CN115439667A - Transformer mechanical fault diagnosis method and system based on sound field distribution diagram - Google Patents
Transformer mechanical fault diagnosis method and system based on sound field distribution diagram Download PDFInfo
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Abstract
The invention discloses a transformer mechanical fault diagnosis method and system based on a sound field distribution diagram, wherein the transformer mechanical fault diagnosis method comprises the following steps: acquiring a sound field distribution diagram of a transformer to be diagnosed and a reference sound field distribution diagram of a transformer of the same type as the transformer to be diagnosed; acquiring the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram; acquiring a Pearson correlation coefficient between the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram; and judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient. The transformer mechanical fault diagnosis method based on the sound field distribution diagram can realize diagnosis of the transformer mechanical fault in a non-contact mode, and the transformer does not need to be stopped in the diagnosis process.
Description
Technical Field
The invention belongs to the technical field of power equipment fault diagnosis, relates to the field of transformer fault diagnosis, and particularly relates to a transformer mechanical fault diagnosis method and system based on a sound field distribution diagram.
Background
In an electric power system, vibration and noise generated by transformer faults are mainly measured and analyzed based on vibration signals, and a complete theoretical system and method are formed, for example: spectrum analysis technology, amplitude parameter index analysis, impact pulse technology, resonance demodulation technology and the like.
In the above-mentioned prior art fault diagnosis method based on vibration signals, it is necessary to arrange sensors on the surface of the vibration device; however, it is difficult to arrange sensors in a harsh environment such as a charged device, a vibrating surface of a complex component, high temperature or oil dirt, etc., and only vibration signals of a plurality of isolated measuring points on the vibrating surface can be analyzed, only local vibration information of the device can be reflected, in many cases, vibration information of a concerned component cannot be obtained, and the vibration situation of the whole device is difficult to present; in addition, for some occasions needing to stop and install the vibration sensor, the stop installation brings great economic loss; furthermore, due to the diversity of equipment failures, the failure characteristics are different, and vibration characteristics are not obvious under some failures, while other characteristics (such as acoustic characteristics) are obvious. In view of the foregoing, there is a need for an effective non-contact monitoring and analysis method.
Disclosure of Invention
The invention aims to provide a transformer mechanical fault diagnosis method and system based on a sound field distribution diagram so as to solve one or more technical problems. The transformer mechanical fault diagnosis method based on the sound field distribution diagram can realize diagnosis of the transformer mechanical fault in a non-contact mode, and the transformer does not need to be stopped in the diagnosis process.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a transformer mechanical fault diagnosis method based on a sound field distribution diagram, which comprises the following steps:
acquiring a sound field distribution diagram of a transformer to be diagnosed and a reference sound field distribution diagram of a transformer of the same type as the transformer to be diagnosed;
acquiring the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
acquiring a Pearson correlation coefficient between the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
and judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient.
In the further improvement of the invention, in the process of acquiring the sound field distribution diagram of the transformer to be diagnosed and the reference sound field distribution diagram of the transformer with the same model as the transformer to be diagnosed,
the step of acquiring the sound field distribution diagram of the transformer to be diagnosed comprises the following steps: in the transformer inspection process, detecting the noise generated by the front, the back, the left and the right surfaces of the transformer by a microphone array to obtain a multi-channel noise signal; drawing and obtaining a sound field distribution diagram of the transformer to be diagnosed based on the multi-channel noise signals obtained in the routing inspection process;
the step of obtaining the reference sound field distribution map of the transformer with the same model of the transformer to be diagnosed comprises the following steps: in the normal working process of the transformer, detecting noise signals generated on the front, rear, left and right surfaces of the transformer with the same type as the transformer to be diagnosed by a microphone array to obtain a multi-channel noise signal; and drawing sound field distribution maps of different surfaces of the transformer according to the multi-channel noise signals obtained in normal work, and obtaining a reference sound field distribution map of the transformer with the same model of the transformer to be diagnosed.
The invention is further improved in that in the process of detection through the microphone array, the distance between the microphone array and the contour line of the transformer box body is 5 times of the length of the outer vertical face of the corresponding contour line of the transformer.
In the further improvement of the invention, in the process of acquiring the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram,
the HOG feature vector extraction method comprises the following steps:
(1) Performing graying processing on the image of the input sound field distribution diagram to obtain a grayed image; wherein, when the graying processing is performed, the grayscale value calculation expression is Gray =0.3 × R +0.59 × G +0.11 × B (1);
in the formula, gray represents the Gray value of a certain pixel point of the grayed sound field distribution diagram; r, G and B respectively represent red, green and blue intensity values of corresponding pixel points in the source image;
(2) Calculating the transverse and longitudinal gradients and directions of each pixel point in the grayed image to obtain contour information;
wherein, the calculation expressions of the gradient at the pixel point (x, y) are formulas (2) and (3),
G x (x,y)＝H(x+1,y)-H(x-1,y) (2)；
G y (x,y)＝H(x,y+1)-H(x,y-1) (3)；
in the formula, G x (x,y)、G y (x, y) and H (x, y) are respectively the gray values of the image after horizontal gradient, longitudinal gradient and gray level in sequence;
wherein, the calculation expressions of the gradient amplitude and the gradient direction at the pixel point (x, y) are formulas (4) and (5),
(3) Dividing the image into a plurality of cells based on the contour information, and acquiring a feature vector of each cell;
(4) Forming a block by a preset number of unit cells; the method comprises the following steps that feature vectors of all cells in each block are connected in series to obtain HOG feature vectors of the block;
(5) Traversing the block through the whole image by the step length of one cell, and finally connecting HOG characteristic vectors of all blocks in the image in series to obtain the HOG characteristic vector of the image.
In the further improvement of the invention, in the process of obtaining the Pearson correlation coefficient between the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram,
the computational expression of Pearson correlation coefficients between HOG feature vectors is formula (6):
wherein n is the number of HOG feature vector data,X i ,Y i sequentially and respectively obtaining the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the ith sample of the HOG characteristic vector of the reference sound field distribution diagram,
sequentially and respectively obtaining the average value, sigma, of the samples of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram X ,σ Y Sequentially and respectively obtaining sample standard deviations of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram, wherein the PCC value range is [ -1,1]。
In the further improvement of the invention, in the process of judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient,
(1) When the PCC is not more than 0.95, the current sound field distribution diagram of the transformer to be diagnosed is strongly related to the reference sound field distribution diagram, and the mechanical state is good;
(2) When the PCC is more than or equal to 0.85 and less than 0.95, the correlation between the current sound field distribution diagram and the reference sound field distribution diagram of the transformer to be diagnosed is general, and the mechanical state changes;
(3) When PCC is less than 0.85, the current sound field distribution diagram of the transformer to be diagnosed is weakly correlated with the reference sound field distribution diagram, and the mechanical state is seriously changed.
The invention provides a transformer mechanical fault diagnosis system based on a sound field distribution diagram, which comprises:
the distribution diagram acquisition module is used for acquiring a sound field distribution diagram of the transformer to be diagnosed and a reference sound field distribution diagram of the transformer with the same model as the transformer to be diagnosed;
the characteristic vector acquisition module is used for acquiring the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
a Pearson correlation coefficient obtaining module, configured to obtain a Pearson correlation coefficient between the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram;
and the mechanical state acquisition module is used for judging the mechanical state of the transformer based on the acquired Pearson correlation coefficient.
In a further improvement of the present invention, during the process of obtaining the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram,
the HOG feature vector extraction method comprises the following steps:
(1) Performing graying processing on the image of the input sound field distribution diagram to obtain a grayed image; wherein, when the graying processing is performed, the grayscale value calculation expression is Gray =0.3 × R +0.59 × G +0.11 × B (1);
in the formula, gray represents the Gray value of a certain pixel point of the grayed sound field distribution diagram; r, G and B respectively represent red, green and blue intensity values of corresponding pixel points in the source image;
(2) Calculating the transverse and longitudinal gradients and directions of each pixel point in the grayed image to obtain contour information;
wherein, the calculation expressions of the gradient at the pixel point (x, y) are formulas (2) and (3),
G x (x,y)＝H(x+1,y)-H(x-1,y) (2)；
G y (x,y)＝H(x,y+1)-H(x,y-1) (3)；
in the formula, G x (x,y)、G y (x, y) and H (x, y) are respectively the gray values of the horizontal gradient, the longitudinal gradient and the gray image in sequence;
wherein, the calculation expressions of the gradient amplitude and the direction at the pixel point (x, y) are formulas (4) and (5),
(3) Dividing the image into a plurality of cells based on the contour information, and acquiring a feature vector of each cell;
(4) Forming a block by a preset number of unit cells; the method comprises the following steps that (1) feature vectors of all cells in each block are connected in series to obtain HOG feature vectors of the block;
(5) Traversing the block through the whole image by the step length of one cell, and finally connecting HOG characteristic vectors of all blocks in the image in series to obtain the HOG characteristic vector of the image.
In a further improvement of the present invention, in the process of acquiring the Pearson correlation coefficient between the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram by the Pearson correlation coefficient acquiring module,
the computational expression of Pearson correlation coefficients between HOG feature vectors is formula (6):
wherein n is the number of HOG feature vector data, X i ,Y i Sequentially and respectively obtaining the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the ith sample of the HOG characteristic vector of the reference sound field distribution diagram,
sequentially and respectively obtaining the average value, sigma, of the samples of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram X ,σ Y Sequentially and respectively obtaining sample standard deviations of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram, wherein the PCC value range is [ -1,1]。
The invention has the further improvement that in the process of judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient,
(1) When the PCC is not more than 0.95, the current sound field distribution diagram of the transformer to be diagnosed is strongly related to the reference sound field distribution diagram, and the mechanical state is good;
(2) When the PCC is more than or equal to 0.85 and less than 0.95, the correlation between the current sound field distribution diagram and the reference sound field distribution diagram of the transformer to be diagnosed is general, and the mechanical state changes;
(3) When PCC is less than 0.85, the current sound field distribution diagram of the transformer to be diagnosed is weakly correlated with the reference sound field distribution diagram, and the mechanical state is seriously changed.
Compared with the prior art, the invention has the following beneficial effects:
in the transformer mechanical fault diagnosis method based on the sound field distribution diagram, a transformer is taken as a sound source, noise signals generated by the transformer in various mechanical states are detected through a microphone array in a non-contact mode, and the sound field distribution under the transformer is drawn according to the detected noise signals, so that the HOG characteristic of the sound field distribution diagram under the transformer is obtained; in the process of judging the mechanical state of the transformer to be detected, the mechanical state of the transformer to be detected can be obtained only by acquiring a noise signal generated in the working process of the transformer to be detected through the microphone array and adopting a comparison mode, so that whether the mechanical fault occurs in the transformer to be detected is judged, the transformer does not need to be detected to be shut down, the detection cost is low, and the sensitivity and the accuracy are high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art are briefly introduced below; it is obvious that the drawings in the following description are some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a transformer mechanical fault diagnosis method based on a sound field distribution diagram according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of HOG feature extraction of a transformer sound field distribution diagram in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the generation and propagation of vibration noise of the transformer according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, a transformer mechanical fault diagnosis method based on a sound field distribution diagram according to an embodiment of the present invention includes the following steps:
1) In the normal working process of the transformer, noise signals generated by the front, the back, the left and the right of a certain transformer of a certain type are detected through a microphone array; drawing sound field distribution diagrams of different surfaces of the transformer according to the multi-channel noise signals, and establishing a sample library of the sound field distribution diagrams of the transformer in a normal state to serve as a reference sound field distribution diagram of the transformer of the type;
2) In the transformer inspection process, acquiring a sound field distribution diagram of the transformer to be detected in the current state according to the mode in the step 1), and comparing the sound field distribution diagram with a reference sound field distribution diagram so as to judge the mechanical state of the transformer;
3) Respectively calculating a Histogram of Oriented Gradient (HOG) feature vector (HOG) for short from the transformer sound field distribution diagram obtained in the step 1) and the step 2);
4) Solving Pearson correlation coefficients among the HOG characteristic vectors of the transformer obtained in the step 3) under different states;
5) And judging the current mechanical state of the transformer according to the Pearson correlation coefficient.
In the step 1) and the step 2) of the embodiment of the invention, sound field distribution maps of four surfaces of the transformer are obtained through the acoustic microphone array, wherein the distance between the microphone array and the contour line of the transformer box body is related to the size of the transformer, the distance is selected to be 5 times of the length of the outer vertical surface of the corresponding contour line of the transformer, so that one surface of the whole transformer can be covered by the acquisition range of the microphone array, and the height of the sensor is half of the height of the transformer box body.
In step 3) of the embodiment of the present invention, the method for extracting the HOG feature vector of the transformer sound field distribution map is as follows:
(1) The image of the input sound field distribution diagram is grayed by the formula (1), wherein R, G and B respectively represent red, green and blue intensity values in the source image, and the grayed result is shown in FIG. 2.
Gray＝0.3×R+0.59×G+0.11×B (1)
In addition, in order to reduce the influence of local shadows and illumination changes of an image and suppress noise interference, a Gamma correction method is generally used to normalize a color space of an input image. However, the sound field distribution diagram in the present invention is not an image obtained by imaging, and does not have these influences, so that Gamma correction is not required.
(2) And calculating the transverse and longitudinal gradients and directions of each pixel point of the grayed image, so that the contour information can be captured. The method for calculating the gradient at the pixel point (x, y) in the image comprises the following steps:
G x (x,y)＝H(x+1,y)-H(x-1,y) (2)
G y (x,y)＝H(x,y+1)-H(x,y-1) (3)
in the formula, G x (x,y)、G y The (x, y) and the H (x, y) are respectively a horizontal gradient, a longitudinal gradient and a pixel value in sequence;
the gradient magnitude and direction at this pixel point are respectively:
(3) Dividing the image into a plurality of small cells (cells), as shown in fig. 2, the invention concretely exemplarily classifies 8 × 8 pixel points of the sound field distribution diagram into one cell, and then constructs a weighted gradient direction histogram for each cell; the direction of the gradient can be divided into several blocks, and a range of blocks dividing 360 degrees into 9 directions is usually selected. Specifically, for example, if the gradient direction at a certain pixel point is 36 degrees and the gradient value is 5, the value of the direction block of 0 to 40 degrees of the histogram is increased by 5. And sequentially traversing all pixel points in the cell to finally obtain a 9-dimensional feature vector of the cell, wherein an Orientation history bin is a polar coordinate expression mode of the histogram as shown in fig. 2.
(4) Every few cells are combined into a block, and the invention specifically exemplifies that 2 × 2 cells are combined into a block, and feature vectors of all cells in the block are connected in series to obtain a HOG feature vector of the block.
(5) Traversing the block from left to right and from top to bottom of the whole image by the step length of one cell, finally connecting HOG characteristic vectors of all blocks in the image in series to obtain the HOG characteristic vector of the image, and after obtaining the HOG characteristic vector of the image, using the HOG characteristic vector as mathematical description of the sound field distribution diagram.
In step 4) of the embodiment of the present invention, the calculation method of the correlation coefficient of the HOG feature vector Pearson of the transformer sound field distribution diagram in different states is as follows:
and (3) solving Pearson correlation coefficients among HOG characteristic vectors of the sound field distribution diagram under different working conditions:
in the formula (I), the compound is shown in the specification,
is the sample mean value, σ X The PCC value range is [ -1,1 ] for the sample standard deviation]. Specifically, for example, it is generally considered that a correlation coefficient greater than 0.9 has a strong correlation.
In step 5) of the embodiment of the invention, the method for judging the current mechanical state of the transformer according to the Pearson correlation coefficient comprises the following steps:
and respectively judging the mechanical states of four surfaces of the same transformer according to the Pearson correlation coefficients of sound field distribution patterns of different surfaces, so as to roughly judge the position of the mechanical fault.
(1) When the PCC is not more than 0.95, the current sound field distribution diagram of the transformer to be tested is strongly related to the reference sound field distribution diagram, and the current mechanical state is good;
(2) When the PCC is more than or equal to 0.85 and less than 0.95, the correlation between the current sound field distribution diagram of the transformer to be tested and the reference sound field distribution diagram is general, the current mechanical state changes, and the monitoring needs to be carried out by combining various means;
(3) When the PCC is less than 0.85, the current sound field distribution diagram of the transformer to be tested is weakly related to the reference sound field distribution diagram, the current mechanical state is seriously changed, the transformer is proposed to be shut down immediately, and fault diagnosis is carried out on one side of the transformer with the sound field distribution diagram exceeding the standard in a targeted mode according to Pearson correlation coefficients of different sides by combining various means. And meanwhile, a detailed regulation test is accepted, so that a large-range power failure accident caused by sudden failure of the transformer is avoided.
In the method for diagnosing the mechanical fault of the transformer based on the acoustic imaging technology, because the noise signals generated by the transformer are different, the transformer is used as a sound source, the noise signals generated by the transformer in various mechanical states are detected by the microphone array in a non-contact mode, the sound field distribution under the transformer is drawn according to the detected noise signals, and the HOG characteristic of the sound field distribution diagram under the transformer is obtained.
The feasibility analysis of the diagnostic method provided by the above embodiment of the invention is as follows:
the transformer vibration is caused by the vibration of the transformer body and the vibration of the cooling device, the transformer body mainly comprises an iron core and a winding, and the vibration generation and propagation process is shown in fig. 3. The vibration of the winding is mainly caused by the dynamic electromagnetic force applied to the coil with alternating current in a leakage magnetic field, the vibration of the iron core is mainly generated by the magnetostrictive phenomenon of silicon steel sheets and the electromagnetic force caused by the eddy current action between the silicon steel sheets, and as can be seen from fig. 3, the vibration and noise signals of the transformer can most directly reflect the change of the internal mechanical structure of the transformer, so that the vibration and noise signals of the transformer can be used as the basis of the state diagnosis of the transformer, and the scientificity and reliability are self-evident.
The fault diagnosis of the transformer is to identify whether the running state of the transformer is normal or not, then determine the nature, the position and the reason of the fault, and finally provide measures for solving the fault. The purpose of fault diagnosis is to judge the mechanical state of the system through mapping according to the measurable characteristic vectors. The conventional acoustic diagnosis technology mainly measures acoustic signals through a limited number of measuring points, and then extracts features of the acoustic signals by using a signal processing technology to perform fault diagnosis, so the conventional acoustic diagnosis technology can be regarded as mode identification based on the acoustic signals, noise signals are vector signals, therefore, the identification features are extracted from the vector signals, while the array measurement and acoustic imaging technology is used for obtaining the sound field quantity distribution of an equipment sound field, a matrix or image feature extraction technology is required when the features are extracted, and therefore, the essence of fault diagnosis by using sound images is fault diagnosis based on sound image mode identification. When the method provided by the embodiment of the invention is used for detecting noise signals, the microphone array is fixed on the array support, then the array support is fixed around the transformer, the noise signals generated by the transformer are detected through the microphone array, then the noise signals collected by the microphones are synchronously collected through the data collection system and then input into the computer, and the computer draws the sound field distribution of the transformer to be detected by adopting an acoustic imaging technology according to the noise signals collected by the microphones and the positions of the microphones. The data acquisition system synchronously acquires the electric signals of the multi-channel microphone, and the requirements of dynamic range, digit, anti-aliasing filter, built-in IEPE excitation and the like which need attention in single sound acquisition are met, and the phase consistency among channels is also required to be good for microphone array application, and acquisition equipment of an independent ADC (analog to digital converter) of each channel is generally selected.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details of non-careless mistakes in the embodiment of the apparatus, please refer to the embodiment of the method of the present invention.
The embodiment of the invention provides a transformer mechanical fault diagnosis system based on a sound field distribution diagram, which comprises the following steps:
the distribution diagram acquisition module is used for acquiring a sound field distribution diagram of the transformer to be diagnosed and a reference sound field distribution diagram of the transformer with the same type as the transformer to be diagnosed;
the characteristic vector acquisition module is used for acquiring the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
a Pearson correlation coefficient obtaining module, configured to obtain a Pearson correlation coefficient between the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram;
and the mechanical state acquisition module is used for judging the mechanical state of the transformer based on the acquired Pearson correlation coefficient.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A transformer mechanical fault diagnosis method based on a sound field distribution diagram is characterized by comprising the following steps:
acquiring a sound field distribution diagram of a transformer to be diagnosed and a reference sound field distribution diagram of a transformer of the same type as the transformer to be diagnosed;
acquiring the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
acquiring a Pearson correlation coefficient between the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
and judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient.
2. The method for diagnosing the mechanical fault of the transformer based on the sound field distribution diagram according to claim 1, wherein in the process of acquiring the sound field distribution diagram of the transformer to be diagnosed and the reference sound field distribution diagram of the transformer with the same model as the transformer to be diagnosed,
the step of acquiring the sound field distribution map of the transformer to be diagnosed comprises the following steps: in the transformer inspection process, detecting the noise generated by the front, the back, the left and the right surfaces of the transformer by a microphone array to obtain a multi-channel noise signal; drawing and obtaining a sound field distribution diagram of the transformer to be diagnosed based on the multi-channel noise signals obtained in the routing inspection process;
the step of acquiring the reference sound field distribution map of the transformer with the same model of the transformer to be diagnosed comprises the following steps: in the normal working process of the transformer, detecting noise signals generated on the front, rear, left and right surfaces of the transformer with the same type as the transformer to be diagnosed by a microphone array to obtain a multi-channel noise signal; and drawing sound field distribution diagrams of different surfaces of the transformer according to the multi-channel noise signals obtained in normal work, and obtaining the reference sound field distribution diagram of the transformer with the same type of the transformer to be diagnosed.
3. The method for diagnosing the mechanical fault of the transformer based on the sound field distribution diagram according to claim 2, wherein in the process of detection through the microphone array, the distance between the microphone array and the contour line of the transformer tank is 5 times of the length of the facade of the contour line of the corresponding transformer.
4. The method for diagnosing the mechanical fault of the transformer based on the sound field distribution diagram according to claim 1, wherein in the process of obtaining the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram,
the HOG feature vector extraction method comprises the following steps:
(1) Carrying out graying processing on the input image of the sound field distribution diagram to obtain a grayed image; wherein, when the graying processing is performed, the Gray value calculation expression is Gray =0.3 × R +0.59 × G +0.11 × B (1);
in the formula, gray represents the Gray value of a certain pixel point of the grayed sound field distribution diagram; r, G and B respectively represent red, green and blue intensity values of corresponding pixel points in the source image;
(2) Calculating the transverse and longitudinal gradients and directions of each pixel point in the grayed image to obtain contour information;
wherein, the calculation expressions of the gradient at the pixel point (x, y) are formulas (2) and (3),
G x (x,y)＝H(x+1,y)-H(x-1,y) (2)；
G y (x,y)＝H(x,y+1)-H(x,y-1) (3)；
in the formula, G x (x,y)、G y (x, y) and H (x, y) are respectively the gray scale of the image after horizontal gradient, longitudinal gradient and gray scale in sequenceA value;
wherein, the calculation expressions of the gradient amplitude and the direction at the pixel point (x, y) are formulas (4) and (5),
(3) Dividing the image into a plurality of cells based on the contour information, and acquiring a feature vector of each cell;
(4) Forming a block by a preset number of unit cells; the method comprises the following steps that feature vectors of all cells in each block are connected in series to obtain HOG feature vectors of the block;
(5) Traversing the block through the whole image by the step length of one cell, and finally connecting HOG characteristic vectors of all blocks in the image in series to obtain the HOG characteristic vector of the image.
5. The method for diagnosing the mechanical fault of the transformer based on the sound field distribution diagram according to claim 4, wherein in the process of obtaining the Pearson correlation coefficient between the HOG eigenvector of the sound field distribution diagram of the transformer to be diagnosed and the HOG eigenvector of the reference sound field distribution diagram,
the computational expression of Pearson correlation coefficients between HOG feature vectors is formula (6):
wherein n is the number of HOG feature vector data, X i ,Y i Sequentially and respectively obtaining the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the ith sample of the HOG characteristic vector of the reference sound field distribution diagram,
sequentially and respectively obtaining the average value, sigma, of the samples of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram X ,σ Y Sequentially and respectively obtaining sample standard deviations of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram, wherein the PCC value range is [ -1,1]。
6. The method for diagnosing the mechanical fault of the transformer based on the sound field distribution diagram according to claim 5, wherein in the process of judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient,
(1) When the PCC is not more than 0.95, the current sound field distribution diagram of the transformer to be diagnosed is strongly related to the reference sound field distribution diagram, and the mechanical state is good;
(2) When the PCC is more than or equal to 0.85 and less than 0.95, the correlation between the current sound field distribution diagram and the reference sound field distribution diagram of the transformer to be diagnosed is general, and the mechanical state changes;
(3) When PCC is less than 0.85, the current sound field distribution diagram of the transformer to be diagnosed is weakly correlated with the reference sound field distribution diagram, and the mechanical state is seriously changed.
7. A transformer mechanical fault diagnosis system based on sound field distribution diagram is characterized by comprising:
the distribution diagram acquisition module is used for acquiring a sound field distribution diagram of the transformer to be diagnosed and a reference sound field distribution diagram of the transformer with the same model as the transformer to be diagnosed;
the characteristic vector acquisition module is used for acquiring the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram;
a Pearson correlation coefficient obtaining module, configured to obtain a Pearson correlation coefficient between the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram;
and the mechanical state acquisition module is used for judging the mechanical state of the transformer based on the acquired Pearson correlation coefficient.
8. The system of claim 7, wherein the feature vector obtaining module implements obtaining the HOG feature vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG feature vector of the reference sound field distribution diagram,
the HOG feature vector extraction method comprises the following steps:
(1) Performing graying processing on the image of the input sound field distribution diagram to obtain a grayed image; wherein, when the graying processing is performed, the grayscale value calculation expression is Gray =0.3 × R +0.59 × G +0.11 × B (1);
in the formula, gray represents the Gray value of a certain pixel point of the grayed sound field distribution diagram; r, G and B respectively represent red, green and blue intensity values of corresponding pixel points in the source image;
(2) Calculating the transverse and longitudinal gradients and directions of each pixel point in the grayed image to obtain contour information;
wherein, the calculation expressions of the gradient at the pixel point (x, y) are formulas (2) and (3),
G x (x,y)＝H(x+1,y)-H(x-1,y) (2)；
G y (x,y)＝H(x,y+1)-H(x,y-1) (3)；
in the formula, G x (x,y)、G y (x, y) and H (x, y) are respectively the gray values of the image after horizontal gradient, longitudinal gradient and gray level in sequence;
wherein, the calculation expressions of the gradient amplitude and the direction at the pixel point (x, y) are formulas (4) and (5),
(3) Dividing the image into a plurality of cells based on the contour information, and acquiring a feature vector of each cell;
(4) Forming a block by a preset number of unit cells; the method comprises the following steps that feature vectors of all cells in each block are connected in series to obtain HOG feature vectors of the block;
(5) Traversing the block through the whole image by the step length of one cell, and finally connecting HOG characteristic vectors of all blocks in the image in series to obtain the HOG characteristic vector of the image.
9. The system of claim 8, wherein the Pearson correlation coefficient obtaining module implements the process of obtaining the Pearson correlation coefficient between the HOG eigenvector of the sound field distribution diagram of the transformer to be diagnosed and the HOG eigenvector of the reference sound field distribution diagram,
the computational expression of Pearson correlation coefficient between HOG feature vectors is formula (6):
wherein n is the number of HOG feature vector data, X i ,Y i Sequentially and respectively obtaining the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the ith sample of the HOG characteristic vector of the reference sound field distribution diagram,
sequentially and respectively obtaining the average value, sigma, of samples of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram X ,σ Y Sequentially and respectively obtaining sample standard deviations of the HOG characteristic vector of the sound field distribution diagram of the transformer to be diagnosed and the HOG characteristic vector of the reference sound field distribution diagram, wherein the PCC value range is [ -1,1]。
10. The system for diagnosing the mechanical fault of the transformer based on the sound field distribution diagram according to claim 9, wherein the mechanical state obtaining module is implemented in a process of judging the mechanical state of the transformer based on the obtained Pearson correlation coefficient,
(1) When the PCC is not more than 0.95, the current sound field distribution diagram of the transformer to be diagnosed is strongly related to the reference sound field distribution diagram, and the mechanical state is good;
(2) When PCC is more than or equal to 0.85 and less than 0.95, the correlation between the current sound field distribution diagram and the reference sound field distribution diagram of the transformer to be diagnosed is general, and the mechanical state changes;
(3) When PCC is less than 0.85, the current sound field distribution diagram of the transformer to be diagnosed is weakly correlated with the reference sound field distribution diagram, and the mechanical state is seriously changed.
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