=Paper=
{{Paper
|id=Vol-2556/paper4
|storemode=property
|title=The Use of Self-Learning Systems to Solve the Problems of Finding Failures on the Railway
|pdfUrl=https://ceur-ws.org/Vol-2556/paper4.pdf
|volume=Vol-2556
|authors=Ekaterina A. Blagoveshchenskaya,Sergey V. Bochkarev,Nikolay V. Gruzdev,Denis V. Zuev
}}
==The Use of Self-Learning Systems to Solve the Problems of Finding Failures on the Railway ==
https://ceur-ws.org/Vol-2556/paper4.pdf
The Use of Self-Learning Systems to Solve the Problems of
Finding Failures on the Railway
Blagoveshchenskaya E.A., Sergey V. Bochkarev
Emperor Alexander I St. Petersburg State LLC "GC IMSAT", Saint-Petersburg, Russia
Transport University, bochkareffsv@yandex.ru
kblag2002@yahoo.com
Peter the Great St. Petersburg State Polytechnic
University
Nikolay V. Gruzdev Denis V. Zuev
LLC SvyazStroyServis, Saint-Petersburg, Russia LLC "GC IMSAT", Saint-Petersburg, Russia
Nik_gru@mail.ru zuevdv@gmail.com
1 Introduction
The share of railway freight turnover in the
transport system of Russia is 45%, while the growth
of freight traffic of Russian Railways continues, in
Abstract 2018 it amounted to 2596.9 billion ton-kilometers.
By order of the government dated March 19, 2019
The most frequent reason for the violation No. 466-r, a long-term development program for
of the train schedule is the failure of the Russian Railways until 2025 was approved, which
technical facilities of the infrastructure provides for the transition to a digital railway. In
complex. The number and duration of train accordance with this, the active development of
downtime and, as a result, the economic technical diagnostic and monitoring systems
losses of Russian Railways depends on the (STDM) continues.
time of search and elimination of failures. Technical diagnostics is the determination of the
Today, failure search is carried out in an technical condition of an object [Efa12]. The object
intuitive way. In practice, such a path leads of railway automation and telemechanics can be in
to unnecessary time costs.In the study of one of the following states [Efa12] (Figure 1):
methods for constructing algorithms, they 1) Intact - this is the state of the facility, during
were classified in terms of the possibility of which it meets all the requirements established in
constructing a model in which each step is a the normative and technical documentation for it.
function of all previous steps and the 2) Faulty - the state of the facility in which it
functions cover the entire space of failures. does not meet at least one of the requirements
We built these functions and described the established in the normative and technical
generation model of such functions. Such a documentation for it.
model for constructing functions can be 3) Workable - the state of an object in which the
applied to any technical branch. For the values of all parameters characterizing the ability to
railway infrastructure, 6 functions were perform specified functions meet the requirements
obtained. An automatic self-learning system established in the regulatory and technical
has been developed, which is a multilayer documentation for this object.
neural network constructed according to a
recurrence model. Based on this model, a
hardware-software complex was
implemented and tested.
1
Copyright c by the paper's authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0). In:
A. Khomonenko, B. Sokolov, K. Ivanova (eds.): Selected Papers
of the Models and Methods of Information Systems Research Figure 1. The technical condition of the
Workshop, St. Petersburg, Russia, 4-5 Dec. 2019, published at facilities
http://ceur-ws.org
21
4) Inoperative - a state in which the value of at number of pre-failure conditions accumulate, since
least one of the parameters characterizing the any minor changes in the diagnostic parameters (for
ability to perform specified functions does not meet example, voltage) are noted by the system [Kal09,
the requirements in the normative and technical Efa12] (Figure 2). The fixation of “false” pre-
documentation for this object. failure conditions can lead either to a failure, which
5) Failure - the state of the object, characterized by can cause a violation of the safety and uninterrupted
an increased risk of its failure [Efa12, Boc12]. operation of trains, or to a “false” response of
Failure - an event consisting in the violation of the service personnel, which will lead to an increase in
operational state of the object. In the absence of labor costs.
logical analysis and analytical forecasting, a large
Figure 2. Distribution of pre-cancers resulting from STDM deficiencies
To improve quality and reduce troubleshooting With an increase in cargo turnover, the
time, monitoring systems for railway automation throughput of railways should be increased, which
and telemechanics are being actively developed. requires serious resources. In 2018 alone, about 300
Directions for the development of technical billion rubles were spent on the development of the
diagnostics and monitoring of HEAT: railway infrastructure of Russian Railways. But all
1) A full range of measurements. investments are leveled in cases of technical
2) Analysis of the operation of devices. equipment failure.
3) Forecast of changes in the status of devices. The most common cause of train schedule
4) Issuing service recommendations devices. disruptions is the failure of infrastructure facilities.
5) Coverage of all devices with diagnostic tools. The number and duration of train downtime and, as
To date, the disadvantages of technical diagnosis a consequence, the economic losses of Russian
include: - “manual” processing of diagnostic Railways depend on the search and elimination of
information, which leads to an increase in the time failures.
for its analysis and decision making; - lack of To date, the search for failures is carried out
identification of the reasons for the failure; - lack of intuitively. In practice, this way leads to
evidence-based methods for fixing pre-failure unnecessary time costs.
conditions; - lack of troubleshooting algorithms
built into STDM; - lack of an optimal set of 2 Failure search process
controlled diagnostic parameters, which leads to
low reliability of determining the technical The analysis of the revealed failures shows that
condition. their main reason is a violation of the technological
Therefore, expanding the functionality of systems process of operation (operational failures).
for identifying the causes of failure, determining the Operational failures account for up to 86% of all
optimal algorithm for troubleshooting and failures of
determining the optimal set of monitored railway AT devices [Ana12, Ana09, Sap02].
parameters is an urgent task. According to statistics, most of the operational
failures cause train delays.
22
Despite a number of measures to increase the improve its quality, and this is only possible by
reliability of railway AT devices, which include increasing the level of its (technological process)
scheduled preventive maintenance, the organization automation.
of new maintenance methods [Aks09], and training The troubleshooting process takes place in the
of maintenance personnel rules and troubleshooting following sequence: After the failure information
methods, the time to search for localization and appears, preparatory steps for troubleshooting are
troubleshooting remains relatively large. started: identification of the failed device,
The long time to find and eliminate the failure is collection and analysis of additional information,
explained by a number of objective and subjective study of technical documentation, analysis of the
factors. Objective factors include territorial train situation to localize the location of the failure
dispersal, difficult access to some outdoor signaling and its nature, then collection of necessary tools
devices, and sometimes lack of complete technical and materials. Delivery of the employee to the
documentation. Subjective factors include the lack place. Additional checks and elimination of the
of experience and qualifications of the service detected malfunction are carried out on site (Figure
personnel of the distance, the inability to read the 3).
schematic and wiring diagrams.
Reducing the influence of the human factor on the
technological process is a necessary measure to
Informing Preparing to Arrival Search Troubleshooting
search
- from - afoot;
chipboard; - determination of the place and - transport. - measurements,
- from nature of the failure; - replacement of faulty
inspections and their
SHCHD. - determination of the failed elements of the device,
analysis.
device; adjustment;
- collection and analysis of - check the working
diagnostic information, tools and good condition.
materials;
- study of technical
documentation.
Figure 3. The process of recovering the operability of the device
But there are situations that an electrician has t’av.p - troubleshooting time taking into account
difficulty finding a malfunction, for example, he STDM (14 min). ttz - technical delay (5 min).
incorrectly localized the location of a malfunction It is worth noting that the preparatory period is the
search: he searches for a malfunction in the field longest from the beginning of the failure to its
when the malfunction is in the relay room. . In such elimination. The safety and security of the railway
situations, the role of automated algorithms is transport depend on the knowledge and skills of the
increasing, which systematize and structure employee. Therefore, we have directed efforts to
troubleshooting. minimize the human factor.
Let us compare the recovery times of the function The ideal model for eliminating the problem of
of operability of devices without and with Heinrich Saulovich Altshuller suggests that the
automated algorithms for troubleshooting. problem should be eliminated by itself,
Average recovery time of TV function of automatically. To solve the problem in this way,
operability of devices of railway automation and automatic troubleshooting is implemented.
telemechanics systems in 1 region on the Moscow
Railway: 3 Failure search algorithm
Tv = topv + tav.p + tav.u + ttz = 40 min.
where topv is the time of notification of a There are 2 ways to compile a failure search
malfunction (1 min); algorithm [Bla19]:
tav.p - average time for troubleshooting (20 1) to paint all possible algorithms and
min); depending on the input data to give the optimal
tav.u - average time for troubleshooting (9 min); search path. Although there are a finite number of
ttz - technical delay (10 min). such algorithms, from a practical point of view it is
The development and implementation of automated large and such a path is not reasonable.
troubleshooting algorithms minimizes the value of 2) The second way is to build a dynamic,
tav.p. when this time tends to a minimum, the ratio recurrent model, where each step will be a function
will take the following form: of all previous steps. But to build such an algorithm
Tv = topv + t’s av.p + tav.u + ttz = 29 min. for all cases is also not optimal. Therefore, we
23
classified all types of algorithms from the point of classification problem in the case of many classes,
view: the possibility of constructing such functions it is recommended to use nonlinear classifiers
and covering the functions of the entire failure [Kru01], i.e. classifiers that use a nonlinear surface
space. We built these functions and described a to separate classes. An example of such classifiers
model for generating such functions. For the is a neural network.
railway infrastructure were obtained 6 functions. A neural network is a distributed parallel processor
Such a model for constructing functions can be consisting of interconnected elementary
applied to any technical industry. information processing units (neurons) that
In general, classification methods [Dmi86, accumulate experimental knowledge for their
Zor07] can be attributed to the following types: subsequent processing [Kru07, Cal01]. Neurons are
1. The linear classifier method. implemented by a nonlinear function of one
2. The method of nonlinear classifier. argument-the weighted sum of all input signals.
3. The method of constructing decision trees. This function is called the activation function. The
The linear classifier [Kal09] allows one to set of interconnected neurons determines the
determine the linear dividing surface. In the case of structure of the network and the tasks that the
two classes, such a surface is a hyperplane dividing neural network is able to solve. The weights that
the space of attributes into two half-spaces. Linear characterize the strength of the connection between
classifiers include the support vector method, two neurons are called SYNOPTIC coefficients.
Bayesian classifier, and other methods. The process of selecting SYNOPTIC coefficients is
In the method of support vectors [Kly87, Par13], a called network learning [Cal01, Hai06]. In the
set of training examples is proposed for each state, process of training, an array of input values (class
given as points in multidimensional space. These attributes) is mapped to each class. Neural networks
points form regions in space corresponding to are able to generalize information obtained during
different classes. The extreme points of a class are training. Also, the advantage of using neural
called reference points, and the distance between networks is the absence of the need to adjust the
the two reference points is the length of the algorithms when changing the number or
reference vector. It is required to find such a characteristics of classes. The disadvantage of using
hyperplane that the length of the support vectors is neural networks can be a large computational
maximal. For the application of this method complexity [Sim94] when using complex network
requires that the classes were linearly separable structures (for example, convolutional neural
among themselves. The disadvantage of the method networks [Cal01, Hai06, Sim94]).
is that it is suitable for solving the classification The method of constructing decision trees allows to
problem with only two linearly separable classes. construct a visual algorithm of object classification.
To solve a problem with a large number of classes, The decision tree consists of nodes (also called
the division of the problem into subtasks of vertices) and branches connecting the nodes. The
classification according to the scheme "one-against- very first node is called the root of the tree, and the
the-others" is used. It is necessary to solve the extreme nodes are called leaves. Each vertex is
problem of combining the results. mapped to some characteristic that describes the
The Bayesian classifier [Sap04] is a method based object, and the branches-the value area of this
on the theorem stating that if the density of the characteristic. The procedure for constructing a
distribution of each class is known, then the decision tree is an iterative process in which the
required algorithm can be written out in an explicit sign that best satisfies a certain branching criterion
analytic form. For each of the classes, likelihood is selected for the next vertex of the tree [Hai06].
functions are defined, by which the a posteriori The branching criterion is selected depending on
probabilities of the classes are calculated. The the algorithm used. Popular algorithms for building
object belongs to the class for which the a posteriori a decision tree are ID3 [Hai06, Sim94] (or its
probability is maximal. As a rule, in practice, the improved version C4. 5 [Hai06, Sim94]) and CART
density of the distribution of classes are unknown, [Hai06, Sim94]. The difference between these
and they have to be collected from the training algorithms is in the way the branching feature is
sample. Recovery is possible only with some selected. The advantage of decision trees is the
sinfulness, and the smaller the training sample, the visibility of the resulting model and the simplicity
higher the probability of a retraining effect, when of its interpretation by a person. The disadvantage
the method loses its generalizing properties and of the method is the problem of retraining, i.e. the
correctly classifies only the examples from the possibility of constructing an excessively large tree
training sample. Also, the efficiency of the method that will not fully represent the data. There is also a
drops sharply when there is an error in the need to build a tree from scratch (i.e. a complete
hypotheses about the density of the class change in the diagnostic algorithm) when changing
distribution. the number of classes and the description of the
Linear classifiers are effective for classification input data.
problems with two classes. To solve the
24
Neural networks provide multi-class classification problem, teacher training is used. The most popular
regardless of the linear time-separability of classes. algorithm for learning with a teacher is The
In addition, neural networks are able to determine backpropaganation algorithm [Sim94], based on the
the presence of the analyzed example features of gradient descent method on the hyperplane of the
several classes. The effectiveness of neural error function, and its modified version RProp
networks for classifying the technical condition of [Cal01, Hai06, Sim94], which is one of the best
devices is shown in [Zue12, Zue13]. Therefore, it first-order learning algorithms.
was decided to apply the theory of neural networks The process of recovering the operability of the
for the development of methods and algorithms for device with an automatic self-learning system,
fault detection. which is a multi-layer neural network based on a
In order to solve the classification problem with the recurrent model is presented in Figure 4)
help of a neural network, the network needs to be As a result, we changed the Troubleshooting
taught examples of different images. The training process. There was a unification of information
sample should include measures that fully describe flows, automated process of preparation for
the image. In practical problems, to achieve the Troubleshooting, calculation and issuance of the
most complete description of possible images in the optimal algorithm of actions.
training sample, it is necessary to collect a In the course of elimination of failure there is an
sufficient number of examples. Examples can be adjustment of the optimal algorithm at change of
located in different parts of the corresponding input data (by results of measurements, change of a
image area in DK space. train situation, the carried-out tests),
To solve the problem of neural network training, recommendations are given, after elimination check
there are many algorithms [Cal01, Hai06]. To train of operability is carried out.
neural networks designed to solve the classification .
Inform Preparation Arrival Search Troubleshooting
- fromDSP; to search
- from - afoot;
SHCHD; - the collection of tools - transport. - replacement of faulty
- carrying out
- from STDM. and materials. elements of the device,
additional
adjustment.
measurements (absent
in STDM), checks.
Preparation Search Troubleshooting
to search
- carrying out
- determination of the location additional - check the working
and nature of the failure; measurements that are good condition.
- determination of the failed not present in the
device; STDM, and their
- collection and analysis of analysis
diagnostic information in
STDM;
- train situation analysis;
- analysis of technical APK-PN
documentation.
Figure 4. The process of recovering the operability of the device with an automatic self-learning system
4 Description of APK-PN system - reduced operating costs;
- development of information exchange between
The APK-PN system with a mobile measuring and adjacent offices.
software complex is designed for automatization of The main objectives of creation of APK-PN are:
fault finding of JAT devices, logging of the fault - reduced Troubleshooting time;
finding process, checking the device operability - reducing the number of train delays and their
after Troubleshooting and providing information to duration;
operational personnel. Implementation of APK-PN - reducing the impact of human factors on the
assumes achievement of the following results: Troubleshooting process;
- reducing the recovery time of the device's - reducing the complexity of work to restore the
working capacity; operability of railway automation and
- improvement of safety and continuity of trains; telemechanics devices;
25
- reduction of "post-preventive" failures. Devices for which the APC-PN system implements
To automate the above operations, the APK-PN the construction of fault finding algorithms:
system provides: - centralization of arrows and signals;
- collection of information from STDM; - track blocking;
- download technical documentation from - moving and barrage centralization;
ARM-VTD; - the formation and transmission of the signals
- display the search algorithm of fault; of ALSN;
- selection of possible faulty elements on circuit - other devices had been controlled by the
diagrams and indication of additional measurement STDM on the basis of the APK-DK.
points; On the basis of the General formulated
- logging of the process of finding non- requirements to system APK-PN the hierarchical
rightness; principle of construction is chosen and two levels
- integration with existing control systems, are allocated:
interaction with ASU-SH2 databases. - linear data collection point (LPS), which is a
On the basis of the above tasks and goals, we mobile measurement and software system (level 1);
present the structure of the APK-PN system. - the Central point of construction of algorithms
System APK-PN includes subsystems: for fault claim in the distances of signaling,
- collection of information in STDM; centralization and blocking (level 2).
- downloads of technical documentation for the The hierarchical structure of the APC-PN
railway AT devices; system is shown in Figure 5.
- analysis of diagnostic information from STDM At level 1, there are linear points for collecting
and technical documentation from ARM-VTD; additional diagnostic information (LPS),
- construction and display of the fault finding performing additional measurements of diagnostic
algorithm of the railway AT device, as well as the parameters of the control object and receiving
image of connection points for additional functions from the Central point of the fault finding
measurements and selection of the checked algorithm.
elements on the technical documentation In case of absence of technical documentation on
(schematic diagram). the failed device, the standard scheme for the
- check the operation of the railway AT device corresponding EC or AB system in which the
after troubleshooting; device is operated is loaded.
- measurement of diagnostic parameters and .
their analysis.
CTDM
Level 2
ASU-SH2 ARM-VTD
ARM-PN, ARM-SHCHD
(Central point)
Level 1 Level 1 (LPD)
Mobil ARM-SHN
MPK ARM-SHNS
(LPS) Concentrator
LPD
Devices Railway
АТ
Figure 5. Block diagram of APK-PN
After receiving the full amount of information, the necessary to carry out checks in order to reduce the
APK-PN begins synthesis of the algorithm for search time is issued. In case of insufficient amount
searching for faults on the basis of its knowledge of diagnostic information to form a list of the
base and a list of the minimum number of possible minimum number of possible faulty elements, the
irregular elements in the sequence in which it is APK-PN synthesizes an algorithm for additional
26
measurements of diagnostic parameters of the
device to reduce the search area and identify the
faulty element. It also provides for the selection of
elements in the schematic diagrams of the device
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