration data is filtered, noise reduced, and data cleaned, and the specific method can be selected according to the characteristics of the signal and the processing requirements.

[0054] S2, inputting the extracted vibration data into a preset modified Laplace Gaussian filter, so that the modified Laplace Gaussian filter strengthens the transition between background noise and signal characteristics in the vibration data, and outputs the filtered vibration data;

[0055] Wherein, the generation of the modified Laplace Gaussian filter includes:

[0056] First-order derivation is performed on a preset Gaussian filter to obtain the first-order derivative of the Gaussian filter;

[0057] Secondly, the first derivative is differentiated to obtain the second derivative of the Gaussian filter, and the second derivative is taken as a corresponding LoG filter;

[0058] The LoG filter is normalized and the sum of taps of the LoG filter is zero to obtain the modified Laplace-Gaussian filter;

[0059] Preferably, before the extracted vibration data is input into a preset modified Laplace-Gaussian filter, the method further comprises: extracting information entropy of the vibration data; judging whether the dimension of the vibration data is greater than a preset threshold according to the extracted information entropy; when the dimension of the vibration data is greater than the preset threshold, inputting the vibration data into the modified Laplace-Gaussian filter; and when the dimension of the vibration data is not greater than the preset threshold, directly extracting a corresponding signal feature of the vibration data.

[0060] Preferably, the information entropy of the vibration data is extracted by the following formula:

[0061] Wherein, H(x) is the information entropy of the vibration data, i=1, 2, 3, …n; p(x i ) is the probability of occurrence of each discrete point in the vibration data, x is a vibration random variable, is the energy sum of the frequency band signal, χ i is the amplitude corresponding to the discrete point of the frequency band signal.

[0062] Preferably, the modified Laplace-Gaussian filter is:

[0063] Wherein, G(n) is the Gaussian filter, G'(n) is the first derivative of the Gaussian filter, G"(n) is the LoG filter, LoG ～ is the normalized LoG filter, MLoG is the modified Laplace-Gaussian filter, σ is the standard deviation of the Gaussian filter, n is the Gaussian index, and C is the number of taps of the modified Laplace-Gaussian filter.

[0064] Further, according to the preprocessed vibration data, a corresponding information entropy is calculated and extracted. Extracting information entropy is a previous step of fault mode recognition. The running state of the equipment becomes complex and disordered under fault, resulting in an increase in information entropy. By monitoring the change of information entropy, the fault of the equipment can be found in time. If the information entropy is not extracted, too many vibration point data will result in an algorithm that is too complex and difficult to process.

[0065] The calculation of information entropy adopts a Shannon information entropy extraction method, which can be calculated by the following formula (1):

[0066] where i = 1, 2, 3,... n; p(x i ) represents the probability of each discrete point appearing, x represents the vibration random variable, H(x) represents the information entropy extracted from the set of vibration signals, and the calculation formula of p(x i ) is as follows:

[0067] where is the energy sum of the frequency band signal, and χ i is the amplitude corresponding to the discrete point of the frequency band signal.

[0068] Further, when the data dimension is low, the traditional feature extraction module can be effective enough to process these data and extract key features. At this time, using a more complex processing module (such as MLoG filter) can introduce unnecessary computational complexity and time. Therefore, selecting a suitable processing module according to the data dimension can improve the processing efficiency.

[0069] For high-dimensional data, traditional feature extraction methods are difficult to fully capture the complexity and internal structure of the data. At this time, using a filter specially designed for processing high-dimensional data is more helpful to extract features with representativeness and discriminability, so that these features are more valuable in subsequent analysis or model training.

[0070] On the other hand, in high-dimensional space, the sparsity of data increases, which can lead to the so-called "dimension disaster", and the distribution of data becomes very complex, making it difficult for traditional statistical methods and machine learning algorithms to work effectively. By using appropriate filters to reduce the dimension or feature conversion of high-dimensional data, the impact of dimension disaster can be reduced, and the performance of the algorithm can be improved. Therefore, according to the extracted information entropy, it is first determined whether the dimension of the vibration data is greater than a preset threshold, when the dimension of the vibration data is greater than the preset threshold, the vibration data is input to the modified Laplace-Gaussian filter, and when the dimension of the vibration data is not greater than the preset threshold, the vibration data is directly input to the feature comparison module in FIG. 2 to extract the corresponding signal features of the vibration data.

[0071] Feature comparison: based on distance metrics (such as Euclidean distance, Manhattan distance, etc.), similarity metrics (such as cosine similarity, Pearson correlation coefficient, etc.), or other comparison methods, find the difference or similarity between the current feature and the reference feature.

[0072] Distinctiveness judgment: evaluate whether the difference between the current feature and the reference feature is significant, i.e., whether it reaches a certain preset threshold or standard. If the difference is significant, it is considered that the current data has enough uniqueness or novelty and needs further processing or attention; if the difference is not significant, it enters the right MLoG information extraction.

[0073] Historical data coverage: For the features that are judged to have enough distinguishability, the module checks whether these features have been covered by historical data. The purpose of this step is to ensure that the system can continuously learn and update, avoiding the repeated processing of known information. If the current feature is not covered by historical data, it will be added to the historical database for future comparison and reference; if it has been covered, the possible location of the fault or the next step of the device can be inferred according to the past vibration historical data under certain types of faults or defects.

[0074] Further, a Laplacian of Gaussian (LoG) filter is used to detect the edge feature in the information entropy image, which can be defined as the sudden change of the vibration signal, i.e. the mutation of the background noise to the action signal P-. Therefore, by smoothing the background noise to detect the mutation of the action signal, the P- arrival time can be made clearer. The coefficient of the LoG filter can be obtained by determining the second derivative of the Gaussian filter, and a one-dimensional Gaussian filter is given in equation (3):

[0075] In the formula, σ represents the standard deviation of the Gaussian filter, and n represents the Gaussian exponent, and the first derivative thereof is shown in equation (4):

[0076] The LoG filter is the second derivative of the Gaussian filter, so the formula of the LoG filter obtained by differentiating equation (4) is shown in equation (5):

[0077] The proportion factor before the exponent is mainly to ensure that the area under the Gaussian integral is 1. Therefore, by dividing equation (5) by the sum of the exponents shown in equation (6), the normalized LoG filter can be obtained:

[0078] When the coefficient and of the filter tend to zero, this filter can be used as a high-pass FIR filter. One of the characteristics of the high-pass FIR filter is no response at zero frequency. Therefore, the formula of the LoG filter is modified so that the sum of the taps is zero, as shown in equation (7). The final formula of the LoG filter is named as the modified Laplacian of Gaussian filter (MLoG), which makes the transition between the background noise and the signal clearer and sharper.

[0079] In equation (7), C represents the number of taps (filter order) of the MLoG filter. When dealing with vibration signals that are not very different, the order of MLoG can be selected as 10, and the order of σ is 2.5.

[0080] S3, square the filtered vibration data, extract the signal features corresponding to the vibration data from the squared vibration data, and then identify the abnormal state of the circuit breaker according to the extracted signal features.

[0081] Preferably, the squared vibration data is:

[0082] wherein X n (i) is a set of lengths of vibration data, W(i) is an average value of vibration data with a length of w taken on the set X n (i), X 2 (i) is a squared signal filtered using a modified Laplacian Gaussian filter, and w is the length of the vibration data.

[0083] The MLoG filter is used to filter the vibration signal. Further, the filtered signal is squared to maximize the difference between the vibration signals. Let the length of the vibration data taken be a set of X n (i), different data can be represented as X1(i), X2(i)…X n (i). The average value of w samples taken on the set is calculated for smoothing, as shown in equation (8).

[0084] In equation (8), X n (i) represents a squared signal filtered using the MLoG filter, and w represents the length of the vibration data taken. Through analysis, it is found that 8 samples in each set have the best filtering effect.

[0085] Then the signal features corresponding to the vibration data can be extracted from the squared vibration data, and the abnormal state of the circuit breaker can be identified according to the extracted signal features.

[0086] The effect of the above signal feature extraction is described below with a specific embodiment:

[0087] For the collection of horizontal circuit breaker vibration signals, the selection of sensor placement is crucial. The structure of the horizontal circuit breaker is relatively complex, and its vibration characteristics may vary depending on different parts. Therefore, when selecting the placement position, the sensor should be able to cover the key parts that may produce significant vibrations, such as the outer box on the contact side, the operating mechanism, etc.

[0088] Please refer to Figure 3, it is a schematic diagram of sensor distribution position and data sampling, as shown in Figure 3 (a), the sensor position is arranged for signal acquisition, and the data sampling waveform after the opening operation is shown in Figure (b), since the sensors are densely distributed, it is difficult to distinguish the signal characteristics collected by each other, therefore, the MLoG method is used for filtering processing, and effective distinction of the signal characteristics is realized.

[0089] As shown in Figure 3, the sensors 4, 5 and 6 are distributed on both sides of the horizontal circuit breaker respectively, in particular, 5 and 6 are completely symmetrical, and the measured data has high similarity, please refer to Figure 4, it is a comparison diagram of information entropy extraction of different sensor positions, after traditional filtering processing and information extraction, the effect is shown in Figure 4 (a). The results show that the traditional extraction method can effectively distinguish the different information of the data measured by the sensor 4 and the sensors 5 and 6, but it is difficult to distinguish the information between the sensors 5 and 6. Therefore, the MLoG method is used to improve the traditional method, and the information entropy extraction result shown in Figure 4 (b) is obtained, which can effectively distinguish the signal characteristics measured by the sensors 5 and 6.

[0090] The effect of extracting vibration signal characteristics under different working conditions by using the method is shown in Figure 5. In Figure 5 (a), only part of the data characteristics is obviously distinguished, but after the information entropy is extracted by MLoG, the opening and closing information in the whole data chain appears in multiple different time sequences, and the wave peak and wave valley characteristics are more helpful for the recognition of different working conditions by the deep learning algorithm.

[0091] The present application is aimed at the problem that part of the data characteristics is not obvious or difficult to distinguish when multiple sensors collect vibration signals, and combines the traditional feature extraction method of sensor data and the information entropy analysis technology to realize the filtering processing of environmental noise and the effective distinction of signal characteristics. At the same time, the application of the method to the analysis of vibration signals can quantify the sparsity and uncertainty of the vibration signals, when the pulse impact in the vibration signal is submerged by noise, the information entropy is larger, and vice versa. By comparing the current signal characteristics with the characteristic mode under the normal state or the known fault state, the abnormal state or the potential fault can be recognized, and the abnormal state or the potential fault can be warned or positioned.

[0092] Embodiment two

[0093] Please refer to Figure 6, it is a structural schematic diagram of a processing device for circuit breaker vibration signals provided by an embodiment of the present application, the device comprises: a vibration data acquisition module, a Gaussian filtering module and a signal feature extraction module.

[0094] The vibration data acquisition module is used for acquiring the vibration data of the circuit breaker.

[0095] The Gaussian filter module is configured to input the extracted vibration data into a preset modified Laplacian Gaussian filter, so that the modified Laplacian Gaussian filter strengthens the transition between background noise and signal characteristics in the vibration data, and outputs filtered vibration data; wherein the generation of the modified Laplacian Gaussian filter comprises: first-order derivation of a preset Gaussian filter to obtain a first-order derivative of the Gaussian filter; second-order derivation of the first-order derivative to obtain a second-order derivative of the Gaussian filter, and taking the second-order derivative as a corresponding LoG filter; normalization processing of the LoG filter, and making the sum of taps of the LoG filter zero to obtain the modified Laplacian Gaussian filter;

[0096] The signal characteristic extraction module is configured to square the filtered vibration data, extract signal characteristics corresponding to the vibration data from the squared vibration data, and then identify the abnormal state of the circuit breaker according to the extracted signal characteristics.

[0097] Preferably, the circuit breaker vibration signal processing device further comprises an information entropy extraction module.

[0098] The information entropy extraction module is configured to extract information entropy of the vibration data; determine whether the dimension of the vibration data is greater than a preset threshold according to the extracted information entropy; when the dimension of the vibration data is greater than the preset threshold, input the vibration data into the modified Laplacian Gaussian filter; and when the dimension of the vibration data is not greater than the preset threshold, directly extract corresponding signal characteristics of the vibration data.

[0099] It should be noted that the device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. In addition, the connection relationship between the modules in the device embodiment provided by the present application indicates that there is a communication connection between them, which can be realized as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0101] Embodiment three

[0102] Correspondingly, the embodiment of the present application provides an electronic device, the device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the processing method of the circuit breaker vibration signal when executing the computer program.

[0103] The electronic device can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The device can include but is not limited to a processor and a memory.

[0104] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and connects various parts of the device through various interfaces and lines.

[0105] Embodiment four

[0106] Correspondingly, the embodiment of the present application provides a storage medium, the storage medium comprising a stored computer program, wherein the computer program controls the device where the storage medium is located to execute the processing method of the circuit breaker vibration signal when the computer program is running.

[0107] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0108] The storage medium is a computer readable storage medium, and the computer program is stored in the computer readable storage medium. When the computer program is executed by a processor, steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0109] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method of processing a circuit breaker vibration signal, characterized by, The method comprises the following steps: obtaining vibration data of a circuit breaker; inputting the extracted vibration data into a preset modified Laplacian of Gaussian filter, so that the modified Laplacian of Gaussian filter strengthens the transition between background noise and signal characteristics in the vibration data, and outputs filtered vibration data; squared processing the filtered vibration data, extracting signal characteristics corresponding to the vibration data from the squared processed vibration data, and then identifying an abnormal state of the circuit breaker according to the extracted signal characteristics; wherein the generation of the modified Laplacian of Gaussian filter comprises: first-order derivation of a preset Gaussian filter to obtain a first-order derivative of the Gaussian filter; second-order derivation of the first-order derivative to obtain a second-order derivative of the Gaussian filter, and taking the second-order derivative as a corresponding LoG filter; normalization processing of the LoG filter, and making the sum of taps of the LoG filter zero to obtain the modified Laplacian of Gaussian filter.

2. The method of claim 1, wherein, Before inputting the extracted vibration data into a preset modified Laplacian of Gaussian filter, it further comprises the following steps: extracting the information entropy of the vibration data; determining whether the dimension of the vibration data is greater than a preset threshold according to the extracted information entropy; when the dimension of the vibration data is greater than the preset threshold, inputting the vibration data into the modified Laplacian of Gaussian filter; when the dimension of the vibration data is not greater than the preset threshold, directly extracting the corresponding signal characteristics of the vibration data.

3. The method of claim 2, wherein the step of determining the vibration signal of the circuit breaker is performed by a circuit breaker vibration signal processor. Before extracting the information entropy of the vibration data, it further comprises the following steps: preprocessing the vibration data; wherein the preprocessing comprises data filtering, data noise reduction and data cleaning.

4. The method of claim 3, wherein, Information entropy of the vibration data is extracted by the following formula: wherein H(x) is the information entropy of the vibration data, i = 1, 2, 3, … n; p(x i ) is the probability of each discrete point appearing in the vibration data, and x is a vibration random variable, E is the energy of the band signal, χ i is the amplitude of the discrete point corresponding to the band signal.

5. The method of claim 4, wherein the step of determining the vibration signal of the circuit breaker is performed by a method comprising: The modified Laplacian Gaussian filter is: where G(n) is the Gaussian filter, G'(n) is the first derivative of the Gaussian filter, G"(n) is the LoG filter, LoG ～ is the normalized LoG filter, MLoG is the modified Laplacian of Gaussian filter, σ is the standard deviation of the Gaussian filter, n is the Gaussian exponent, and C is the tap number of the modified Laplacian of Gaussian filter.

6. The method of claim 5, wherein, The squared processed vibration data is: where X n (i) is a set of lengths of the vibration data, W(i) is an average value of the vibration data of length w taken over the set X n (i) is a set of lengths of the vibration data, W(i) is an average value of the vibration data of length w taken over the set X 2 (i) is a set of lengths of the vibration data, W(i) is an average value of the vibration data of length w taken over the set X 7. A processing device of a circuit breaker vibration signal, characterized by, The method comprises the following steps: vibration data acquisition module, Gaussian filtering module and signal characteristic extraction module; the vibration data acquisition module is used for obtaining vibration data of a circuit breaker; the Gaussian filtering module is used for inputting the extracted vibration data into a preset modified Laplacian of Gaussian filter, so that the modified Laplacian of Gaussian filter strengthens the transition between background noise and signal characteristics in the vibration data, and outputs filtered vibration data; wherein the generation of the modified Laplacian of Gaussian filter comprises: first-order derivation of a preset Gaussian filter to obtain a first-order derivative of the Gaussian filter; second-order derivation of the first-order derivative to obtain a second-order derivative of the Gaussian filter, and taking the second-order derivative as a corresponding LoG filter; normalization processing of the LoG filter, and making the sum of taps of the LoG filter zero to obtain the modified Laplacian of Gaussian filter; the signal characteristic extraction module is used for squared processing the filtered vibration data, extracting signal characteristics corresponding to the vibration data from the squared processed vibration data, and then identifying an abnormal state of the circuit breaker according to the extracted signal characteristics.

8. The processing device of a circuit breaker vibration signal according to claim 7, wherein, It further comprises the following steps: information entropy extraction module; the information entropy extraction module is used for extracting the information entropy of the vibration data; According to the extracted information entropy, it is judged whether the dimension of the vibration data is greater than a preset threshold; when the dimension of the vibration data is greater than the preset threshold, the vibration data is input to the modified Laplacian Gaussian filter; when the dimension of the vibration data is not greater than the preset threshold, corresponding signal features of the vibration data are directly extracted.

9. An electronic device, comprising: The circuit breaker vibration signal processing method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the circuit breaker vibration signal processing method according to any one of claims 1 to 6 when executing the computer program.

10. A storage medium, characterized by The storage medium comprises a stored computer program, wherein the device where the storage medium is located executes the circuit breaker vibration signal processing method according to any one of claims 1 to 6 when the computer program runs.

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