Turnout power curve state monitoring method and apparatus

Abstract

The present application relates to the technical field of rail transit device fault monitoring, and in particular to a turnout power curve state monitoring method and apparatus. The method comprises: acquiring first feature information of a real-time turnout action power curve; acquiring second feature information of turnout action power curves in different operating states; on the basis of a grey relational analysis method and / or a fuzzy clustering method, calculating the similarity between the first feature information and second feature information corresponding to each fault type; and on the basis of the similarity, determining an operating state corresponding to the real-time turnout action power curve. The present application provides an intelligent railway device electrical characteristic curve abnormality diagnosis method and system, which have a higher accuracy, automation degree and robustness. By using a fault diagnosis method based on digital signal processing, fault features of turnout action power curve data are effectively extracted, thereby meeting various fault detection requirements, simplifying the structure of a classifier, and improving the precision and efficiency of fault diagnosis.

Description

A method and device for monitoring turnout power curve status Technical Field

[0001] The present application relates to the technical field of rail transit equipment fault monitoring, and in particular to a method and device for monitoring the status of a turnout power curve. Background Art

[0002] The electrical characteristic curve of railway equipment is a key indicator of its electrical performance and is crucial for monitoring its operational status and diagnosing faults. Turnouts, one of the three major outdoor components of railway signaling equipment, are the connecting devices that allow locomotives and rolling stock to switch from one track to another. They are also a weak link in the track system and are typically installed in large numbers at stations and marshalling yards to maximize the line's throughput capacity, significantly impacting the safety and efficiency of railway transportation. Therefore, analyzing the turnout's operating power curve is crucial to determine whether the turnout equipment is operating properly, whether any anomalies exist, and the type of potential faults. However, current railway signal monitoring systems rely on regular turnout inspections and maintenance at designated maintenance points to ensure safe and reliable turnout operation. This maintenance approach is not only inefficient and labor-intensive, but also relies solely on worker experience for fault identification, which can lead to misdiagnosis and missed detections, compromising operational safety.

[0003] A popular approach currently involves diagnosing electrical characteristic curve anomalies based on machine learning and data mining techniques. This approach utilizes extensive historical data for training and constructs models to diagnose electrical characteristic curve anomalies. The basic process is as follows: data acquisition and preprocessing; feature extraction and selection; model construction and training; and anomaly identification and diagnosis. While this existing solution improves the accuracy of electrical characteristic curve anomaly diagnosis to a certain extent, it still suffers from the following deficiencies:

[0004] For new abnormal situations, the model's generalization ability is limited and may not be able to accurately identify and diagnose; during data collection and preprocessing, the handling of noise and interference is not perfect enough, which may affect the reliability of the diagnosis results; the feature extraction and selection methods may be limited by manual experience and lack automated and intelligent feature selection capabilities. Summary of the Invention

[0005] The present application aims to solve at least one of the technical problems in the above-mentioned technologies to a certain extent. To this end, a method for monitoring the power curve state of a turnout is proposed, comprising:

[0006] Acquire first characteristic information of a real-time turnout operation power curve;

[0007] Obtaining second characteristic information of the turnout operation power curve under different operating conditions;

[0008] Calculating the similarity between the first feature information and the second feature information corresponding to each fault type based on a grey relational method and / or a fuzzy clustering method;

[0009] The operating state corresponding to the real-time switch action power curve is determined according to the similarity.

[0010] Preferably, obtaining the first characteristic information of the real-time turnout operation power curve includes:

[0011] Obtaining an IMF component of the real-time turnout operation power curve;

[0012] Obtain the fuzzy entropy of the IMF component as the first feature information.

[0013] Preferably, obtaining the IMF component of the real-time turnout operation power curve includes: decomposing the real-time turnout operation power curve based on CEEMD to obtain the IMF component.

[0014] Preferably, after determining the operating state corresponding to the real-time turnout action power curve according to the similarity, the method further includes:

[0015] Constructing a similarity set based on several similarities obtained by calculation;

[0016] Sorting the similarity sets from largest to smallest according to similarity;

[0017] Determine whether the operating states corresponding to the first several similarities are all normal or all abnormal; if not, determine the difference between the first several similarities;

[0018] When it is determined that the difference between the first several similarities is less than a threshold value, a secondary judgment is made on the operating state corresponding to the real-time turnout action power curve based on at least one method among the year-on-year value method, the year-on-year amplitude method and the chain ratio value method.

[0019] Preferably, judging the operating state corresponding to the real-time turnout action power curve based on the ratio method includes:

[0020] Obtain the maximum value of several normal turnout operation power curves at the target time;

[0021] Correcting the maximum value based on a preset static threshold value to obtain a reference maximum value;

[0022] It is determined whether the value of the real-time switch operation power curve at the target time is greater than the reference maximum value; if the determination result is yes, it is determined that the real-time switch operation power curve has a sudden increase at the target time.

[0023] Preferably, judging the operating state corresponding to the real-time turnout action power curve based on the year-on-year amplitude method includes:

[0024] Obtain the maximum amplitude of several normal turnout operation power curves at the target time;

[0025] Correcting the maximum amplitude based on a preset static threshold to obtain a reference maximum amplitude;

[0026] It is determined whether the amplitude of the real-time switch operation power curve at the target time is greater than zero and greater than the reference maximum value. If the determination result is yes, it is determined that the real-time switch operation power curve has a sudden increase at the target time.

[0027] Preferably, judging the operating state corresponding to the real-time turnout action power curve based on the ratio value method includes:

[0028] Obtaining target data of the real-time turnout operation power curve at a target time;

[0029] Obtaining a difference between the target data and each data within a preset time window before the target moment, and determining the number of differences that are greater than a preset static threshold;

[0030] When it is determined that the difference number is greater than the threshold, the target data is determined to be abnormal.

[0031] Preferably, the static threshold is determined based on historical turnout action power curve data.

[0032] This application also proposes a turnout power curve condition monitoring system, comprising:

[0033] A first acquisition module is used to acquire first characteristic information of a real-time turnout operation power curve;

[0034] A second acquisition module is used to obtain second characteristic information of the turnout operation power curve under different operating conditions;

[0035] A first calculation module is used to calculate the similarity between the first characteristic information and the second characteristic information corresponding to each fault type based on a grey relational method and / or a fuzzy clustering method;

[0036] A state determination module is used to determine the operating state corresponding to the real-time switch action power curve according to the similarity.

[0037] The present application also proposes a computer-readable storage medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, it is at least used to implement the above method.

[0038] Compared with the existing technology, the beneficial effects of the present application are: the present application provides an intelligent method and system for diagnosing abnormalities in the electrical characteristic curves of railway equipment, which has higher accuracy, automation and robustness. By adopting a fault diagnosis method based on digital signal processing, the fault characteristics of the turnout action power curve data can be effectively extracted, meeting various fault detection requirements, simplifying the classifier structure, and improving the accuracy and efficiency of fault diagnosis.

[0039] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0040] The technical solution of the present application is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0042] FIG1 is a schematic diagram of a turnout power curve condition monitoring method provided in this application;

[0043] FIG2 is a flow chart of feature information extraction based on CEEMD provided in an embodiment;

[0044] FIG3 is a flow chart of obtaining IMF components by decomposing a signal using CEEMD according to an embodiment;

[0045] FIG4 is a flowchart of fault diagnosis based on gray association and fuzzy clustering according to an embodiment;

[0046] FIG5 is a flowchart of a fault diagnosis method based on grey correlation according to an embodiment;

[0047] FIG6 is a flow chart of a fault diagnosis method based on fuzzy clustering according to an embodiment.

[0048] FIG7 is a flowchart of enhanced fault diagnosis according to an embodiment;

[0049] FIG8 is a schematic diagram of a method for monitoring the turnout power curve state according to an embodiment;

[0050] FIG9 is a schematic diagram of a turnout power curve condition monitoring system according to an embodiment;

[0051] FIG10 is a schematic diagram of a computer-readable storage medium according to an embodiment. DETAILED DESCRIPTION

[0052] The present application is described below in conjunction with the accompanying drawings. The preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0053] FIG1 is a turnout power curve condition monitoring method provided in this application, including:

[0054] S11 obtains the first characteristic information of the real-time turnout action power curve;

[0055] S12 obtains the second characteristic information of the turnout action power curve under different operating conditions;

[0056] S13. Based on the grey relational method and / or the fuzzy clustering method, calculating the similarity between the first characteristic information and the second characteristic information corresponding to each fault type;

[0057] S14. Determine the operating state corresponding to the real-time switch operation power curve based on the similarity.

[0058] According to some embodiments of the present application, step S11 obtains first characteristic information of the real-time switch operation power curve, including extracting characteristic information corresponding to the real-time switch operation power curve based on CEEMD multi-scale fuzzy entropy, and the scheme is as follows:

[0059] This feature extraction method uses the complementary set empirical mode decomposition (CEEMD) algorithm to decompose the turnout power curve, obtaining a set of intrinsic mode functions (IMFs) with varying time scales, ranging from high to low frequency. This method achieves adaptive multiscaling of the original signal. The fuzzy entropy of each IMF component is then extracted as a fault diagnosis feature parameter, thereby constructing a fault diagnosis feature set. This process is illustrated in Figure 2.

[0060] (1) CEEMD decomposition to obtain IMF components

[0061] For a given original signal x(t), the number of complementary set empirical mode decompositions (EMDs) is first initialized M. Then, a set of positive and negative paired white noise sequences n(t) is added to the original signal x(t). The EMD decomposition is performed M times on the original signal x(t). The results of the M EMD decompositions are summed and averaged to eliminate the influence of white noise, thereby obtaining the final IMF component. The above process is shown in Figure 3:

[0062] (2) Fuzzy entropy of IMF.

[0063] The definition of fuzzy entropy is as follows:

[0064] Among them, m is the pattern dimension, r is the similarity tolerance parameter, N is the length of the original data, It is the relationship dimension under n dimension.

[0065] The original signal x(t) is decomposed using CEEMD to obtain intrinsic mode functions (IMFs) at different time scales, from high to low frequency, achieving adaptive multiscaling of the original signal. The fuzzy entropy of each IMF component after decomposition is extracted as a characteristic parameter of the original signal, i.e., the multiscale fuzzy entropy of the original signal, thereby constructing a feature set for turnout fault diagnosis.

[0066] According to some embodiments of the present application, step S12 obtains the second characteristic information of the turnout operation power curve under different operating states in the same principle as step S11, and extracts the characteristic information of the turnout operation power curve under a known operating state.

[0067] According to some embodiments of the present application, step S13 calculates the similarity between the first feature information and the second feature information corresponding to each fault type based on a grey relational method and / or a fuzzy clustering method, including the following schemes:

[0068] The diagnostic method based on grayscale correlation and fuzzy clustering draws on the heterogeneous integration concept in ensemble learning to combine two simple and fast weak diagnostic tools into a strong diagnostic tool. The grayscale correlation method will obtain the "correlation degree" of different fault types, and the fuzzy clustering method will obtain the "membership degree" of different fault types. The results obtained by both methods are numerical values ​​between 0 and 1. This system weights the results of the two methods by a certain proportion to obtain a new diagnostic basis "comprehensive similarity". It is used to diagnose whether the switch action power curve has abnormalities. It can significantly improve the accuracy of abnormality diagnosis and reduce the number of missed alarms in the railway signal centralized monitoring system. The above process is shown in Figure 4. The solution includes:

[0069] (1) Fault identification method based on grey correlation

[0070] As shown in Figure 5, the process of the fault identification method based on grey correlation includes: establishing a fault diagnosis feature set X, setting the feature quantity of the sample to be identified, calculating the feature quantity of the sample to be identified and the feature vector X in the feature set X. i The absolute difference between them is calculated, and the minimum and maximum values ​​of the absolute difference are obtained. The grey correlation coefficient between the feature quantity of the sample to be identified and the feature set X is calculated. According to the grey correlation coefficient, the grey correlation degree between the feature quantity of the sample to be identified and each diagnostic feature vector is calculated respectively. According to the size of the “grey correlation degree”, it is determined which type of fault the current turnout fault power curve belongs to in the fault feature set.

[0071] (2) Fault identification method based on fuzzy clustering

[0072] As shown in Figure 6, this fault identification method first requires the establishment of the original characteristic pattern matrix X, which is composed of the eigenvalues ​​of the normal power curve, multiple typical fault power curves, and the curve to be tested. Matrix X is then standardized through the translation standard deviation transformation and the translation range transformation, so that the mean of each variable is 0 and the standard deviation is 1, thereby eliminating the influence of different dimensions on the data. Using the distance method to calculate the original matrix X, the fuzzy similarity matrix R can be obtained. Then, the fuzzy equivalent matrix R* with transitivity is calculated. Then, when λ changes from 1 to 0 in *R, a corresponding Boolean matrix is ​​formed, thus forming a dynamic clustering graph. Based on the size of the "membership degree" in the dynamic graph, it is determined to which type of fault in the fault feature set the current turnout fault power curve belongs.

[0073] According to some embodiments of the present application, when the top two rankings of the "comprehensive similarity" of the diagnostic results are a certain fault type and a normal type (the two are not distinguished in the order of ranking), and the "comprehensive similarity" is very close, there is a high probability that a misjudgment may occur. In order to avoid misjudgment, when this situation occurs, the diagnostic judgment can be further strengthened, which is equivalent to adding a layer of misjudgment protection, further improving the accuracy of abnormal diagnosis. In this embodiment, three simple and fast weak diagnostic devices, namely the year-on-year value method diagnostic device, the year-on-year amplitude method diagnostic device, and the ring-ratio value method diagnostic device, are combined into a strong diagnostic device. When the diagnostic system encounters a situation that is prone to misjudgment, it helps it further analyze it. The specific process is shown in Figure 7.

[0074] (1) Comparative Value Method (CV)

[0075] Select the data of the switch action power curve corresponding to the past P normal switch operations. At a certain moment, P points can be obtained as reference values, which we record as D i (t), where i = 1, ..., P. The static threshold T can be used to determine whether the data of the turnout power curve at a certain moment is D i (t) Is there any abnormality (sudden increase / decrease). If D i If (t) is smaller than the minimum value of the same moment in the past P times multiplied by a threshold T, the data at that moment will be considered as an abnormal point (sudden decrease); and if D i (t) is greater than the maximum value of the same moment in the past P times multiplied by a threshold T, then the data D at that moment is considered i (t) is the abnormal point (sudden increase), and the specific calculation formula is as follows:

[0076] Among them, Result represents the judgment result, max_T represents the maximum threshold T, min_ represents the minimum threshold T, min(·) represents the maximum value of ·, and max(·) represents the minimum value of ·.

[0077] (2) Common Amplitude Method (CA)

[0078] Select the data of the switch action power curve corresponding to the past P normal switch operations, and for a certain moment, P amplitude values ​​can be obtained. Using the absolute value of the P amplitudes as the standard, if the amplitude Damp(t) at time t is greater than the maximum amplitude at the same time in the past P days multiplied by a threshold T, and the amplitude at time t is greater than 0, then it is considered that an abnormality (sudden increase) has occurred at that moment. If the amplitude Damp(t) at time t is greater than the maximum amplitude at the same time in the past P days multiplied by a threshold T, and the amplitude at time m is less than 0, then it is considered that an abnormality (sudden decrease) has occurred at that moment. The specific calculation formula is as follows:

[0079] (3) Chain value method (SS)

[0080] Set the time window size to W, then take the detection value D(t) at time t and the past W (denoted as D j ) is compared with the data value at the time point. If it is greater than the threshold T, we will increase the count by 1. If the count exceeds the count_num we set, the point is considered an outlier. The specific calculation formula is as follows:

[0081] According to some embodiments of the present application, the threshold T is set by referring to the average value avg, the maximum value max, and the minimum value min over a period of time (such as the size within a window), and then taking the minimum value of max-avg and avg-min. The specific calculation formula is as follows: T = min(max-avg,avg-min)

[0082] The above three methods usually select N moments, N ≥ 3n, and these N moments are the same number selected in the three time periods of starting, during and ending the turnout. When one or more moments are abnormal, it is determined to be abnormal.

[0083] FIG8 is a method and system for diagnosing abnormalities of the electrical characteristic curve (turnout operation power curve) of railway signaling equipment based on CEEMD multi-scale fuzzy entropy feature extraction and heterogeneous integration ideas, as provided in an embodiment. The complete process is as follows:

[0084] Curves for different fault types are obtained from the Centralized Railway Signal Monitoring System (CSM) database, and the turnout operation power curve currently collected by the station machine is obtained from the Centralized Railway Signal Monitoring System station machine. Both are then subjected to CEEMD decomposition to obtain their corresponding IMF components. A standard fault feature set and the feature quantities of the sample to be diagnosed are constructed. The correlation and membership between the standard fault feature set and the feature quantities of the sample to be diagnosed are calculated using grey correlation and fuzzy clustering. A weighted comprehensive similarity is then obtained to determine the operating status. Next, the highest comprehensive similarities are determined to determine whether they correspond to the same operating status type. If so, the current judgment is correct, and the presence of a fault and the type of fault are determined. If not, the diagnostic results are further analyzed using the aforementioned diagnostic device to ultimately determine the fault type.

[0085] Based on the same inventive concept, as shown in FIG9 , this application also proposes a switch power curve state monitoring system, comprising: a first acquisition module 91 for acquiring first characteristic information of a real-time switch operation power curve; a second acquisition module 92 for acquiring second characteristic information of the switch operation power curve under different operating conditions; a first calculation module 93 for calculating the similarity between the first characteristic information and the second characteristic information corresponding to each fault type based on a gray correlation method and / or a fuzzy clustering method; and a state determination module 94 for determining the operating state corresponding to the real-time switch operation power curve based on the similarity. As shown in FIG10 , this application also provides a computer-readable storage medium 1100 , which stores a computer program or instructions. When executed by a processor, the computer program or instructions are used to implement at least the above-described method.

[0086] It is obvious that those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if such modifications and variations fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.

Claims

1. A method for monitoring the status of a turnout power curve, characterized in that: include: Acquire first characteristic information of a real-time turnout operation power curve; Obtaining second characteristic information of the turnout operation power curve under different operating conditions; Calculating the similarity between the first feature information and the second feature information corresponding to each fault type based on a grey relational method and / or a fuzzy clustering method; The operating state corresponding to the real-time switch action power curve is determined according to the similarity.

2. The method according to claim 1, wherein Obtaining the first characteristic information of the real-time turnout operation power curve includes: Obtaining an IMF component of the real-time turnout operation power curve; Obtain the fuzzy entropy of the IMF component as the first feature information.

3. The method according to claim 2, wherein Obtaining the IMF component of the real-time turnout operation power curve includes: decomposing the real-time turnout operation power curve based on CEEMD to obtain the IMF component.

4. The method according to claim 3, wherein After determining the operating state corresponding to the real-time turnout action power curve according to the similarity, the method further includes: Constructing a similarity set based on several similarities obtained by calculation; Sorting the similarity sets from largest to smallest according to similarity; Determine whether the operating states corresponding to the first several similarities are all normal or all abnormal; if not, determine the difference between the first several similarities; When it is determined that the difference between the first several similarities is less than a threshold value, a secondary judgment is made on the operating state corresponding to the real-time turnout action power curve based on at least one method among the year-on-year value method, the year-on-year amplitude method and the chain ratio value method.

5. The method according to claim 4, wherein The operation state corresponding to the real-time turnout operation power curve is judged based on the comparative value method, including: Obtain the maximum value of several normal turnout operation power curves at the target time; Correcting the maximum value based on a preset static threshold value to obtain a reference maximum value; It is determined whether the value of the real-time switch operation power curve at the target time is greater than the reference maximum value; if the determination result is yes, it is determined that the real-time switch operation power curve has a sudden increase at the target time.

6. The method according to claim 4, wherein The corresponding operating state of the real-time turnout operation power curve is judged based on the year-on-year amplitude method, including: Obtain the maximum amplitude of several normal turnout operation power curves at the target time; Correcting the maximum amplitude based on a preset static threshold to obtain a reference maximum amplitude; It is determined whether the amplitude of the real-time switch operation power curve at the target time is greater than zero and greater than the reference maximum value. If the determination result is yes, it is determined that the real-time switch operation power curve has a sudden increase at the target time.

7. The method according to claim 4, wherein The operation state corresponding to the real-time turnout operation power curve is judged based on the ratio value method, including: Obtaining target data of the real-time turnout operation power curve at a target time; Obtaining a difference between the target data and each data within a preset time window before the target moment, and determining the number of differences that are greater than a preset static threshold; When it is determined that the difference number is greater than the threshold, the target data is determined to be abnormal.

8. The method according to any one of claims 5 to 7, wherein: The static threshold is determined based on historical turnout operation power curve data.

9. A turnout power curve status monitoring system, characterized in that: include: A first acquisition module is used to acquire first characteristic information of a real-time turnout operation power curve; A second acquisition module is used to obtain second characteristic information of the turnout operation power curve under different operating conditions; A first calculation module is used to calculate the similarity between the first characteristic information and the second characteristic information corresponding to each fault type based on a grey relational method and / or a fuzzy clustering method; A state determination module is used to determine the operating state corresponding to the real-time switch action power curve according to the similarity.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program or instructions, which, when executed by a processor, are used to at least implement the method according to any one of claims 1 to 8.

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