CN112477917A - Turnout fault detection method and device, electronic equipment and storage medium - Google Patents
Turnout fault detection method and device, electronic equipment and storage medium Download PDFInfo
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- CN112477917A CN112477917A CN202011248055.6A CN202011248055A CN112477917A CN 112477917 A CN112477917 A CN 112477917A CN 202011248055 A CN202011248055 A CN 202011248055A CN 112477917 A CN112477917 A CN 112477917A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L1/00—Devices along the route controlled by interaction with the vehicle or vehicle train, e.g. pedals
- B61L1/20—Safety arrangements for preventing or indicating malfunction of the device, e.g. by leakage current, by lightning
Abstract
The embodiment of the application provides a turnout fault detection method, a turnout fault detection device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining a rotating current curve of a turnout; extracting a characteristic value of the rotating current curve; the characteristic values include: the switching time, the peak current, the switching current average value, the switching current variance, the locking current mean value, the slow release region current average value and the slow release region duration time; and inputting the characteristic values into a pre-established fault tree for analysis, and determining the fault type of the turnout. The turnout fault detection method, the turnout fault detection device, the electronic equipment and the storage medium provided by the embodiment of the application are based on the control principle of the turnout, the fault tree is established, the branches of the fault tree are realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Description
Technical Field
The present disclosure relates to the field of rail transit technologies, and in particular, to a method and an apparatus for detecting a turnout fault, an electronic device, and a storage medium.
Background
A switch is a line connection device for a rolling stock to be switched from one track to another, and is one of weak links of a track, and is usually laid in a large number at a station and a marshalling station. With the turnout, the passing capacity of the line can be fully exerted. Even if a single-track railway is used, a turnout is paved, and a section of fork line with the length larger than that of a train is constructed, so that the train can be split. Turnouts play an important role on railway lines.
At present, two main methods are available for fault diagnosis of turnout: 1. the manual analysis method is based on the understanding and experience of maintenance personnel on the turnout, each rotating current curve is manually analyzed, whether the turnout has a fault or is possible to have the fault is judged, and when the turnout has the fault, the fault reason is identified based on different curve forms. 2. The method based on artificial intelligence machine learning does not need to understand turnout control excitation, utilizes a large amount of fault data and normal data, firstly carries out feature extraction, then carries out clustering, and depends on manual marking, and carries out classification after new data comes, thereby realizing the identification of the fault.
The method based on manual analysis is time-consuming and labor-consuming, and the analysis results are different due to different understanding of each person on the turnout, so that the unity is lacked. The artificial intelligence method needs a large amount of fault data, and the fault data in actual operation cannot meet the requirements of machine learning training easily. In addition, manual standards are also needed to identify faults.
Disclosure of Invention
The embodiment of the application provides a switch fault detection method and device, electronic equipment and a storage medium, and aims to solve the technical problems that in the prior art, a manual analysis method is time-consuming and labor-consuming, and an artificial intelligence method needs a large amount of fault training data.
The embodiment of the application provides a turnout fault detection method, which comprises the following steps:
obtaining a rotating current curve of a turnout;
extracting a characteristic value of the rotating current curve; the characteristic values include: the switching time, the peak current, the switching current average value, the switching current variance, the locking current mean value, the slow release region current average value and the slow release region duration time;
and inputting the characteristic values into a pre-established fault tree for analysis, and determining the fault type of the turnout.
According to the turnout fault detection method provided by the embodiment of the application, the conversion time is the number of sampling points of a conversion area multiplied by a sampling interval;
the peak current is the maximum value of all sampled data values of the unlocking zone;
the average value of the conversion current is the average value of the data values of the sampling points in the conversion area;
the conversion current variance is the variance of the data values of the sampling points in the conversion area;
the mean value of the locking current is the mean value of the data values of the sampling points in the unlocking area;
the average value of the current of the slow release region is the average value of data values of sampling points in the slow release region;
and multiplying the number of sampling points of the slow release region duration time slow release region by the sampling interval.
According to an embodiment of the present application, before obtaining a turning current curve of a switch, the method further includes:
and establishing the fault tree based on the control principle of the turnout.
According to an embodiment of the present invention, after extracting the characteristic value of the rotating current curve, the method for detecting a turnout fault further includes:
and predicting whether the turnout is in fault or not according to the latest characteristic value and a preset maximum threshold or minimum threshold.
According to the turnout fault detection method of one embodiment of the application, predicting whether the turnout will have a fault according to the latest feature value and the preset maximum threshold value or the preset minimum threshold value specifically includes:
when the latest characteristic value exceeds the maximum threshold value or the preset proportion of the minimum threshold value, predicting that the turnout is in failure;
and when the latest characteristic value does not exceed the maximum threshold value or the preset proportion of the minimum threshold value, predicting that the turnout does not have a fault.
According to an embodiment of the present invention, after extracting the characteristic value of the rotating current curve, the method for detecting a turnout fault further includes:
drawing a characteristic curve according to a plurality of continuous characteristic values;
calculating the variance and the average value of the characteristic curve;
and predicting whether the turnout is in fault or not according to the variance and the average value.
According to the turnout fault detection method of one embodiment of the application, predicting whether the turnout will have a fault according to the variance and the average specifically includes:
when the variance is kept unchanged and the average value is always increased or decreased, the predicted turnout can be in failure.
The embodiment of the present application further provides a switch fault detection device, include:
the acquisition module is used for acquiring a rotating current curve of the turnout;
the extraction module is used for extracting a characteristic value of the rotating current curve;
and the determining module is used for inputting the characteristic values into a fault tree which is established in advance for analysis and determining the fault type of the turnout.
The embodiment of the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the switch fault detection method according to any one of the above mentioned steps when executing the program.
The embodiments of the present application also provide a non-transitory computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the switch fault detection method according to any one of the above.
The turnout fault detection method, the turnout fault detection device, the electronic equipment and the storage medium provided by the embodiment of the application are based on the control principle of the turnout, the fault tree is established, the branches of the fault tree are realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a switch fault detection method according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a turning current curve obtained when a turnout is normal;
FIG. 3 is a schematic diagram of a fault tree provided by an embodiment of the present application;
FIG. 4 is a schematic diagram of a switch fault detection and prediction principle provided by an embodiment of the present application;
fig. 5 is a schematic structural diagram of a switch fault detection apparatus provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic flow chart of a switch fault detection method provided in an embodiment of the present application, and as shown in fig. 1, the embodiment of the present application provides a switch fault detection method, which includes:
and 101, obtaining a rotating current curve of the turnout.
Specifically, first, current data is acquired from a signal microcomputer monitoring system, and the current data is displayed in the form of a rotating current curve.
Fig. 2 is a schematic diagram of a turning current curve obtained when a turnout is normal, and as shown in fig. 2, the turning current curve can be divided into three regions (segments): an unlock region, a transition region, and a slow release region, corresponding to A, B and C in FIG. 2, respectively. The horizontal axis of the rotating current curve represents time (unit: ms) and the vertical axis represents current value (unit: mA).
It should be noted that: the turning current of the turnout is three-phase current, and the three-phase current is overlapped in figure 2, so that only one curve can be displayed.
102, extracting a characteristic value of the rotating current curve; the characteristic values include: transition time, peak current, transition current average, transition current variance, latching current mean, slow release region current average, and slow release region duration.
Specifically, a mathematical-based method extracts a characteristic value of a rotating current curve. The characteristic value can reflect whether the turnout is in failure or not and the reason of the failure.
The characteristic value can reflect the basic state of a turnout, and the variation trend of the statistical value in a period of time can also be used for fault prediction.
For example, the characteristic values may include: transition time, peak current, transition current average, transition current variance, latching current mean, slow release region current average, and slow release region duration.
And 103, inputting the characteristic values into a fault tree established in advance for analysis, and determining the fault type of the turnout.
Specifically, the method and the device for detecting the turnout fault are based on the turnout control principle, simulate the mode of analyzing a rotating current curve by a maintenance expert, establish the fault tree, utilize different characteristic values to realize the branching of the fault tree, and finally realize the fault detection of the turnout.
And after the characteristic value of the rotating current curve is obtained, inputting the characteristic value into a fault tree established in advance for analysis, and determining the fault type of the turnout.
Fig. 3 is a schematic diagram of a fault tree provided in the embodiment of the present application, and as shown in fig. 3, a fault tree is established by simulating a manner in which a maintenance expert analyzes a rotating current curve based on a control principle of a turnout, where the fault tree includes a plurality of nodes, each node corresponds to a judgment, and a judgment flow of a next node is based on a judgment result of a previous node.
The fault analysis process is automated and some simple logic is illustrated below:
the first node is to judge whether the conversion time and the slow release time are normal, if not, whether peak current exists is judged, and if the total conversion time is about 2S and the peak current exists, the 1DQJF fault can be judged;
if the peak current exists, the current is 2DQJ, the pole is not rotated;
if the switching time is 15S and the switching current is equal to the friction current after about 4S, it is that the switch is not locked;
if the total conversion time is normal, but the slow release current is 0, the open circuit of the diode is indicated to cause the open circuit of the resistor.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
According to any of the above embodiments, the characteristic values include: transition time, peak current, transition current average, transition current variance, latching current mean, slow release region current average, and slow release region duration.
Specifically, in the embodiment of the present application, the characteristic values include: transition time, peak current, transition current average, transition current variance, latching current mean, slow release region current average, and slow release region duration.
As shown in fig. 2, a mathematical method is used to extract the characteristic value of the rotating current curve, and the specific method is as follows:
(a) the conversion time is fixed, generally 40ms one point, and the total conversion time length can be obtained by calculating all points received by the conversion area and multiplying the sampling time;
(b) peak current, which is instantaneous peak current during unlocking, is generally the maximum value of the whole curve, and multiple peak currents may be caused during repeated operation for part of times, so that the first peak current is the peak current for unlocking the turnout;
(c) the average current value of the slow release area is shown on the right part of the dotted vertical line and is called as a slow release area, and the average current value is obtained by adding data of all points and then dividing the sum by the number of the points;
(d) the slow release region duration, calculating the number of sampling points from the virtual vertical line to the end of the curve and multiplying the number of the sampling points by the sampling time;
(e) converting the current average value, accumulating all sampling point values between the real vertical line and the virtual vertical line and dividing the accumulated sampling point values by the total point number;
(f) converting the current variance, and calculating the variance of values of all sampling points between the real vertical line and the virtual vertical line based on a statistical principle;
(g) and locking the current mean value, pushing the real vertical line forward for 0.5 second, and calculating the mean value of the sampling points within 0.5 second.
The embodiment of the application divides the rotating current curve into 8 kinds of characteristic values, including conversion time, peak current, conversion current average value, conversion current variance, locking current average value, slow release area current average value and slow release area duration to combine together with switch friction current and carry out the analysis, confirm whether there is the trouble in switch, and the type of trouble.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Based on any embodiment of the foregoing, before obtaining the turning current curve of the turnout, the method further includes:
and establishing the fault tree based on the control principle of the turnout.
Specifically, in the embodiment of the application, before the rotating current curve of the turnout is obtained, based on the control principle of the turnout, a mode of analyzing the rotating current curve by a maintenance expert is simulated, a fault tree is established, branches of the fault tree are realized by using different characteristic values, and finally fault detection of the turnout is realized.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Based on any embodiment of the foregoing, after extracting the characteristic value of the rotating current curve, the method further includes:
and predicting whether the turnout is in fault or not according to the latest characteristic value and a preset maximum threshold or minimum threshold.
Specifically, fig. 4 is a schematic diagram of a switch fault detection and prediction principle provided by an embodiment of the present application, and as shown in fig. 4, in the embodiment of the present application, after extracting a characteristic value of a rotating current curve, a curve is further described by using a statistical method to implement prediction of a fault.
And describing a characteristic curve based on the acquired characteristic values, setting a maximum value and a minimum value, and predicting whether the turnout is in failure or not according to the latest characteristic values and the set maximum value or minimum value.
For example, when the latest characteristic value exceeds a preset proportion of a maximum threshold value or a minimum threshold value, the turnout is predicted to be in failure;
and when the latest characteristic value does not exceed the preset proportion of the maximum threshold value or the minimum threshold value, predicting that the turnout does not have a fault.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Based on any of the above embodiments, the predicting whether the turnout will fail according to the latest feature value and the preset maximum threshold or minimum threshold specifically includes:
when the latest characteristic value exceeds the maximum threshold value or the preset proportion of the minimum threshold value, predicting that the turnout is in failure;
and when the latest characteristic value does not exceed the maximum threshold value or the preset proportion of the minimum threshold value, predicting that the turnout does not have a fault.
Specifically, in the embodiment of the present application, the specific steps of predicting whether the turnout will fail according to the latest feature value and the preset maximum threshold or minimum threshold are as follows:
and when the latest characteristic value exceeds the preset proportion of the maximum threshold value or the minimum threshold value, predicting that the turnout is in failure.
And when the latest characteristic value does not exceed the preset proportion of the maximum threshold value or the minimum threshold value, predicting that the turnout does not have a fault.
For example, when the latest feature value exceeds a certain proportion, e.g., 70%, of the maximum or minimum value, a fault is deemed likely to occur, which enables a rough fault prediction.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Based on any embodiment of the foregoing, after extracting the characteristic value of the rotating current curve, the method further includes:
drawing a characteristic curve according to a plurality of continuous characteristic values;
calculating the variance and the average value of the characteristic curve;
and predicting whether the turnout is in fault or not according to the variance and the average value.
Specifically, in the embodiment of the present application, after extracting the characteristic value of the rotating current curve, the method further includes using a statistical method to describe the curve to predict the fault, and the specific steps are as follows:
first, a characteristic curve is drawn from a plurality of continuous characteristic values.
Then, the variance and mean of the characteristic curve are calculated.
And finally, predicting whether the turnout fails or not according to the variance and the average value.
For example, taking several points at a time on the characteristic curve, the variance and mean of the characteristic curve are calculated. And a sliding forward approach is adopted, that is, assuming that 1 st to 10 th points are taken for the first time, the mean and variance are calculated, and 2 nd to 11 th points are taken for the second time, the mean and variance are calculated.
In addition, in the embodiment of the application, the turnout fault can be predicted according to one characteristic curve, and the turnout fault can also be predicted according to a plurality of characteristic curves.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Based on any of the above embodiments, the predicting whether the turnout will fail according to the variance and the average specifically includes:
when the variance is kept unchanged and the average value is always increased or decreased, the predicted turnout can be in failure.
Specifically, in the embodiment of the present application, the specific steps of predicting whether the switch will fail according to the variance and the average value are as follows:
if the variance remains the same, but the mean value consistently becomes larger or smaller, then a risk of failure is considered. And calculating the relation between the change of a plurality of points and the time, performing curve fitting, and bringing the maximum value or the minimum value into the relation, so that the time of the fault can be obtained, and the accurate fault prediction is realized.
The turnout fault detection method provided by the embodiment of the application is based on the control principle of the turnout, the fault tree is established, the branch of the fault tree is realized by utilizing different characteristic values, the fault detection of the turnout is finally realized, a large amount of fault training data is not needed, and the detection efficiency is improved.
Based on any of the above embodiments, fig. 5 is a schematic structural diagram of a switch fault detection apparatus provided in an embodiment of the present application, and as shown in fig. 5, the switch fault detection apparatus provided in the embodiment of the present application includes an obtaining module 501, an extracting module 502, and a determining module 503, where:
the obtaining module 501 is configured to obtain a turning current curve of a turnout; the extracting module 502 is configured to extract a characteristic value of the rotating current curve; the determining module 503 is configured to input the feature values into a fault tree established in advance for analysis, and determine the fault type of the switch.
Specifically, the turnout fault detection device provided in the embodiment of the present application can implement all the method steps implemented by the above method embodiment, and can achieve the same technical effect, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not described herein again.
Fig. 6 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 6: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may invoke logic instructions in the memory 630 to perform a switch fault detection method comprising:
obtaining a rotating current curve of a turnout;
extracting a characteristic value of the rotating current curve;
and inputting the characteristic values into a pre-established fault tree for analysis, and determining the fault type of the turnout.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present application further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, where the computer program includes a program or instructions, and when the program or instructions are executed by a computer, the computer is capable of executing the switch fault detection method provided by the above-mentioned method embodiments, where the method includes:
obtaining a rotating current curve of a turnout;
extracting a characteristic value of the rotating current curve;
and inputting the characteristic values into a pre-established fault tree for analysis, and determining the fault type of the turnout.
In another aspect, the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the switch fault detection method provided by the foregoing embodiments, where the method includes:
obtaining a rotating current curve of a turnout;
extracting a characteristic value of the rotating current curve;
and inputting the characteristic values into a pre-established fault tree for analysis, and determining the fault type of the turnout.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A switch fault detection method, comprising:
obtaining a rotating current curve of a turnout;
extracting a characteristic value of the rotating current curve; the characteristic values include: the switching time, the peak current, the switching current average value, the switching current variance, the locking current mean value, the slow release region current average value and the slow release region duration time;
and inputting the characteristic values into a pre-established fault tree for analysis, and determining the fault type of the turnout.
2. The turnout fault detection method according to claim 1, wherein the conversion time is the number of sampling points of a conversion area multiplied by a sampling interval;
the peak current is the maximum value of all sampled data values of the unlocking zone;
the average value of the conversion current is the average value of the data values of the sampling points in the conversion area;
the conversion current variance is the variance of the data values of the sampling points in the conversion area;
the mean value of the locking current is the mean value of the data values of the sampling points in the unlocking area;
the average value of the current of the slow release region is the average value of data values of sampling points in the slow release region;
and multiplying the number of sampling points of the slow release region duration time slow release region by the sampling interval.
3. The switch fault detection method according to claim 1, wherein before acquiring the turning current curve of the switch, the method further comprises:
establishing the fault tree based on the control principle of the turnout; the fault tree comprises a plurality of nodes, each node corresponds to one judgment, and the judgment process of the next node is based on the judgment result of the previous node.
4. The switch fault detection method according to claim 1, wherein after extracting the characteristic value of the rotating current curve, the method further comprises:
and predicting whether the turnout is in fault or not according to the latest characteristic value and a preset maximum threshold or minimum threshold.
5. The turnout fault detection method according to claim 4, wherein the predicting whether the turnout will have a fault according to the latest feature value and a preset maximum threshold or a preset minimum threshold specifically comprises:
when the latest characteristic value exceeds the maximum threshold value or the preset proportion of the minimum threshold value, predicting that the turnout is in failure;
and when the latest characteristic value does not exceed the maximum threshold value or the preset proportion of the minimum threshold value, predicting that the turnout does not have a fault.
6. The switch fault detection method according to claim 1, wherein after extracting the characteristic value of the rotating current curve, the method further comprises:
drawing a characteristic curve according to a plurality of continuous characteristic values;
calculating the variance and the average value of the characteristic curve;
and predicting whether the turnout is in fault or not according to the variance and the average value.
7. The turnout fault detection method according to claim 6, wherein the predicting whether the turnout will fail according to the variance and the average specifically comprises:
when the variance is kept unchanged and the average value is always increased or decreased, the predicted turnout can be in failure.
8. A switch fault detection device, comprising:
the acquisition module is used for acquiring a rotating current curve of the turnout;
the extraction module is used for extracting a characteristic value of the rotating current curve;
and the determining module is used for inputting the characteristic values into a fault tree which is established in advance for analysis and determining the fault type of the turnout.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the switch fault detection method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the switch fault detection method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113627496A (en) * | 2021-07-27 | 2021-11-09 | 交控科技股份有限公司 | Method, device, electronic equipment and readable storage medium for predicting fault of turnout switch machine |
| CN114368409A (en) * | 2021-12-20 | 2022-04-19 | 广西交控智维科技发展有限公司 | Method and device for analyzing health condition of track traffic turnout |
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