Intelligent fault detection method for electrical equipment

Abstract

The invention relates to the technical field of electrical variable measurement, and provides an intelligent fault detection method for electrical equipment, and the method comprises the steps: obtaining a three-dimensional imaging equipment fault monitoring matrix; obtaining a division result of each row of data from the three-dimensional imaging equipment fault monitoring matrix, and calculating a synchronous rising fit factor, a synchronous reducing fit factor and a synchronous trend fit correlation coefficient according to the division result of each row of data; obtaining a trend fitting indirect correlation coefficient and a trend fitting comprehensive correlation coefficient according to the synchronous trend fitting correlation coefficient; acquiring a comprehensive weight correction index according to the trend fitting comprehensive correlation coefficient; and obtaining a fitting curve of each row of data in the three-dimensional imaging equipment fault detection matrix by adopting a local weighted regression algorithm based on the comprehensive weight correction index, and obtaining an equipment fault detection result based on the fitting curve. According to the method, the fitting curve of the equipment monitoring data is obtained through the comprehensive weight correction index, and the equipment fault detection precision is improved.

Description

Technical Field

[0001] The invention relates to the technical field of measuring electrical variables, and in particular to an intelligent fault detection method for electrical equipment. Background Art

[0002] Infrared three-dimensional precise imaging equipment is an electrical device that can use binocular systems and motion compensation technology to obtain an accurate three-dimensional point cloud model of the detection target, achieve accurate measurement and correction of the surface temperature of the detection target, and solve the current problem of inaccurate temperature measurement at different distances, angles and environments. At the same time, it can also perform inversion calculations on the internal temperature field of the detection target, thereby diagnosing and analyzing the internal heating conditions of the detection target. Among them, the electrical parameter data of the infrared three-dimensional precise imaging equipment can reflect whether its operating status has a fault, but the working environment of the infrared three-dimensional precise imaging equipment is usually more complex, and the equipment is small and requires high precision. The core components are easily affected by the external environment, resulting in large errors in the acquired electrical parameter data, which affects the accuracy of fault detection by the infrared three-dimensional precise imaging equipment.

[0003] The local weighted regression Lowess (Locally Weighted Scatterplot Smoothing) algorithm can eliminate the influence of other factors based on the nonlinear relationship between data sequences, reflect the overall trend of electrical parameter data, obtain accurate electrical parameter fitting values, and improve the accuracy of fault detection of infrared three-dimensional precise imaging equipment. However, the Lowess algorithm needs to pre-set a weight for the input data. When the pre-set weight is inappropriate, the accuracy of the Lowess algorithm fitting may be low, reducing the accuracy of fault detection of infrared three-dimensional precise imaging equipment. Summary of the invention

[0004] The present invention provides a fault intelligent detection method for electrical equipment to solve the problem of low fault detection accuracy of infrared three-dimensional precise imaging equipment. The technical solution adopted is as follows: An embodiment of the present invention provides a method for intelligent fault detection of electrical equipment, the method comprising the following steps: Obtaining a three-dimensional imaging equipment fault monitoring matrix; According to the change trend characteristics of each row of data in the three-dimensional imaging device fault monitoring matrix, the division result of each row of data is obtained; according to the division result of each row of data in the three-dimensional imaging device fault monitoring matrix, the synchronization trend matching correlation coefficient between different rows of data in the three-dimensional imaging device fault monitoring matrix is ​​obtained; According to the synchronous trend matching correlation coefficient between different rows of data in the three-dimensional imaging equipment fault monitoring matrix, a trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix is ​​constructed; according to the trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix, a trend matching comprehensive correlation coefficient between non-electrical parameter data and electrical parameter data in the three-dimensional imaging equipment fault monitoring matrix is ​​obtained; according to the trend matching comprehensive correlation coefficient between non-electrical parameter data and electrical parameter data in the three-dimensional imaging equipment fault monitoring matrix, a comprehensive weight correction index of each element in the electrical parameter data sequence in the three-dimensional imaging equipment fault detection matrix is ​​obtained; A fitting curve for each row of data in the three-dimensional imaging device fault monitoring matrix is ​​obtained according to the comprehensive weight correction index of each element in the three-dimensional imaging device fault monitoring matrix, and a CNN neural network model is used based on the fitting curve to obtain the fault monitoring result of the three-dimensional imaging device.

[0005] Preferably, the method for obtaining the division result of each row of data according to the change trend characteristics of each row of data in the three-dimensional imaging device fault monitoring matrix is: For each row of data in the three-dimensional imaging device fault monitoring matrix, the sequence composed of each row of data is used as the monitoring data sequence, the position number of each data in the monitoring data sequence is used as the horizontal coordinate, the size of each data value in the monitoring data sequence is used as the vertical coordinate, and the curve formed by the horizontal coordinate and the vertical coordinate is used as the monitoring data curve of the monitoring data sequence, and all the maximum points and minimum points of the monitoring data curve are obtained, and all the maximum points and minimum points are used as the maximum segmentation points and the minimum segmentation points of the monitoring data sequence respectively, and the sequence composed of all the maximum segmentation points and the minimum segmentation points in ascending time order is used as the segmentation sequence, and according to the segmentation sequence, in order from left to right, the sequence composed of the corresponding data in the monitoring data sequence between the adjacent maximum segmentation points and the minimum segmentation points is used as the decreasing trend sequence of the monitoring data sequence, and the sequence composed of the corresponding data in the monitoring data sequence between the adjacent minimum segmentation points and the maximum segmentation points is used as the increasing trend sequence of the monitoring data sequence.

[0006] Preferably, the method for obtaining the synchronization trend matching correlation coefficient between different rows of data in the three-dimensional imaging device fault monitoring matrix according to the division result of each row of data in the three-dimensional imaging device fault monitoring matrix is: For each row of data in the three-dimensional imaging device fault monitoring matrix, the sequence composed of each row of data is used as a monitoring data sequence, a Bayesian curve fitting algorithm is used to obtain a Bayesian fitting curve corresponding to the monitoring data sequence, and a slope corresponding to each element in the monitoring data sequence is obtained according to the Bayesian fitting curve; Obtaining a synchronous increase matching factor between different monitoring data sequences according to the difference in growth trend sequences between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix; Acquire a synchronization reduction fit factor between different monitoring data sequences according to the difference in reduction trend sequence between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix; The sum of the synchronous increase fit factor and the synchronous decrease fit factor between the different monitoring data sequences is taken as the first correlation coefficient, the SBD distance between the different monitoring data sequences is taken as the second correlation coefficient, and the product of the first correlation coefficient and the second correlation coefficient is taken as the synchronous trend fit correlation coefficient between the different monitoring data sequences.

[0007] Preferably, the calculation method for obtaining the synchronous increase matching factor between different monitoring data sequences according to the difference in growth trend sequences between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is: In the formula, Indicates monitoring data series and The synchronous increase matching factor between the monitoring data series; and Respectively represent and The monitoring data series The first in the growth trend sequence The collection time corresponding to each data; and Respectively represent and The monitoring data series The first in the growth trend sequence The data corresponds to the slope; Indicates The monitoring data series The number of data in the growth trend series is The monitoring data series The minimum value of the number of data in a growth trend series; Indicates The number of growth trend sequences in the monitoring data series is The minimum value of the number of growth trend sequences in the monitoring data series; represents the adjustment parameter; is a natural constant.

[0008] Preferably, the calculation method for obtaining the synchronous reduction fit factor between different monitoring data sequences according to the difference in the reduction trend sequence between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is: In the formula, Indicates monitoring data series and The synchronization between the monitoring data series reduces the fit factor; and Respectively represent and The monitoring data series The first The collection time corresponding to each data; and Respectively represent and The monitoring data series The first The data corresponds to the slope; Indicates The monitoring data series The number of data in the trend series is reduced by The monitoring data series The minimum value among the number of data in the decreasing trend series; Indicates The number of decreasing trend sequences in the monitoring data series is related to the The minimum value of the number of decreasing trend sequences in the monitored data series; represents the adjustment parameter; is a natural constant.

[0009] Preferably, the method for constructing a trend-related undirected graph of a three-dimensional imaging device fault monitoring matrix according to the synchronization trend matching correlation coefficient between different rows of data in the three-dimensional imaging device fault monitoring matrix is: For each row of data in the three-dimensional imaging device fault monitoring matrix, a sequence composed of each row of data is used as a monitoring data sequence, a sequence composed of synchronization trend matching correlation coefficients between the monitoring data sequence and all other monitoring data sequences is sorted in ascending order as a synchronization trend matching correlation sequence of the monitoring data sequence, and a binary tree of the monitoring data sequence is constructed according to the synchronization trend matching correlation sequence; Each monitoring data sequence is regarded as a node in an undirected graph, the tree edit distance between the binary trees corresponding to the two monitoring data sequences is regarded as the initial weight between the corresponding two nodes, and the undirected graph constructed based on all monitoring data sequences, the initial weights of different monitoring data sequences and the undirected graph is regarded as the trend association undirected graph of the fault monitoring matrix of the three-dimensional imaging equipment.

[0010] Preferably, the method for obtaining the trend matching comprehensive correlation coefficient between the non-electrical parameter data and the electrical parameter data in the three-dimensional imaging device fault monitoring matrix according to the trend correlation undirected graph of the three-dimensional imaging device fault monitoring matrix is: Each row of data corresponding to the non-electrical parameter data in the three-dimensional imaging device fault monitoring matrix is ​​used as a non-electrical parameter data sequence of the three-dimensional imaging device fault monitoring matrix, and each row of data corresponding to the electrical parameter data in the three-dimensional imaging device fault monitoring matrix is ​​used as an electrical parameter data sequence of the three-dimensional imaging device fault monitoring matrix; Calculate the trend matching indirect correlation coefficient according to the corresponding relationship between the non-electrical parameter data sequence and the electrical parameter data sequence in the trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix; The sum of the synchronous trend matching correlation coefficient and the trend matching indirect correlation coefficient between each non-electrical parameter data sequence and each electrical parameter data sequence in the three-dimensional imaging equipment fault monitoring matrix is ​​taken as the first characteristic coefficient, the sum of all the first characteristic coefficients is taken as the second characteristic coefficient, and the ratio of the first characteristic coefficient to the second characteristic coefficient is taken as the comprehensive correlation coefficient of the trend matching between each non-electrical parameter data sequence and each electrical parameter data sequence.

[0011] Preferably, the method for calculating the trend matching indirect correlation coefficient according to the correspondence between the non-electrical parameter data sequence and the electrical parameter data sequence in the trend correlation undirected graph of the three-dimensional imaging device fault monitoring matrix is: In the formula, Indicates The non-electrical parameter data series and The trend fit indirect correlation coefficient between the electrical parameter data series; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The first The synchronization trend fit correlation coefficient between the monitoring data sequences corresponding to the nodes; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The first The actual weight of the edge between nodes; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The sequence number corresponding to each node; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The number of nodes in a path; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The number of paths between electrical parameter data sequences.

[0012] Preferably, the method for obtaining the comprehensive weight correction index of each element in the electrical parameter data sequence in the three-dimensional imaging device fault detection matrix according to the trend matching comprehensive correlation coefficient between the non-electrical parameter data and the electrical parameter data in the three-dimensional imaging device fault monitoring matrix is: For each electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix, the trend matching comprehensive correlation coefficient between each electrical parameter data sequence and each other non-electrical parameter data sequence is used as the fusion weight of each non-electrical parameter data sequence, all the non-electrical parameter data sequences are weightedly fused according to the corresponding fusion weights, and the weighted fusion result is used as the non-electrical parameter fusion sequence of each electrical parameter data sequence, and the predicted value of each element in each electrical parameter data sequence is obtained by using the ARIMAX model based on the non-electrical parameter fusion sequence of each electrical parameter data sequence; The comprehensive weight correction index of each element in each electrical parameter data sequence is obtained according to the true value and the predicted value corresponding to each element in each electrical parameter data sequence.

[0013] Preferably, the method for obtaining the comprehensive weight correction index of each element in each electrical parameter data sequence according to the true value and the predicted value corresponding to each element in each electrical parameter data sequence is: In the formula, Indicates The first The comprehensive weight correction index of the elements; Indicates The first The difference between the true value and the predicted value corresponding to the element; Indicates The maximum value of the difference between the true value and the predicted value corresponding to all elements in the electrical parameter data sequence; Indicates The first The original weights in the local weighted regression algorithm corresponding to the elements; is a natural exponential function.

[0014] The beneficial effects of the present invention are as follows: obtaining synchronous trend matching correlation coefficients between different types of monitoring data in a three-dimensional imaging device fault monitoring matrix through synchronous change characteristics between different types of monitoring data, constructing a trend association undirected graph of the three-dimensional imaging device fault monitoring matrix according to the synchronous matching correlation coefficients between different types of monitoring data, obtaining a trend matching comprehensive correlation coefficient between a non-electrical parameter data sequence and an electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix according to the trend association undirected graph, obtaining a comprehensive weight correction index based on the trend matching comprehensive correlation coefficient by using data fusion and an ARIMAX model, and adjusting the weight of each data in a Lowess algorithm based on the comprehensive weight correction index. The beneficial effects of the present invention are to avoid the low accuracy of curve fitting using a Lowess algorithm due to unreasonable weight setting, improve the accuracy of processing electrical parameter data of infrared three-dimensional precise imaging equipment, and thereby improve the accuracy of fault monitoring of infrared three-dimensional precise imaging equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] Figure 1 A schematic diagram of a flow chart of a method for intelligent fault detection of electrical equipment provided by an embodiment of the present invention; Figure 2 A schematic diagram of a trend association undirected graph acquisition process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 , which shows a flow chart of a method for intelligent fault detection of electrical equipment provided by an embodiment of the present invention, the method comprising the following steps: Step S001, obtaining a three-dimensional imaging device fault monitoring matrix.

[0019] During the operation of the infrared 3D precise imaging equipment, a voltmeter and an ammeter are placed on the equipment circuit, a vibration sensor, a temperature sensor, and a humidity sensor are arranged in the internal gaps of the infrared 3D precise imaging equipment structure, and a sound sensor, an outdoor air detector, and an anemometer are arranged on the surface of the infrared 3D precise imaging equipment to collect data such as current, voltage, vibration, temperature, humidity, audio, dust concentration, and wind speed of the infrared 3D precise imaging equipment. The time interval for data collection is (the size is taken as the empirical value 1), the length of the collected data sequence is (The empirical value is 500). At the same time, since data may be missing during the data collection process, and different data types have different units, the above data are preprocessed. Specifically, the missing values ​​are filled by linear interpolation, and the filled data sequence is Z-Score standardized. The specific calculation process of linear interpolation and Z-Score standardization are well-known technologies and will not be described in detail.

[0020] Obtain all monitoring data sequences of the preprocessed infrared three-dimensional precise imaging device, and record the matrix composed of all the monitoring data sequences as the three-dimensional imaging device fault monitoring matrix ,in is the electrical parameter data sequence, corresponding to the current and voltage data sequence, It is a non-electrical parameter data sequence, which includes vibration, temperature, humidity, audio, dust concentration, and wind speed data sequence. Represents the transpose of a matrix.

[0021] At this point, the three-dimensional imaging equipment fault monitoring matrix is ​​obtained.

[0022] Step S002, obtaining the division result of each row of data according to the change trend characteristics of each row of data in the three-dimensional imaging matrix, and calculating the synchronous increase fit factor, the synchronous decrease fit factor, and the synchronous trend fit correlation coefficient according to the division result of each row of data.

[0023] Infrared 3D precision imaging equipment usually performs infrared 3D imaging on non-active heat dissipation equipment, such as cable joints, casings and other equipment, to measure the accurate surface temperature of the equipment. However, the working environment of infrared 3D precision imaging equipment is usually more complicated, and the infrared 3D precision imaging equipment is small in size, but the measurement precision of internal parts is higher. Therefore, the current and voltage data obtained are easily affected by other factors (such as temperature, wind speed, etc.), resulting in low accuracy of the collected electrical parameter data, resulting in low fault detection accuracy of infrared 3D precision imaging equipment.

[0024] Therefore, the purpose of the present invention is to obtain the change characteristics of each electrical parameter data point according to the correlation characteristics between non-electrical parameter data and electrical parameter data, and use the local weighted regression Lowess algorithm to fit the electrical parameter data sequence, reflect the overall trend of the electrical parameter data sequence, and correct the original electrical parameter data. Finally, based on the electrical parameter data curve of the infrared three-dimensional precise imaging device after correction, a high-precision equipment fault detection model is established using a convolutional neural network model to achieve intelligent detection of infrared three-dimensional precise imaging device faults.

[0025] Specifically, in an infrared three-dimensional precise imaging device, the two data sequences may have a high synchronization correlation. For example, when the temperature of the detection target increases, the detection target may emit more infrared radiation. As the temperature of the detection target increases, the temperature of the internal components of the infrared three-dimensional imaging device, such as the optical element, increases, causing the collected temperature data to also increase. At the same time, since the infrared three-dimensional precise imaging device can convert the received infrared radiation into a visible image, the detector in the imaging device can receive more energy, resulting in a larger current value of the infrared three-dimensional precise imaging device.

[0026] Furthermore, the data is divided according to the change characteristics of each row of data in the three-dimensional imaging device fault monitoring matrix. Specifically, for example, the monitoring data sequence corresponding to the first row of data in the three-dimensional imaging device fault monitoring matrix is ​​the current data sequence , Indicates the first data, will The position number of each data in is taken as the horizontal axis, and The value of each data in is taken as the ordinate, and the curve formed by the abscissa and ordinate is taken as The monitoring data curve is obtained, and all the maximum points and minimum points of the monitoring data curve are obtained, and all the maximum points and minimum points are respectively used as The maximum and minimum split points of The sequence of data between the maximum split point and the minimum split point is The decreasing trend sequence will The sequence of data between the minimum split point and the maximum split point is growth trend sequence.

[0027] Furthermore, the input is a current data sequence , using the Bayesian curve fitting algorithm to obtain The corresponding Bayesian fitting curve is obtained according to the Bayesian fitting curve The slope corresponding to each element in .

[0028] At this point, the division result corresponding to each row of data in the three-dimensional imaging device fault monitoring matrix and the slope corresponding to each element in each row of data can be obtained.

[0029] Furthermore, the synchronization reduction matching factor between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is ​​obtained according to the difference in the reduction trend sequence between the different monitoring data sequences. The specific calculation formula is as follows: In the formula, Indicates monitoring data series and The synchronous increase matching factor between the monitoring data series; and Respectively represent and The monitoring data series The first in the growth trend sequence The collection time corresponding to each data; and Respectively represent and The monitoring data series The first in the growth trend sequence The data corresponds to the slope; Indicates The monitoring data series The number of data in the growth trend series is The monitoring data series The minimum value of the number of data in a growth trend series; Indicates The number of growth trend sequences in the monitoring data series is The minimum value of the number of growth trend sequences in the monitoring data series; It represents the adjustment parameter, and its empirical value is 0.001; is a natural constant.

[0030] If the growth trend of different monitoring data sequences in the three-dimensional imaging equipment fault monitoring matrix is ​​close, the calculated The smaller the value of The larger the value of The larger the value is, the greater the monitoring data series and The growth trends of the three monitoring data series are close.

[0031] Furthermore, the synchronization reduction matching factor between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is ​​obtained according to the difference in the reduction trend sequence between the different monitoring data sequences. The specific calculation formula is as follows: In the formula, Indicates monitoring data series and The synchronization between the monitoring data series reduces the fit factor; and Respectively represent and The monitoring data series The first The collection time corresponding to each data; and Respectively represent and The monitoring data series The first The data corresponds to the slope; Indicates The monitoring data series The number of data in the trend series is reduced by The monitoring data series The minimum value among the number of data in the decreasing trend series; Indicates The number of decreasing trend sequences in the monitoring data series is related to the The minimum value of the number of decreasing trend sequences in the monitored data series; It represents the adjustment parameter, and its empirical value is 0.001; is a natural constant.

[0032] If the decreasing trend between different monitoring data sequences in the three-dimensional imaging equipment fault monitoring matrix is ​​close, the calculated The smaller the value of The larger the value of The larger the value is, the greater the monitoring data series and The decreasing trend changes between the monitoring data series are close.

[0033] Furthermore, the synchronization trend matching correlation coefficient between different monitoring data sequences is calculated based on the analysis results of the growth change trend and the decrease change trend characteristics between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix. Specifically, the sum of the synchronization increase matching factor and the synchronization decrease matching factor between the different monitoring data sequences is used as the first correlation coefficient, the SBD distance between the different monitoring data sequences is used as the second correlation coefficient, and the product of the first correlation coefficient and the second correlation coefficient is used as the synchronization trend matching correlation coefficient between the different monitoring data sequences; if the growth change trend and the decrease change trend characteristics between different monitoring data sequences are close, the larger the value of the synchronization trend matching correlation coefficient between the different monitoring data sequences is.

[0034] So far, the synchronization trend matching correlation coefficients between different monitoring data sequences in the three-dimensional imaging equipment fault monitoring matrix have been obtained.

[0035] Step S003, obtaining a trend matching indirect correlation coefficient according to the synchronous trend matching correlation coefficient, calculating a trend matching comprehensive correlation coefficient according to the synchronous trend matching correlation coefficient and the trend matching indirect correlation coefficient, and obtaining a comprehensive weight correction index according to the trend matching comprehensive correlation coefficient.

[0036] According to the synchronization trend matching correlation coefficients between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix, a trend association undirected graph of the three-dimensional imaging device fault monitoring matrix is ​​constructed, and the indirect correspondence between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is ​​reflected through the trend association undirected graph of the three-dimensional imaging device fault monitoring matrix. Specifically, the sequence composed of the synchronization trend matching correlation coefficients between each monitoring data sequence and all other monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is ​​arranged in order from small to large as the synchronization trend matching correlation sequence of each monitoring data sequence, each monitoring data sequence is taken as a root node, all other monitoring data sequences are taken as child nodes, according to the arrangement order of elements in the synchronization trend matching correlation sequence, the monitoring data sequence corresponding to each element in the synchronization trend matching correlation sequence is taken as a child node, and the binary tree constructed by the root node and all child nodes is taken as the binary tree of each monitoring data sequence.

[0037] Further, each monitoring data sequence in the three-dimensional imaging device fault monitoring matrix is ​​used as a node in the initial undirected graph, and the tree edit distance between the binary trees corresponding to different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is ​​used as the initial weight between different monitoring data sequences. The initial undirected graph of the three-dimensional imaging data matrix is ​​constructed according to the nodes and the initial weights; a sequence consisting of all the initial weights sorted in order from small to large is used as the initial weight sequence of the initial undirected graph, and input as the initial weight sequence, and the segmentation threshold of the initial weight sequence is obtained by using the Otsu threshold segmentation algorithm, and the elements in the initial weight sequence that are less than or equal to the segmentation threshold are used as the true weights of the edges in the initial undirected graph, and the true weights of the edges in the initial undirected graph are retained, and the updated result of the initial undirected graph is used as the trend association undirected graph of the three-dimensional imaging device fault monitoring matrix. The process of obtaining the trend association undirected graph from the initial undirected graph is as follows. Figure 2 shown.

[0038] Furthermore, the trend matching indirect correlation coefficient is calculated according to the corresponding relationship between the non-electrical parameter data sequence and the electrical parameter data sequence in the trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix. The specific calculation formula is as follows: In the formula, Indicates The non-electrical parameter data series and The trend fit indirect correlation coefficient between the electrical parameter data series; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The first The synchronization trend fit correlation coefficient between the monitoring data sequences corresponding to the nodes; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The first The actual weight of the edge between nodes; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The sequence number corresponding to each node; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The number of nodes in a path; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The number of paths between electrical parameter data sequences.

[0039] Jordi The non-electrical parameter data series and The correlation between the electrical parameter data sequences is high, then the calculated The smaller the value of The larger the value of The non-electrical parameter data series and The trend fit indirect correlation coefficient between the electrical parameter data series The larger the value of .

[0040] Furthermore, the sum of the synchronous trend matching correlation coefficient and the trend matching indirect correlation coefficient between each non-electrical parameter data sequence and each electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix is ​​taken as the first characteristic coefficient, the sum of all first characteristic coefficients is taken as the second characteristic coefficient, and the ratio of the first characteristic coefficient to the second characteristic coefficient is taken as the trend matching comprehensive correlation coefficient between each non-electrical parameter data sequence and each electrical parameter data sequence. If the degree of correlation between the non-electrical parameter data sequence and the electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix is ​​large, the larger the value of the calculated first characteristic coefficient is, that is, the larger the trend matching comprehensive correlation coefficient between the calculated non-electrical parameter data sequence and the electrical parameter data sequence is.

[0041] Furthermore, for each electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix, the trend-matching comprehensive correlation coefficient between each electrical parameter data sequence and each other non-electrical parameter data sequence is used as the fusion weight of each non-electrical parameter data sequence, and all the non-electrical parameter data sequences are weightedly fused according to the corresponding fusion weights. The result of the weighted fusion is used as the non-electrical parameter fusion sequence of each electrical parameter data sequence. Based on the non-electrical parameter fusion sequence of each electrical parameter data sequence, the ARIMAX model is used to obtain the predicted value of each element in each electrical parameter data sequence. The specific training process of the ARIMAX model is a well-known technology and will not be repeated here.

[0042] Specifically, for example, the monitoring data sequence corresponding to the first row of data in the three-dimensional imaging device fault monitoring matrix is ​​the current data sequence , the current data sequence is the electrical parameter data sequence of the three-dimensional imaging equipment fault monitoring matrix. Nonparametric data sequences in the fault monitoring matrix of three-dimensional imaging equipment The trend fit comprehensive correlation coefficients between The fusion weight is based on The corresponding fusion weights are weighted fused, and the calculation result of weighted fusion is used as Obtain the non-electrical parameter fusion sequence. The front of each data (The value is 50) The current data corresponding to the acquisition time is arranged in ascending order of time as a sequence The predicted data sequence of each data in the predicted data sequence is a sequence composed of all elements corresponding to the non-electrical parameter fusion sequence in ascending order of time at the acquisition time of all elements in the predicted data sequence. The predicted control sequence for each data in .

[0043] Furthermore, the input is The predicted control sequence and predicted data sequence of each data in the ARIMAX model are used as the exogenous variable sequence of the ARIMAX model. The predicted value of each data in .

[0044] Furthermore, the comprehensive weight correction index of each element is obtained according to the real value and predicted value corresponding to each element in the electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix. The specific calculation formula is as follows: In the formula, Indicates The first The comprehensive weight correction index of the elements; Indicates The first The difference between the true value and the predicted value corresponding to the element; Indicates The maximum value of the difference between the true value and the predicted value corresponding to all elements in the electrical parameter data sequence; Indicates The first The original weights in the local weighted regression algorithm corresponding to the elements; is a natural exponential function.

[0045] If the three-dimensional imaging equipment fault monitoring matrix The first The smaller the difference between the true value and the predicted value corresponding to the element, the The larger the value of The first The comprehensive weight correction index of the elements The larger the value of .

[0046] At this point, the comprehensive weight correction index of each element in the electrical parameter data sequence in the three-dimensional imaging equipment fault monitoring matrix is ​​obtained.

[0047] Step S004, using a local weighted regression algorithm based on the comprehensive weight correction index to obtain a fitting curve for each row of data in the three-dimensional imaging device fault detection matrix, and obtaining a fault detection result of the three-dimensional imaging device based on the fitting curve for each row of data.

[0048] The electrical parameter data sequences in the fault monitoring matrix of the three-dimensional imaging equipment are the current data sequences , voltage data sequence The inputs are and The comprehensive weight correction index corresponding to the elements in and The comprehensive weight correction index corresponding to the element in the formula is used as the weight of the input data, and the Lowess algorithm is used to obtain and The fitting curves of and The fitting curve of and The data at each collection moment is adjusted, and the specific calculation process of the Lowess algorithm is a well-known technology and will not be described in detail.

[0049] Furthermore, the adjusted and As input, a convolutional neural network model is used to obtain the fault detection results of the infrared three-dimensional precise imaging equipment, wherein the fault detection results include but are not limited to current anomalies, voltage anomalies, temperature anomalies, etc., wherein the optimization algorithm is the Adam algorithm, and the loss function is the cross entropy loss function. The specific training process of the convolutional neural network is a well-known technology and will not be described in detail.

[0050] At this point, the fault detection results of the infrared three-dimensional precise imaging equipment have been obtained.

[0051] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligent fault detection of electrical equipment, characterized in that: The method comprises the following steps: Obtaining a three-dimensional imaging equipment fault monitoring matrix; According to the change trend characteristics of each row of data in the three-dimensional imaging device fault monitoring matrix, the division result of each row of data is obtained; according to the division result of each row of data in the three-dimensional imaging device fault monitoring matrix, the synchronization trend matching correlation coefficient between different rows of data in the three-dimensional imaging device fault monitoring matrix is ​​obtained; According to the synchronous trend matching correlation coefficient between different rows of data in the three-dimensional imaging equipment fault monitoring matrix, a trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix is ​​constructed; according to the trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix, a trend matching comprehensive correlation coefficient between non-electrical parameter data and electrical parameter data in the three-dimensional imaging equipment fault monitoring matrix is ​​obtained; according to the trend matching comprehensive correlation coefficient between non-electrical parameter data and electrical parameter data in the three-dimensional imaging equipment fault monitoring matrix, a comprehensive weight correction index of each element in the electrical parameter data sequence in the three-dimensional imaging equipment fault detection matrix is ​​obtained; A fitting curve for each row of data in the three-dimensional imaging device fault monitoring matrix is ​​obtained according to the comprehensive weight correction index of each element in the three-dimensional imaging device fault monitoring matrix, and a CNN neural network model is used based on the fitting curve to obtain the fault monitoring result of the three-dimensional imaging device.

2. A method for intelligent fault detection of electrical equipment according to claim 1, characterized in that: The method for obtaining the division result of each row of data according to the change trend characteristics of each row of data in the three-dimensional imaging device fault monitoring matrix is: For each row of data in the three-dimensional imaging device fault monitoring matrix, the sequence composed of each row of data is used as the monitoring data sequence, the position number of each data in the monitoring data sequence is used as the horizontal coordinate, the size of each data value in the monitoring data sequence is used as the vertical coordinate, and the curve formed by the horizontal coordinate and the vertical coordinate is used as the monitoring data curve of the monitoring data sequence, and all the maximum points and minimum points of the monitoring data curve are obtained, and all the maximum points and minimum points are used as the maximum segmentation points and the minimum segmentation points of the monitoring data sequence respectively, and the sequence composed of all the maximum segmentation points and the minimum segmentation points in ascending time order is used as the segmentation sequence, and according to the segmentation sequence, in order from left to right, the sequence composed of the corresponding data in the monitoring data sequence between the adjacent maximum segmentation points and the minimum segmentation points is used as the decreasing trend sequence of the monitoring data sequence, and the sequence composed of the corresponding data in the monitoring data sequence between the adjacent minimum segmentation points and the maximum segmentation points is used as the increasing trend sequence of the monitoring data sequence.

3. The intelligent fault detection method for electrical equipment according to claim 1, characterized in that: The method for obtaining the synchronization trend matching correlation coefficient between different rows of data in the three-dimensional imaging device fault monitoring matrix according to the division result of each row of data in the three-dimensional imaging device fault monitoring matrix is: For each row of data in the three-dimensional imaging device fault monitoring matrix, the sequence composed of each row of data is used as a monitoring data sequence, a Bayesian curve fitting algorithm is used to obtain a Bayesian fitting curve corresponding to the monitoring data sequence, and a slope corresponding to each element in the monitoring data sequence is obtained according to the Bayesian fitting curve; Obtaining a synchronous increase matching factor between different monitoring data sequences according to the difference in growth trend sequences between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix; Acquire a synchronization reduction fit factor between different monitoring data sequences according to the difference in reduction trend sequence between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix; The sum of the synchronous increase fit factor and the synchronous decrease fit factor between the different monitoring data sequences is taken as the first correlation coefficient, the SBD distance between the different monitoring data sequences is taken as the second correlation coefficient, and the product of the first correlation coefficient and the second correlation coefficient is taken as the synchronous trend fit correlation coefficient between the different monitoring data sequences.

4. A method for intelligent fault detection of electrical equipment according to claim 3, characterized in that: The calculation method for obtaining the synchronous increase matching factor between different monitoring data sequences according to the difference in the growth trend sequence between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is: In the formula, Indicates monitoring data series and The synchronous increase matching factor between the monitoring data series; and Respectively represent and The monitoring data series The first in the growth trend sequence The collection time corresponding to each data; and Respectively represent and The monitoring data series The first in the growth trend sequence The data corresponds to the slope; Indicates The monitoring data series The number of data in the growth trend series is The monitoring data series The minimum value of the number of data in a growth trend series; Indicates The number of growth trend sequences in the monitoring data series is The minimum value of the number of growth trend sequences in the monitoring data series; represents the adjustment parameter; is a natural constant.

5. The intelligent fault detection method for electrical equipment according to claim 3, characterized in that: The calculation method for obtaining the synchronous reduction fit factor between different monitoring data sequences according to the difference in the reduction trend sequence between different monitoring data sequences in the three-dimensional imaging device fault monitoring matrix is: In the formula, Indicates monitoring data series and The synchronization between the monitoring data series reduces the fit factor; and Respectively represent and The monitoring data series The first The collection time corresponding to each data; and Respectively represent and The monitoring data series The first The data corresponds to the slope; Indicates The monitoring data series The number of data in the trend series is reduced by The monitoring data series The minimum value among the number of data in the decreasing trend series; Indicates The number of decreasing trend sequences in the monitoring data series is related to the The minimum value of the number of decreasing trend sequences in the monitored data series; represents the adjustment parameter; is a natural constant.

6. A method for intelligent fault detection of electrical equipment according to claim 1, characterized in that: The method for constructing a trend-related undirected graph of a three-dimensional imaging device fault monitoring matrix according to the synchronization trend matching correlation coefficient between different rows of data in the three-dimensional imaging device fault monitoring matrix is: For each row of data in the three-dimensional imaging device fault monitoring matrix, a sequence composed of each row of data is used as a monitoring data sequence, a sequence composed of synchronization trend matching correlation coefficients between the monitoring data sequence and all other monitoring data sequences is sorted in ascending order as a synchronization trend matching correlation sequence of the monitoring data sequence, and a binary tree of the monitoring data sequence is constructed according to the synchronization trend matching correlation sequence; Each monitoring data sequence is regarded as a node in an undirected graph, the tree edit distance between the binary trees corresponding to the two monitoring data sequences is regarded as the initial weight between the corresponding two nodes, and the undirected graph constructed based on all monitoring data sequences, the initial weights of different monitoring data sequences and the undirected graph is regarded as the trend association undirected graph of the fault monitoring matrix of the three-dimensional imaging equipment.

7. The intelligent fault detection method for electrical equipment according to claim 1, characterized in that: The method for obtaining the trend matching comprehensive correlation coefficient between the non-electrical parameter data and the electrical parameter data in the three-dimensional imaging device fault monitoring matrix according to the trend correlation undirected graph of the three-dimensional imaging device fault monitoring matrix is: Each row of data corresponding to the non-electrical parameter data in the three-dimensional imaging device fault monitoring matrix is ​​used as a non-electrical parameter data sequence of the three-dimensional imaging device fault monitoring matrix, and each row of data corresponding to the electrical parameter data in the three-dimensional imaging device fault monitoring matrix is ​​used as an electrical parameter data sequence of the three-dimensional imaging device fault monitoring matrix; Calculate the trend matching indirect correlation coefficient according to the corresponding relationship between the non-electrical parameter data sequence and the electrical parameter data sequence in the trend correlation undirected graph of the three-dimensional imaging equipment fault monitoring matrix; The sum of the synchronous trend matching correlation coefficient and the trend matching indirect correlation coefficient between each non-electrical parameter data sequence and each electrical parameter data sequence in the three-dimensional imaging equipment fault monitoring matrix is ​​taken as the first characteristic coefficient, the sum of all the first characteristic coefficients is taken as the second characteristic coefficient, and the ratio of the first characteristic coefficient to the second characteristic coefficient is taken as the comprehensive correlation coefficient of the trend matching between each non-electrical parameter data sequence and each electrical parameter data sequence.

8. A method for intelligent fault detection of electrical equipment according to claim 7, characterized in that: The method for calculating the trend matching indirect correlation coefficient according to the correspondence between the non-electrical parameter data sequence and the electrical parameter data sequence in the trend correlation undirected graph of the three-dimensional imaging device fault monitoring matrix is: In the formula, Indicates The non-electrical parameter data series and The trend fit indirect correlation coefficient between the electrical parameter data series; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The first The synchronization trend fit correlation coefficient between the monitoring data sequences corresponding to the nodes; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The first The actual weight of the edge between nodes; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The first The sequence number corresponding to each node; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The first The number of nodes in a path; Represents the trend correlation in an undirected graph. The non-electrical parameter data series and The number of paths between electrical parameter data sequences.

9. The intelligent fault detection method for electrical equipment according to claim 1, characterized in that: The method for obtaining the comprehensive weight correction index of each element in the electrical parameter data sequence in the three-dimensional imaging device fault detection matrix according to the trend matching comprehensive correlation coefficient between the non-electrical parameter data and the electrical parameter data in the three-dimensional imaging device fault monitoring matrix is: For each electrical parameter data sequence in the three-dimensional imaging device fault monitoring matrix, the trend matching comprehensive correlation coefficient between each electrical parameter data sequence and each other non-electrical parameter data sequence is used as the fusion weight of each non-electrical parameter data sequence, all the non-electrical parameter data sequences are weightedly fused according to the corresponding fusion weights, and the weighted fusion result is used as the non-electrical parameter fusion sequence of each electrical parameter data sequence, and the predicted value of each element in each electrical parameter data sequence is obtained by using the ARIMAX model based on the non-electrical parameter fusion sequence of each electrical parameter data sequence; The comprehensive weight correction index of each element in each electrical parameter data sequence is obtained according to the true value and the predicted value corresponding to each element in each electrical parameter data sequence.

10. A method for intelligent fault detection of electrical equipment according to claim 9, characterized in that: The method for obtaining the comprehensive weight correction index of each element in each electrical parameter data sequence according to the true value and the predicted value corresponding to each element in each electrical parameter data sequence is: In the formula, Indicates The first The comprehensive weight correction index of the elements; Indicates The first The difference between the true value and the predicted value corresponding to the element; Indicates The maximum value of the difference between the true value and the predicted value corresponding to all elements in the electrical parameter data sequence; Indicates The first The original weights in the local weighted regression algorithm corresponding to the elements; is a natural exponential function.

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