Automatic determination method, determination device and maintenance support system for turnout equipment failure

Abstract

The present application provides a method for automatically determining a fault in a turnout device, a determination device and a maintenance support system, the method comprising: a method for automatically determining a fault in a turnout device, the method comprising: step S1: when the turnout device operates normally, establishing a turnout operation current reference curve; step S2: dividing the time domain of the turnout operation current into a start-unlock time domain, a conversion time domain, and a lock-and-release time domain; step S3: obtaining a start-unlock time domain reference current array, a conversion time domain reference current array, and a lock-and-release time domain reference current array; step S4: obtaining a start-unlock time domain sampling current array, a conversion time domain sampling current array, and a lock-and-release time domain sampling current array; step S5: respectively calculating the linear correlation coefficients of each time domain reference current array and the sampling current array; step S6: comparing the linear correlation coefficient obtained in step S5 with the corresponding preset linear correlation coefficient threshold to determine whether the turnout device has a fault.

Description

Technical Field

[0001] The present application relates to the technical field of turnout equipment, and in particular to an automatic fault determination method and determination device of turnout equipment and a maintenance support system. Background Art

[0002] At present, most methods for determining turnout equipment failures require manual self-determination of whether the turnout equipment has failed or manual monitoring and judgment in combination with monitoring equipment. The methods have the advantages of low work efficiency, poor reliability, and complex methods with low practicality. They are no longer suitable for the requirements of the rapid development of high-speed railways. How to quickly and accurately determine turnout failures is an important measure to ensure driving safety and passenger life safety.

[0003] Therefore, it is necessary to improve the current method for determining the fault of the turnout equipment and solve the above-mentioned problems. Summary of the invention

[0004] In view of the problems of low working efficiency, poor reliability, and complex method and low practicality in the method for determining turnout equipment failure in the prior art, the first aspect of the present application provides an automatic determination method for turnout equipment failure, the automatic determination method comprising:

[0005] The automatic determination method comprises:

[0006] Step S1: When the turnout equipment operates normally, a turnout operation current reference curve is established;

[0007] Step S2: dividing the time domain of the turnout action current into at least three time domains according to the turnout action current reference curve, namely, a start unlocking time domain, a conversion time domain, and a locking and releasing time domain;

[0008] Step S3: obtaining the current value within each time domain range to obtain a start-unlock time domain reference current array, a conversion time domain reference current array, and a lock-release time domain reference current array;

[0009] Step S4: when the turnout equipment is working, the turnout action current data of the turnout equipment is collected online in real time, and the turnout action current is divided according to the time domain to obtain a start-unlock time domain sampling current array, a conversion time domain sampling current array, and a lock-release time domain sampling current array;

[0010] Step S5: calculating the linear correlation coefficient between the start-unlock time domain reference current array and the start-unlock time domain sampling current array, calculating the linear correlation coefficient between the conversion time domain reference current array and the conversion time domain sampling current array, and calculating the linear correlation coefficient between the lock-and-release time domain reference current array and the lock-and-release time domain sampling current array;

[0011] Step S6: Compare the linear correlation coefficient obtained in step S5 with the corresponding preset linear correlation coefficient threshold. If any linear correlation coefficient obtained in step S5 is less than the corresponding preset linear correlation coefficient threshold, it is directly determined that the turnout equipment fails within the time domain.

[0012] Optionally, the step S1 includes:

[0013] Step S11: determining a sampling period of the turnout action current according to the factory turnout action current curve of the turnout equipment;

[0014] Step S12: sampling the turnout action current when the turnout equipment is in normal action for multiple times within the sampling period to obtain multiple samples and acquire time and current data of each sample to form multiple groups of action current time-current two-dimensional arrays;

[0015] Step S13: according to the time-current two-dimensional array, a turnout action current reference curve is established with the turnout action time as the abscissa and the discrete current data as the ordinate.

[0016] Optionally, the step S1 includes:

[0017] Step S11: determining a sampling period of the turnout action current according to the factory turnout action current curve of the turnout equipment;

[0018] Step S12: sampling the turnout action current when the turnout equipment is in normal action for multiple times within the sampling period to obtain multiple samples and acquire time and current data of each sample to form multiple groups of action current time-current two-dimensional arrays, and performing weighted synthesis operation on the multiple groups of time-current two-dimensional arrays to generate weighted time-current two-dimensional arrays;

[0019] Step S13: establishing a turnout action current reference curve based on the weighted time-current two-dimensional array, with the turnout action time as the abscissa and the discrete current data as the ordinate.

[0020] Optionally, the step S4 includes:

[0021] According to the sampling period of the turnout action current, the turnout action current data of the turnout equipment is collected online in real time, and the turnout action current data collected in real time is converted into a time-current two-dimensional array, and the turnout action current is divided according to the time domain to obtain a start-unlock time domain sampling current array, a conversion time domain sampling current array, and a lock-release time domain sampling current array.

[0022] Optionally, step S2 includes:

[0023] Determine the starting time point and the ending time point of the start unlocking time domain, the conversion time domain and the locking and releasing time domain respectively;

[0024] The time domain of the turnout action current is divided into the start unlocking time domain, the conversion time domain and the locking and releasing time domain according to the starting time point and the ending time point of each time domain.

[0025] Optionally, in the step S5, the linear correlation coefficients of the sampled current arrays and the reference current arrays in each time domain are calculated in sequence from the start-unlocking time domain, the conversion time domain to the locking and releasing time domain.

[0026] Optionally, in step S6, if it is determined that the turnout equipment fails in one of the time domain ranges, it is no longer determined whether a failure occurs in other time domain ranges.

[0027] Optionally, the linear correlation coefficient is a Pearson product-moment correlation coefficient.

[0028] Optionally, in step S6, if any linear correlation coefficient obtained in step S5 is greater than or equal to the corresponding preset linear correlation coefficient threshold, the difference between the mean of the sampled current array within the time domain range and the mean of the reference current array in the corresponding time domain is calculated, and if the difference is greater than the preset threshold, it is determined that the turnout equipment has a fault within the time domain range.

[0029] The present application also provides an automatic determination device for turnout equipment failure, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the automatic determination method as described above when executing the computer program.

[0030] The present application also provides a maintenance support system, including the automatic determination device for turnout equipment failure as described above.

[0031] The present application proposes a method for automatically determining a turnout equipment fault. The method divides the turnout action current reference curve into time domains according to the characteristics of the turnout action current reference curve, and calculates the linear correlation coefficient (e.g., PEARSON correlation coefficient) between the sampling current array of the turnout action current collected online and the reference current array generated by the system in each time domain, and automatically determines whether the turnout equipment has a fault based on this linear correlation coefficient. This method can help maintenance personnel to timely discover turnout equipment faults when the turnout action current data collected by the signal maintenance support system (Maintenance Suppoort System, MSS) is collected, thereby improving the work efficiency of maintenance personnel.

[0032] The advantages of this application are:

[0033] 1. Online measurement of turnout action current reference curve makes the reference basis for turnout fault judgment more reliable.

[0034] 2. Divide the turnout action current reference curve into time domain, calculate the Pearson product-moment correlation coefficient (Pearson correlation coefficient) between the sampled current array in each time domain and the corresponding time domain reference current array, and use this coefficient as the basis to judge the similarity between the current sampled current curve and the reference current curve, so as to determine whether there is a fault in the turnout, and the result is more accurate;

[0035] 3. Compared with other methods, this judgment method is simpler and more effective, and easier to implement in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The following drawings of the present application are hereby used as part of the present application for understanding the present application. The drawings show the embodiments of the present application and their descriptions, and are used to explain the device and principle of the present application. In the drawings,

[0037] Figure 1 A process flow chart of the automatic determination method of turnout equipment failure in this application;

[0038] Figure 2 This is a schematic diagram of the turnout action current curve in this application;

[0039] Figure 3 This is an example of a sample action current curve of a single-action turnout in an embodiment of the present application;

[0040] Figure 4 This is a flowchart of a method for automatically determining a turnout equipment failure in one embodiment of the present application. DETAILED DESCRIPTION

[0041] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present application. However, it is apparent to those skilled in the art that the present application can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present application, some technical features well known in the art are not described.

[0042] It should be understood that the present application can be implemented in different forms and should not be construed as being limited to the embodiments presented herein. On the contrary, providing these embodiments will make the disclosure thorough and complete and fully convey the scope of the present application to those skilled in the art. In the accompanying drawings, the sizes and relative sizes of layers and regions may be exaggerated for clarity. The same reference numerals throughout represent the same elements.

[0043] It should be understood that when an element or layer is referred to as "on ...", "adjacent to ...", "connected to" or "coupled to" other elements or layers, it can be directly on, adjacent to, connected to or coupled to other elements or layers, or there can be intervening elements or layers. On the contrary, when an element is referred to as "directly on ...", "directly adjacent to ...", "directly connected to" or "directly coupled to" other elements or layers, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc. can be used to describe various elements, components, regions, layers and / or parts, these elements, components, regions, layers and / or parts should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or part from another element, component, region, layer or part. Therefore, without departing from the teachings of the present application, the first element, component, region, layer or part discussed below can be represented as a second element, component, region, layer or part.

[0044] Spatially relative terms such as "under," "below," "below," "under," "above," "above," etc., may be used herein for ease of description to describe the relationship of an element or feature shown in the figures to other elements or features. It should be understood that in addition to the orientations shown in the figures, the spatially relative terms are intended to include different orientations of the device in use and operation. For example, if the device in the accompanying drawings is flipped, then the elements or features described as "under other elements" or "under" or "under" will be oriented as "on" the other elements or features. Therefore, the exemplary terms "under" and "under" may include both upper and lower orientations. The device may be oriented otherwise (rotated 90 degrees or other orientations) and the spatial descriptors used herein are interpreted accordingly.

[0045] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present application. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0046] In order to thoroughly understand the present application, a detailed structure will be presented in the following description to illustrate the technical solution proposed by the present application. The preferred embodiments of the present application are described in detail below, but in addition to these detailed descriptions, the present application may also have other implementation methods.

[0047] The first aspect of the present application provides a method for automatically determining a turnout equipment fault, such as Figure 1 As shown, the automatic determination method includes:

[0048] Step S1: When the turnout equipment operates normally, a turnout operation current reference curve is established;

[0049] Step S2: dividing the time domain of the turnout action current into at least three time domains according to the turnout action current reference curve, namely, a start unlocking time domain, a conversion time domain, and a locking and releasing time domain;

[0050] Step S3: obtaining the current value within each time domain range to obtain a start-unlock time domain reference current array, a conversion time domain reference current array, and a lock-release time domain reference current array;

[0051] Step S4: when the turnout equipment is working, the turnout action current data of the turnout equipment is collected online in real time, and the turnout action current is divided according to the time domain to obtain a start-unlock time domain sampling current array, a conversion time domain sampling current array, and a lock-release time domain sampling current array;

[0052] Step S5: calculating the linear correlation coefficient between the start-unlock time domain reference current array and the start-unlock time domain sampling current array, calculating the linear correlation coefficient between the conversion time domain reference current array and the conversion time domain sampling current array, and calculating the linear correlation coefficient between the lock-and-release time domain reference current array and the lock-and-release time domain sampling current array;

[0053] Step S6: Compare the linear correlation coefficient obtained in step S5 with the corresponding preset linear correlation coefficient threshold. If any linear correlation coefficient obtained in step S5 is less than the corresponding preset linear correlation coefficient threshold, it is directly determined that the turnout equipment fails within the time domain.

[0054] The present application proposes a method for automatically determining a turnout equipment fault. The method divides the turnout action current reference curve into time domains according to the characteristics of the turnout action current reference curve, and calculates the linear correlation coefficient (such as the PEARSON correlation coefficient) between the sampled current array of the turnout action current collected online and the reference current array generated by the system in each time domain, and automatically determines whether the turnout equipment has a fault based on this linear correlation coefficient. This method can help maintenance personnel to timely discover turnout equipment faults when the turnout action current data collected by the signal maintenance support system MSS is collected, thereby improving the work efficiency of maintenance personnel.

[0055] The advantages of this application are:

[0056] 1. Online measurement of turnout action current reference curve makes the reference basis for turnout fault judgment more reliable.

[0057] 2. Divide the turnout action current reference curve into time domain, calculate the Pearson product-moment correlation coefficient (Pearson correlation coefficient) between the sampled current array in each time domain and the corresponding time domain reference current array, and use this coefficient as the basis to judge the similarity between the current sampled current curve and the reference current curve, so as to determine whether there is a fault in the turnout, and the result is more accurate;

[0058] 3. Compared with other methods, this judgment method is simpler and more effective, and easier to implement in practical applications.

[0059] The following is a detailed description of the automatic fault determination method of the turnout equipment of the present application in conjunction with the accompanying drawings, wherein Figure 2 This is a schematic diagram of the turnout action current curve in this application; Figure 3 This is an example of a sample action current curve of a single-action turnout in an embodiment of the present application; Figure 4 This is a flowchart of a method for automatically determining a turnout equipment failure in one embodiment of the present application.

[0060] In step S1, the sampling period of the turnout current is determined according to the factory-set turnout action current curve provided by the turnout equipment. The sampling period can be determined according to the duration of the normal action of the turnout equipment and is not limited to a certain value range. In one embodiment of the present application, the sampling period of the turnout current can be 10ms.

[0061] In one embodiment of the present application, the current of the normal operation of the turnout equipment is sampled multiple times, and the time and current data of each sampling point are recorded to form multiple groups of time-current two-dimensional arrays of the operation current.

[0062] In one embodiment of the present application, the switch action current is sampled at equal intervals to obtain current data of N switch actions, and a switch action current reference curve is established with the switch action time as the horizontal coordinate and the discrete current data as the vertical coordinate.

[0063] In one embodiment of the present application, the method further includes performing a weighted synthesis operation on the collected multiple sets of time-current curve two-dimensional arrays to generate a weighted time-current two-dimensional array to improve the accuracy of the switch action current reference curve. Then, based on the weighted time-current two-dimensional array, a switch action current reference curve is established with the switch action time as the horizontal coordinate and the discrete current data as the vertical coordinate.

[0064] In one embodiment of the present application, three sets of two-dimensional arrays of time-current curves are collected, and the current values ​​at corresponding sampling time points are averaged, so that the calculated average value is used as the time-current two-dimensional array of the current reference curve.

[0065] In step S2, the turnout action current reference curve is divided into time domains, wherein, in one embodiment of the present application, the turnout action current reference curve is divided into at least three time domains, namely, a start unlocking time domain, a conversion time domain, and a locking and releasing time domain.

[0066] The time domain division may be performed based on the sampling period of the turnout current and the turnout action current curve provided by the manufacturer of the turnout equipment. The operator may adjust the time domain based on the actual situation and the time domain division is not limited to a certain example.

[0067] Specifically, the step S2 further includes determining the starting time point and the ending time point of the start unlocking time domain, the conversion time domain and the locking and releasing time domain, such as Figure 2 As shown, according to each starting time point and ending time point, the time domain of the action current is divided into the start and unlock time domain, the conversion time domain, the locking and release time domain, as shown in Figure 2 As shown; determine the starting time point T0 and the ending time point T1 of the turnout start unlocking time domain and record them; determine the starting time point T1 and the ending time point T2 of the turnout switching time domain and record them; determine the starting time point T2 and the ending time point T3 of the turnout locking and releasing time domain and record them.

[0068] In one embodiment of the present application, according to the manual of the turnout equipment, the normal action time of the turnout from the reverse position to the fixed position is 6000ms, and the sampling period for collecting the turnout current is 10ms. Therefore, each time the turnout moves from the reverse position to the fixed position, the MSS will collect 600 current data, and the sampling interval time of each data is 10ms.

[0069] like Figure 3 As shown, the start time point of the unlocking time domain is set to T0=0ms, the end time point of the unlocking time domain is set to T1=the start time point of the conversion time domain is set to T1=600ms, the end time point of the conversion time domain is set to T2=the start time point of the unlocking slow-release time domain is set to T2=5450ms, and the end time point of the unlocking slow-release time domain is set to T3=6000ms.

[0070] In step S3, the current value at each sampling time point within each time domain range is obtained from the reference time-current two-dimensional array, the switch action current is divided according to the time domain, and recorded as current arrays of each time domain (arranged in the order of sampling time points), namely, the start unlocking time domain reference current array, the conversion time domain reference current array, and the locking and releasing time domain reference current array.

[0071] That is, in this step, according to the time range of the start-unlock time domain, the conversion time domain, and the lock-and-release time domain, combined with the sampling time points in the time-current two-dimensional array of the reference curve, the time-current two-dimensional array of the reference curve obtained in step S1 is divided into corresponding time domains, thereby obtaining the start-unlock time domain reference current array, the conversion time domain reference current array, and the lock-and-release time domain reference current array.

[0072] Among them, the start-unlock time domain reference current array, the conversion time domain reference current array and the lock-release time domain reference current array are respectively part of the turnout action current reference curve, that is, partial curves, and the combination of the three curves is the complete turnout action current reference curve.

[0073] In the step S4, the turnout action current data is continuously collected online in real time with the same sampling period as mentioned above, that is, according to the sampling period of the turnout action current, the turnout action current data of the turnout equipment is collected online in real time, and after converting it into a time-current two-dimensional array, the turnout action current is divided according to the time domain, that is, the current arrays of each time domain are obtained according to the same time domain as mentioned above, which are the start unlocking time domain sampling current array, the conversion time domain sampling current array, and the locking and releasing time domain sampling current array.

[0074] Among them, the start-unlock time domain sampling current array, the conversion time domain sampling current array, and the lock-release time domain sampling current array are three curves respectively, which together constitute the turnout action current sampling curve. In this application, the turnout action current reference curve is compared with the turnout action current sampling curve in sections for similarity comparison to determine whether the turnout equipment has a fault.

[0075] In order to improve the accuracy of the similarity comparison between the turnout action current reference curve and the turnout action current sampling curve, in the step S5, the linear correlation coefficients of the sampling current arrays in each time domain and the reference current array are calculated in sequence according to the start-unlock time domain, the conversion time domain, and the locking and releasing time domain, that is, the linear correlation coefficients of the start-unlock time domain reference current array and the start-unlock time domain sampling current array are calculated in sequence, the linear correlation coefficients of the conversion time domain reference current array and the conversion time domain sampling current array are calculated, and the linear correlation coefficients of the locking and releasing time domain reference current array and the locking and releasing time domain sampling current array are calculated.

[0076] The linear correlation coefficient is the Pearson product-moment correlation coefficient P. The Pearson correlation coefficient is widely used to measure the correlation between two variables, and is used in the present application to measure the correlation (linear correlation) between two corresponding current arrays in the time domain, and its value is between -1 and 1.

[0077] In step S6, during the similarity comparison process, a preset linear correlation coefficient threshold P1 is usually pre-set, and then the Pearson correlation coefficient P obtained in step S5 is compared with the corresponding preset linear correlation coefficient threshold P1, and whether the turnout equipment fails is determined based on the comparison result.

[0078] Specifically, in a certain time domain, if any linear correlation coefficient obtained in step S5 is less than the corresponding system preset linear correlation coefficient threshold, it is directly determined that the turnout equipment has a fault in this time domain stage, and a fault alarm prompt is given.

[0079] If the Pearson correlation coefficient P between the time domain sampling current array and the reference current array is greater than or equal to the preset linear correlation coefficient threshold P1, although the turnout action sampling current curve and the turnout action reference current curve are similar in shape, it is possible that the value of the turnout action sampling current curve at each time point in the time domain increases or decreases by the same or nearly the same current value compared with the turnout action reference current curve. In order to prevent such an error, in this case, the mean difference ΔAVG of the two current arrays within the time domain range is further calculated to determine the turnout fault in this case.

[0080] If the linear correlation coefficient obtained in step S5 is greater than or equal to the corresponding system preset linear correlation coefficient threshold, the difference between the mean of the sampling current array within the time domain range and the mean of the corresponding time domain reference current array is calculated. If the difference in the mean is greater than the system preset threshold, a fault is determined within the time domain range and an alarm prompt is given.

[0081] In one embodiment of the present application, a system-preset threshold for comparing the difference with the mean value is set in the start-unlock time domain, the conversion time domain and the lock-release time domain.

[0082] In the present application, the methods are all executed in the signal maintenance support subsystem (MSS) to realize real-time online detection of turnout equipment failures, which is simpler, more effective and easier to implement.

[0083] In addition, the movement of the turnout equipment described in the present application can be from reverse position to fixed position, or from fixed position to reverse position, and is not limited to any one of them.

[0084] It should be noted that the method of the present application is not only applicable to single-action switch equipment, but also to multi-action switch equipment. In the multi-action switch equipment, there are multiple switch action current reference curves and multiple switch action current sampling curves. The switch action current reference curve and the corresponding switch action current are compared for similarity to make a judgment, which will not be repeated here.

[0085] In one embodiment of the present application, the specific implementation method of the method is as follows: In this embodiment, the signal maintenance support subsystem (MSS) is used as the operating system to perform fault monitoring and fault warning on the single-action turnout equipment of the signal system, and the monitoring of the action current curve from reverse position to fixed position is taken as an example.

[0086] According to the factory settings of the turnout equipment, the normal rotation time of the turnout from reverse position to fixed position is 6000ms, and the sampling period of the MSS system for collecting turnout current is determined to be 10ms. Therefore, each time the turnout moves from reverse position to fixed position, the MSS will collect 600 current data, and the sampling interval of each data is 10ms.

[0087] After the signal system and MSS system are installed, the signal system repeats the normal operation of the turnout from reverse position to fixed position three times, and the MSS system collects the current of the turnout from reverse position to fixed position respectively. The three sets of turnout action current curve data collected are shown in Tables 1 to 3, and the weighted sample action current curve data are shown in Table 4: Table-1 The first set of turnout action current table from reverse position to fixed position

[0088] Time (ms) 10 20 10*n 5990 6000 Current (A) I1 I2 In I599 I600

[0089] Table-2 Second set of turnout reverse to positioning action current table

[0090] Time (ms) 10 20 10*n 5990 6000 Current (A) II1 II2 IIn II599 II600

[0091] Table-3 Current table of the third group of turnout reverse to positioning action

[0092] Time (ms) 10 20 10*n 5990 6000 Current (A) III1 III2 III III599 III600

[0093] Table-4 Weighted synthesis generated turnout reverse to positioning action current data table

[0094]

[0095]

[0096] Next, the MSS system draws the switch action current reference curve, which can be obtained from Figure 3 The curve characteristics in determine the start value T0 of the unlocking time domain, the end value of the unlocking time domain and the start time point T1 of the conversion time domain, the end time point of the conversion time domain and the start time point T2 of the unlocking slow-release time domain, the end time point T3 of the unlocking slow-release time domain, the system preset linear correlation coefficient threshold P1, and the system preset threshold ΔAVG1.

[0097] Specifically, the start time point of the unlocking time domain is set to T0 = 0ms, the end time point of the unlocking time domain is set to T1 = the start time point of the conversion time domain T1 = 600ms, the end time point of the conversion time domain T2 = the start time point of the unlocking slow-release time domain T2 = 5450ms, the end time point of the unlocking slow-release time domain T3 = 6000ms, the system preset linear correlation coefficient threshold P1 = 0.90, and the system preset threshold ΔAVG1 = 1A, such as Figure 3 As shown;

[0098] In actual operation, if the Pearson correlation coefficient of all the sampled current arrays acquired in the 0-600ms turnout start-up and unlocking time domain and the reference current array in the corresponding time domain is 0.5 in the action current curve data collected when a turnout switches from reverse position to positioning, which is less than the system preset linear correlation coefficient threshold value P1=0.90, the system determines that the turnout equipment has a fault during the turnout start-up and locking period, and no longer makes judgments in the remaining time domain, and gives a turnout fault alarm;

[0099] If in the action current curve data collected when a turnout switches from reverse position to positioning, the Pearson correlation coefficient calculated between the sampled current array obtained in the 0-600ms turnout start-up unlocking time domain and the reference current array in the corresponding time domain is 0.95, which is greater than the system preset linear correlation coefficient threshold value P1=0.90, then the system calculates that the difference between the mean of the sampled current array in the 0-600ms time domain and the mean of the reference current array in the corresponding time domain is 1.2A, which is greater than the system preset threshold value ΔAVG1(1A), then the system determines that there is a fault in the turnout during the start-up unlocking period, no longer makes judgments in the remaining time domains, and gives a turnout fault alarm; the judgment method for other time domains is the same as the judgment method for the aforementioned start-up unlocking time domain.

[0100] In another embodiment of the present application, Figure 4 As shown, in this embodiment, firstly, the normal turnout action current data is sampled online;

[0101] Then, a two-dimensional time-current array of the switch action current is generated and weighted calculation is performed;

[0102] Draw the reference curve of turnout action current;

[0103] Set the turnout fault judgment parameters, such as the starting point of the unlocking time domain, the conversion time domain, the locking and releasing time domain, as well as the system preset linear correlation coefficient threshold and the system preset threshold.

[0104] In actual work, the turnout action current sampling curve data is sampled in real time, and it is determined whether the collection of the entire turnout action current sampling curve data is completed. If completed, the next instruction is executed. If the collection of the entire turnout action current sampling curve data is not completed, data collection continues to ensure that the collection of the entire turnout action current sampling curve data is completed.

[0105] After completing the acquisition of the entire turnout action current sampling curve data, determine whether the linear correlation coefficient P between the start unlocking time domain reference current array and the start unlocking time domain sampling current array is less than the system preset linear correlation coefficient threshold value P1. If this condition is met, an alarm fault is given. If not, continue to determine whether the difference between the start unlocking time domain reference current array mean and the reference current array mean of the start unlocking time domain sampling current array is greater than the system preset threshold value ΔAVG1. If so, an alarm fault is given.

[0106] If there is no fault in the start-up unlocking time domain, the conversion time domain and the locking and releasing time domain are judged in sequence according to the same method as above until there is no fault in all the time domains.

[0107] The present application also provides an automatic determination device for turnout equipment failure, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor implements corresponding operations of the above steps S1 to S6 when executing the computer program.

[0108] The memory is used to store various data and executable computer programs generated when the turnout equipment is in operation, such as various application programs or algorithms for implementing various specific functions. It can include one or more computer program products, and the computer program product can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0109] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other forms of processing units having data processing capabilities and / or instruction execution capabilities.

[0110] The present application also provides a maintenance support system, which includes the automatic determination device for turnout equipment failure as described above and other conventional structures, which will not be described in detail here.

[0111] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present application to this. Those of ordinary skill in the art may make various changes and modifications therein without departing from the scope and spirit of the present application. All these changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0112] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0114] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0115] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present application, the various features of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present application should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with features less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby explicitly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0116] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0117] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0118] It should be noted that the above embodiments illustrate rather than limit the present application, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets should not be constructed as a limitation to the claims. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names.

Claims

1. A method for automatically determining a turnout equipment fault, characterized in that: The automatic determination method comprises: Step S1: When the turnout equipment operates normally, a turnout operation current reference curve is established; Step S2: dividing the time domain of the turnout action current into at least three time domains according to the turnout action current reference curve, namely, a start unlocking time domain, a conversion time domain, and a locking and releasing time domain; Step S3: obtaining the current value within each time domain range to obtain a start-unlock time domain reference current array, a conversion time domain reference current array, and a lock-release time domain reference current array; Step S4: when the turnout equipment is working, the turnout action current data of the turnout equipment is collected online in real time, and the turnout action current is divided according to the time domain to obtain a start-unlock time domain sampling current array, a conversion time domain sampling current array, and a lock-release time domain sampling current array; Step S5: calculating the linear correlation coefficient between the start-unlock time domain reference current array and the start-unlock time domain sampling current array, calculating the linear correlation coefficient between the conversion time domain reference current array and the conversion time domain sampling current array, and calculating the linear correlation coefficient between the lock-and-release time domain reference current array and the lock-and-release time domain sampling current array; Step S6: Compare the linear correlation coefficient obtained in step S5 with the corresponding preset linear correlation coefficient threshold. If any linear correlation coefficient obtained in step S5 is less than the corresponding preset linear correlation coefficient threshold, it is directly determined that the turnout equipment has a fault within the time domain; if any linear correlation coefficient obtained in step S5 is greater than or equal to the corresponding preset linear correlation coefficient threshold, the difference between the mean of the sampled current array within the time domain and the mean of the reference current array in the corresponding time domain is calculated. If the difference is greater than the preset threshold, it is determined that the turnout equipment has a fault within the time domain.

2. The automatic determination method according to claim 1, characterized in that: The step S1 comprises: Step S11: determining a sampling period of the turnout action current according to the factory turnout action current curve of the turnout equipment; Step S12: sampling the turnout action current when the turnout equipment is in normal action for multiple times within the sampling period to obtain multiple samples and acquire time and current data of each sample to form multiple groups of action current time-current two-dimensional arrays; Step S13: according to the time-current two-dimensional array, a turnout action current reference curve is established with the turnout action time as the abscissa and the discrete current data as the ordinate.

3. The automatic determination method according to claim 1, characterized in that: The step S1 comprises: Step S11: determining a sampling period of the turnout action current according to the factory turnout action current curve of the turnout equipment; Step S12: sampling the turnout action current when the turnout equipment is in normal action for multiple times within the sampling period to obtain multiple samples and acquire time and current data of each sample to form multiple groups of action current time-current two-dimensional arrays, and performing weighted synthesis operation on the multiple groups of time-current two-dimensional arrays to generate weighted time-current two-dimensional arrays; Step S13: establishing a turnout action current reference curve based on the weighted time-current two-dimensional array, with the turnout action time as the abscissa and the discrete current data as the ordinate.

4. The automatic determination method according to claim 2, characterized in that: The step S4 includes: According to the sampling period of the turnout action current, the turnout action current data of the turnout equipment is collected online in real time, and the turnout action current data collected in real time is converted into a time-current two-dimensional array, and the turnout action current is divided according to the time domain to obtain a start-unlock time domain sampling current array, a conversion time domain sampling current array, and a lock-release time domain sampling current array.

5. The automatic determination method according to claim 1, characterized in that: The step S2 comprises: Determine the starting time point and the ending time point of the start unlocking time domain, the conversion time domain and the locking and releasing time domain respectively; The time domain of the turnout action current is divided into the start unlocking time domain, the conversion time domain and the locking and releasing time domain according to the starting time point and the ending time point of each time domain.

6. The automatic determination method according to claim 1, characterized in that: In the step S5, the linear correlation coefficients of the sampled current array and the reference current array in each time domain are calculated in sequence from the start-up unlocking time domain, the conversion time domain to the lock-release time domain.

7. The automatic determination method according to claim 6, characterized in that: In step S6, if it is determined that the switch equipment fails in one of the time domains, it is no longer determined whether a failure occurs in other time domains.

8. The automatic determination method according to any one of claims 1 to 7, characterized in that: The linear correlation coefficient is the Pearson product-moment correlation coefficient.

9. An automatic judgment device for turnout equipment failure, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the automatic determination method according to any one of claims 1 to 8 when executing the computer program.

10. A maintenance support system, characterized in that: It comprises the automatic determination device of turnout equipment failure as claimed in claim 9.

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