CN111896044A - Monitoring method and equipment for railway contact net compensation device - Google Patents
Monitoring method and equipment for railway contact net compensation device Download PDFInfo
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Abstract
The application discloses a monitoring method and equipment for a railway contact net compensation device, which are used for solving the technical problems that the existing monitoring method cannot truly reflect the running state of the compensation device and cannot perform early warning aiming at environmental data. The method comprises the following steps: the method comprises the steps that a server receives running state monitoring data of a plurality of contact net compensation device test points in a preset time period; constructing a training data set and a verification data set of the neural network model according to the monitoring data, and training and verifying the neural network model through the data set to obtain a b value prediction model of the overhead line system compensation device; inputting the running state monitoring data at a plurality of preset moments into a b value prediction model to obtain a b value at T moment; and sending alarm information to the mobile terminal under the condition that the value b at the time T is greater than a first preset threshold value. By the method, the b value truly reflects the running state of the current compensation device, early warning is realized, and the prevention control capability is improved.
Description
Technical Field
The application relates to the technical field of equipment monitoring, in particular to a monitoring method and equipment for a railway contact net compensation device.
Background
The overhead contact system of the high-speed railway is a special power transmission line which is erected over a railway line and supplies power to an electric power unit, and the stable operation of the overhead contact system is closely related to the stable operation of railway running safety. The tension of the catenary compensation device (also called a tension automatic compensator) is a key parameter related to the stable, reliable and safe operation of the catenary, and is directly related to the operation state of the catenary and the railway driving safety. Therefore, it is important to monitor the touch screen compensation device.
However, by using the existing monitoring method of the contact net compensation device, the device which is rusted or deteriorated is difficult to find in time. And the judgment mode aiming at the monitoring data is single, the running state of the current catenary compensation device cannot be accurately reflected, and early warning cannot be performed according to the current environmental data.
Disclosure of Invention
The embodiment of the application provides a monitoring method and equipment for a railway contact net compensation device, which are used for solving the technical problems that the existing monitoring method for the contact net compensation device cannot accurately reflect the running state of the current contact net compensation device and cannot perform early warning aiming at the current environmental data.
On the one hand, the embodiment of the application provides a monitoring method of a railway contact network compensation device, comprising the following steps: the method comprises the steps that a server receives running state monitoring data of a plurality of contact net compensation device test points in a preset time period; according to the operation state monitoring data, a training data set and a verification data set of the neural network model are constructed, the neural network model is trained through the training data set, and the neural network model is verified through the verification data set, so that a b value prediction model of the overhead line system compensation device is obtained; the value b is the vertical distance between the bottom surface of a first balance weight of the contact net compensation device and the ground, and the first balance weight is the balance weight closest to the ground in a balance weight string; inputting the running state monitoring data at a plurality of preset moments into a b value prediction model to obtain a b value at T moment; wherein the time T is after a plurality of preset times; and sending alarm information to the mobile terminal under the condition that the value b at the time T is greater than a first preset threshold value.
According to the monitoring method for the running state of the catenary compensation device, the data set of the neural network model is constructed through the monitoring data in the preset time period, the neural network model is trained and verified through the data set, the b value prediction model of the catenary compensation device is obtained, and the b value is predicted through the model. The advanced deep learning intelligent analysis technology is adopted to carry out prediction analysis on the running state of the contact net compensation device, the b value obtained by model prediction can truly reflect the running state of the compensation device, so that monitoring personnel can timely find problems of the compensation device in the running process, and the method has important significance for ensuring the stable running of the contact net compensation device. In addition, when the predicted b value changes greatly, early warning information is sent out in time, monitoring personnel can correct data in time, whole-network early warning management is achieved, and larger loss is avoided.
In an implementation manner of the present application, the operation state monitoring data mainly includes any one or more of the following items: time, temperature, humidity, wind speed, wind direction, longitude and latitude, and b value. The neural network model is trained through a training data set constructed by various running state monitoring data, so that the model is established to integrate the information of the external environment, the weather and the like of the installation area of the overhead line system compensation device, the external influence factors are considered, and the running state of the compensation device can be accurately analyzed.
In an implementation manner of the present application, a training data set and a verification data set of a neural network model are constructed according to the operation state monitoring data, and specifically include: encoding wind speed data in the running state monitoring data in a first preset encoding mode; the wind direction data are coded through a second preset coding mode; carrying out normalization processing on time data, temperature data, humidity data, longitude and latitude data, b value data, coded wind direction data and coded wind speed data in the operation state monitoring data, and storing the processed data into a data set to be grouped in a preset format; and after the abnormal data in the data set to be grouped is removed, grouping is carried out to obtain a training data set and a verification data set.
In an implementation of the present application, the neural network model is trained through a training data set, and the neural network model is verified through a verification data set, so as to obtain a b-value prediction model of the catenary compensation device, which specifically includes: inputting the running state monitoring data at t-n, t- (n-1) and t-1 in the training data set and the b value at t into a neural network model to train the neural network model; wherein n is a positive integer greater than 1; training until the output converges to obtain a trained neural network model; and inputting the verification data set into the trained neural network model for verification to obtain a b value prediction model of the overhead line system compensation device.
In one implementation of the present application, the method further comprises: the neural network model is trained by adopting an LSTM model, the training period epochs of the LSTM model is 100, and the batch of each training period is 36.
In one implementation of the present application, the method further comprises: adam is adopted as an optimization algorithm of the neural network model, and average absolute errors are adopted as loss functions.
In an implementation manner of the present application, a hidden layer of a b-value prediction model of a catenary compensation device includes 50 neurons, and an output layer includes 1 neuron.
In an implementation manner of the present application, the operation state monitoring data at a plurality of preset times is input into the b value prediction model to obtain a b value at time T, which specifically includes: inputting the running state monitoring data at the T-n, T- (n-1),. and T-1 moments into a b value prediction model, and encoding the input running state monitoring data at the T-n, T- (n-1),. and T-1 moments to obtain a feature vector; wherein n is a positive integer greater than 1; and decoding the characteristic vector in a preset decoding mode, and outputting a b value at the T moment.
In one implementation of the present application, after obtaining the b value at time T, the method further includes: under the condition that the difference value between the b value at the T moment and the real measured value is larger than a second preset threshold value, the server sends alarm information to the mobile terminal; and uploading the real measured value to a server by the b value detection device. And when the b value changes greatly, an early warning is sent out, so that monitoring personnel can correct data in time, the whole network early warning management is realized, and the larger loss is avoided.
On the other hand, this application embodiment still provides a monitoring facilities of railway contact net compensation arrangement, and equipment includes: a processor; and a memory having executable code stored thereon, the executable code, when executed, causing the processor to perform a monitoring method of the railroad catenary compensation device as described above.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of a compensation device for a railway catenary provided in an embodiment of the present application;
fig. 2 is a flowchart of a monitoring method of a compensation device for a railway catenary provided in an embodiment of the present application;
fig. 3 is a schematic view of an internal structure of a monitoring device of a compensation device for a railway catenary provided in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Since the railway contact net system is eroded by natural environments such as rain, snow, ice, wind and the like all the year round and impacts and vibrations of the pantograph operating at high speed, the mechanical and electrical performance states of the railway contact net system are dynamically changed and work under the dual action of force and electricity, and mechanical and electrical faults constitute main faults of the contact net system. The tension of the contact net compensation device is a key parameter related to stable, reliable and safe operation of a contact net system, and is directly related to the operation state and driving safety of the contact net system.
The running condition and running environment of the railway contact net compensation device are very bad. Due to the change of atmospheric temperature and tension in a thread, equipment corrosion and oxidation are continuous and lengthy processes, the stuck catenary compensation device can be accurately found in the troubleshooting by utilizing the conventional monitoring method, but the device which is corroded and deteriorated cannot be found in time.
At present, the state checking and measuring work of the railway contact net compensating device is mainly carried out by depending on walking inspection of contact net workers, the compensation b value is measured, and then whether the compensation b value meets the requirement or not is checked by calculating or searching an installation temperature curve chart. The supporting weight lump string (500kg) observes whether the pulley rotates and whether obvious clamping stagnation exists, and such a detection mode has many uncontrollable factors, can not accurately judge the size of supporting force, can not correctly analyze the size of tension, and can not judge whether the compensation device is clamped stagnation. Therefore, the current manual patrol inspection compensation detection is not scientific, and the health state of the compensation device cannot be judged correctly.
Except the manual inspection mode, the railway contact net tension compensation device monitoring system mainly utilizes various sensors to acquire the a and b values of the contact net tension compensation device and the current data such as the environmental temperature, the humidity and the wind power, and stores the data in a database, and when analyzing the data, whether the current value is in the temperature variation range through simple inquiry is judged to be out of limit: 1) and comparing the measured value with the preset threshold value to judge the state of the compensation device during subsequent overrun alarm according to the preset threshold value and the initial value of the temperature curve chart. 2) And inquiring data under the same condition in the historical records for comparison and judgment. However, the measurement mode wastes labor cost and cannot give an early warning to the current environmental data.
In summary, the existing monitoring method of the catenary compensation device has a single judgment mode for b-value data, and cannot truly reflect the running state of the current catenary compensation device equipment due to the fact that the existing monitoring method is variable in environment. Moreover, the early warning cannot be predicted in advance according to the current environment, and the prevention control capability is improved.
The embodiment of the application provides a monitoring method and equipment for a railway contact net compensation device, and the b value prediction is completed by constructing a b value prediction model of the contact net compensation device so as to solve the technical problem.
The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic structural view of a railway catenary compensation device provided in an embodiment of the present application, and as shown in fig. 1, the catenary compensation device is installed in series between a catenary and a contact line, and is used for compensating for changes in tension in the catenary. The structure of the device mainly comprises a pull wire, a limiting guide pipe, a compensating pulley, a compensating rope, a balance weight and the like.
The railway contact net compensation device mainly keeps the tension of a cable balanced by the gravity of a weight string. When the temperature changes, the falling weight string is lifted and lowered by the stretching of the clue, and when the falling weight string is lifted beyond an allowable range, the bottom surface of the falling weight string is contacted with the ground by too much lowering, or the compensation device loses the compensation function by too much lifting. Therefore, in practical application, the values a and b of the compensation device are used for limiting the lifting range of the pendant string. Wherein, the value a of the contact net compensation device refers to the distance from the center of an ear ring hole of the pendant rod to the lower edge of the compensation (fixed) pulley; the b value of the contact net compensation device refers to the vertical distance between the lower bottom surface of the weight closest to the ground and the ground. The technical scheme provided by the embodiment of the application is that the running state of the contact net compensation device is comprehensively monitored and early warning is realized through predicting the b value.
Fig. 2 is a flowchart of a monitoring method of a compensation device for a railway catenary provided in an embodiment of the present application; as shown in fig. 2, the monitoring method provided in the embodiment of the present application mainly includes the following steps:
The server receives operation state monitoring data of a plurality of contact net compensation device test points in a preset time period. And preprocessing a plurality of running state monitoring data, converting the running state monitoring data into a data format suitable for processing a time sequence problem, and constructing a training data set and a verification data set of the neural network model according to the processed data.
In an embodiment of the application, the server receives operation state monitoring data of a plurality of test points in the last three months, and performs preprocessing processes such as encoding and grouping on the received operation state monitoring data to obtain a training data set and a verification data set of the neural network model.
Wherein, the operation state monitoring data mainly comprises any one or more of the following items: time, temperature, humidity, wind speed, wind direction, longitude and latitude, and b value.
The preprocessing of the received operation state monitoring data mainly comprises the following processes:
firstly, encoding the running state monitoring data.
Because the wind direction data in the operation state monitoring data is in a specific representation mode, such as northeast wind and south wind; wind direction data needs to be coded in a first preset coding mode and converted into numerical information representing the wind direction; the wind speed data is usually represented in a decimal point-containing manner, for example, 5.6m/s, and therefore, the wind speed data also needs to be encoded in a second preset encoding manner. And the data such as temperature, humidity, time, longitude and latitude, b value and the like can be directly identified by the neural network model, so that the encoding processing is not required to be carried out in advance.
In an embodiment of the present application, the first preset encoding method adopts a sklern encoding method; the second preset coding mode adopts integer coding.
And secondly, carrying out normalization processing on the coded data.
And carrying out normalization processing on the coded wind direction data and wind speed data and the data such as temperature, humidity, time, longitude and latitude, b value and the like to obtain a preset format data set.
In one embodiment of the present application, the format of the data after the normalization process is [ samples, time-step, features ]. Samples is the number of samples, and is set to 35039, for example; time-step is a time step, e.g., set to 1; features are attribute feature dimensions, here 7 attribute features, which are temperature, humidity, time, longitude and latitude, b value, wind speed, and wind direction, respectively.
And thirdly, grouping the prediction format data sets obtained after the normalization processing.
Screening the data set obtained after normalization processing, and then removing data obviously not conforming to the actual application scene; and grouping the data sets in a preset mode to obtain a training data set and a verification data set of the neural network model.
After the training data set and the verification data set are obtained, the training data set is used for training the neural network model, then the verification data set is used for verifying the trained neural network model, and a b value prediction model of the overhead line system compensation device is obtained.
In one embodiment of the application, the running state monitoring data at t-n, t- (n-1),. and.1 in the training data set and the b value at t are input into the neural network model, and the neural network model is trained; wherein n is a positive integer greater than 1; and training until the output is converged to obtain the trained neural network model.
In one embodiment of the present application, the neural network model may be trained using a Long short term Memory network (LSTM). The hidden layer comprises 50 neurons, and the output layer comprises 1 neuron.
Further, Adam is adopted as an optimization algorithm of the LSTM neural network model, and average absolute error is adopted as a loss function.
Further, epochs of the LSTM neural network is set to 100 and batch is set to 36. For example, when an LSTM neural network model is trained, a complete training data set passes through the primary neural network model, and a weight is output once and recorded as the completion of an epochs; when the training data set is input into the neural network model every time, if the training data set is input in a one-time mode, the calculated amount of the neural network model is too large, and the output time is long; therefore, the training data set is divided into 36 batches, namely 36 batchs, and the 36 batches are respectively input into the neural network model for training.
And finally, inputting the verification data set into the trained neural network model for verification to obtain a b value prediction model of the overhead line system compensation device.
And step 203, predicting the b value at the T moment by using a b value prediction model.
And inputting the running state monitoring data at a plurality of preset moments into a b value prediction model to complete the prediction of the b value at the T moment.
In one embodiment of the application, the operation state monitoring data at the time of T-n, T- (n-1),.. and T-1 is input into a b value prediction model, and the input operation state monitoring data at the time of T-n, T- (n-1),. and T-1 is encoded to obtain a feature vector; wherein n is a positive integer greater than 1; then, the feature vector is decoded in a preset decoding mode, and a b value at the time T is output.
For example, all the operation state monitoring data at T-3, T-2 and T-1 are input into a b value prediction model, and the b value at T is predicted.
And step 204, sending alarm information when the value b is larger than a first preset threshold value.
After the b value at the time T is obtained, if the predicted b value is larger than or equal to a preset threshold value, namely the predicted b value exceeds the limit, alarm information is directly sent out to give an alarm.
In one embodiment of the application, if the difference value between the predicted b value and the actual measurement value is greater than a second preset threshold value, alarm information is also directly sent to the mobile terminal. And uploading the real measured value to a server by the b value detection device.
It will be clear to those skilled in the art that the actually measured b value is only the b value uploaded by the b value measuring device or the measuring sensor, and the influence of environmental or meteorological factors on the compensation device for a long time is not comprehensively considered. The b value obtained by the prediction of the b value prediction model provided by the embodiment of the application is effectively integrated with the information of environment, weather and the like, and the running state of the compensation device can be truly and comprehensively reflected. Therefore, when the predicted b value exceeds the limit or the difference value between the predicted b value and the actual measured value is large, the influence of factors such as environment and weather on the compensating device for a long time is explained, namely, the operating state of the compensating device is changed, and then the compensating device is in failure.
Therefore, the embodiment of the application provides that when the value b exceeds the limit, or the difference value between the value b and the real measured value is too large, alarm information is sent out, so that monitoring personnel can find problems possibly occurring in the running state in time, correct data in time, and avoid causing greater loss.
The embodiment of the method provided by the application is based on the same inventive concept, and the embodiment of the application also provides monitoring equipment of the railway contact network compensation device, wherein the internal structure of the monitoring equipment is shown in fig. 3.
Fig. 3 is a schematic view of an internal structure of a monitoring device of a compensation device for a railway catenary provided in an embodiment of the present application, and as shown in fig. 3, the monitoring device includes: a processor 301 and a memory 302, on which executable code is stored, which when executed causes the processor 301 to perform a method of detecting a railroad catenary compensation device as described above.
In an embodiment of the present application, the processor 301 is configured to receive operation state monitoring data of a plurality of contact network compensation device test points in a preset time period; the system is also used for constructing a training data set and a verification data set of the neural network model according to the operation state monitoring data, training the neural network model through the training data set, and verifying the neural network model through the verification data set to obtain a b value prediction model of the overhead line system compensation device; the value b is the vertical distance between the bottom surface of a first balance weight of the contact net compensation device and the ground, and the first balance weight is the balance weight closest to the ground in a balance weight string; the system comprises a b value prediction model, a data acquisition module, a data processing module and a data processing module, wherein the b value prediction model is used for inputting running state monitoring data at a plurality of preset moments into the b value prediction model to obtain a b value at T moment; wherein the time T is after a plurality of preset times; and the alarm information sending module is used for sending alarm information to the mobile terminal under the condition that the value b at the time T is greater than a first preset threshold value.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A monitoring method of a railway contact network compensation device is characterized by comprising the following steps:
the method comprises the steps that a server receives running state monitoring data of a plurality of contact net compensation device test points in a preset time period;
according to the running state monitoring data, a training data set and a verification data set of a neural network model are constructed, the neural network model is trained through the training data set, and the neural network model is verified through the verification data set, so that a b value prediction model of the overhead line system compensation device is obtained; the value b is the vertical distance between the bottom surface of a first balance weight of the contact net compensation device and the ground, and the first balance weight is the balance weight closest to the ground in a balance weight string;
inputting the running state monitoring data at a plurality of preset moments into the b value prediction model to obtain a b value at T moment; wherein the time T is after the preset times;
and sending alarm information to the mobile terminal under the condition that the value b at the time T is greater than a first preset threshold value.
2. The monitoring method of the compensation device for the railway contact network as claimed in claim 1, wherein the operation state monitoring data mainly comprises any one or more of the following items: time, temperature, humidity, wind speed, wind direction, longitude and latitude, and b value.
3. The monitoring method of the compensation device for the railway catenary according to claim 1, wherein the constructing of the training data set and the verification data set of the neural network model according to the operation state monitoring data specifically comprises:
encoding the wind speed data in the running state monitoring data in a first preset encoding mode; the wind direction data are coded through a second preset coding mode;
performing normalization processing on time data, temperature data, humidity data, longitude and latitude data, b value data, coded wind direction data and coded wind speed data in the operation state monitoring data, and storing the processed data as a data set to be grouped in a preset format;
and after the abnormal data in the set of data to be grouped is removed, grouping is carried out to obtain a training data set and a verification data set.
4. The method for monitoring the compensation device of the railway catenary according to claim 1, wherein the training of the neural network model through the training data set and the verification of the neural network model through the verification data set to obtain the b-value prediction model of the compensation device of the catenary specifically comprise:
inputting the running state monitoring data at t-n, t- (n-1) and t-1 in the training data set and the b value at t into a neural network model to train the neural network model; wherein n is a positive integer greater than 1;
training until the output converges to obtain a trained neural network model;
and inputting the verification data set into the trained neural network model for verification to obtain a b value prediction model of the overhead line system compensation device.
5. The method for monitoring the compensation device of the railway contact network as claimed in claim 4, wherein the method further comprises the following steps:
the neural network model is trained by adopting an LSTM model, the training period number epochs of the LSTM model is 100, and the batch of each training period is 36.
6. The method for monitoring the compensation device of the railway contact network as claimed in claim 4, wherein the method further comprises the following steps:
adam is adopted as an optimization algorithm of the neural network model, and average absolute errors are adopted as loss functions.
7. The monitoring method of the railway contact network compensation device as claimed in claim 4, wherein the hidden layer of the b-value prediction model comprises 50 neurons, and the output layer comprises 1 neuron.
8. The monitoring method of the compensation device for the railway catenary according to claim 1, wherein the step of inputting the operation state monitoring data at a plurality of preset moments into the b value prediction model to obtain the b value at the T moment comprises the following specific steps:
inputting the running state monitoring data at the T-n, T- (n-1),.. and T-1 moment into the b value prediction model, and coding the input running state monitoring data at the T-n, T- (n-1),. and T-1 moment to obtain a feature vector; wherein n is a positive integer greater than 1;
and decoding the characteristic vector in a preset decoding mode, and outputting a b value at the T moment.
9. The method for monitoring the compensation device of the railway contact network as claimed in claim 1, wherein after obtaining the b value at the time T, the method further comprises:
under the condition that the difference value between the b value at the T moment and the real measured value is larger than a second preset threshold value, the server sends alarm information to the mobile terminal;
and uploading the real measured value to a server by the b value detection device.
10. A monitoring device of a railway contact network compensation device is characterized by comprising:
a processor;
and a memory having executable code stored thereon, which when executed causes the processor to perform a method of monitoring a railroad catenary compensation arrangement of any of claims 1-9.
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| CN112541228A (en) * | 2020-12-10 | 2021-03-23 | 重庆交通大学 | Pantograph active control method capable of memorizing network prediction of contact force duration |
| CN116337154A (en) * | 2023-03-27 | 2023-06-27 | 成都铁路科创有限责任公司 | Contact net health state monitoring method and system |
| CN116337154B (en) * | 2023-03-27 | 2024-04-19 | 成都铁路科创有限责任公司 | Contact net health state monitoring method and system |
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