The problem considered in the work is concerned to the methods to reveal and identify the signal disturbances occurred in the railway cab signaling system caused by electromagnetic interference from traction current. The continuous automatic locomotive signalling system (ALSN) is a type of cab signalling systems that provides track status information to the train cab and uses the rails as a continuous communication channel between track and train. To ensure the reliability of the transmission of ALSN commands to the train cab, the basic parameters of the coded current are periodically checked for compliance with certain requirements during planned test trips of a specially equipped railway car-laboratory. The ALSN signal is received by the coils of the car-laboratory and recorded by computer. Then an operator visually analyzes the recorded signal to detect problem segments with interference exceeding the limit level what requires a lot of time and does not provide the necessary accuracy. To automate the detection and identification of the ALSN signal disturbances, the following algorithm has been proposed. At the first stage, the wavelet packet energy Shannon entropy is used to reveal problem segments of ALSN signal. At the second stage, a detailed analysis of the ALSN signal using DWPT is performed to determine the type and parameters of interference at the revealed problem segments. To classify ALSN signal disturbances, an artificial neural network was used. The efficiency of the proposed algorithm was verified by processing of several specially simulated as well as real ALSN signals measured during tests.

Wavelet Based Detection of Signal Disturbances in Cab Signalling System

The problem of revealing and identifying of signal disturbances in audio frequency track circuits is considered in the article. Track circuits are a key component of automatic train control system and used to detect whether the rail section is free or occupied. Amplitude-modulated signals used in track circuits have low noise immunity, therefore the power wide-band-frequency interference generated by an electrified railway can cause failure operation of track circuits. To ensure the safety of the track circuits, they are subjected to periodic maintenance with registration of the signal current to the computer and measurements of its parameters. The detection of time intervals during which the interference in the track circuits exceeds certain limit values is performed by simple visual analysis of the recorded signal and is time consuming and expensive. In order to automate the revealing and identification of disturbances in audio frequency track circuits with values exceeding the certain level, a new method for processing of the recorded signal have been proposed. At the first stage, the segments of a signal with interference exceeding a certain permissible level are revealed using the wavelet packet energy Shannon entropy. At the second stage, for the revealed segment with disturbances, a detailed analysis of the signal should be carried out using the digital wavelet packet transform to determine the type and parameters of the disturbances. For automatic identification of the track circuits signal disturbances, an artificial neural network classifier has been used. As a result of investigation the features of the signal disturbances in audio frequency track circuits have been extracted. The processing of the track circuits signals in accordance with the proposed method show high effectiveness in the revealing and identification of the signal disturbances.

Audio Frequency Track Circuits Monitoring Based on Wavelet Transform and Artificial Neural Network Classifier

Введение в вейвлет-анализ для физиков

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Diagnostics of railway turnout systems using tools of centralization blocking system

In railway signaling systems, some parameters required for monitoring and controlling train movements are transmitted using amplitude shift keying (ASK) signals. ASK signals are more often used in railway automated systems due to their simpler generation by contact transmitters, but such signals have a low level of immunity and interference in them can lead to incorrect operation of railway signaling systems. The efficiency of using the signal decomposition method by wavelet transform to reveal distortions for continuous ASK railway signals have been investigated in the work. Simulated signals were generated as a pure ASK signals with a carrier frequency of 420 Hz and a modulation frequency of 8 Hz, as well as five variants of the original signal with disturbances in the form of signals with distorted signal shape, signals with swells, signals with sags, signals with 50 Hz interferences, and signals with sharp pulses . The signal segments of ten periods were decomposed using a five-level wavelet packet transform (WPT). For all investigated signals, the distortions or interferences in them lead, not only to changes in distribution of partial energies at the nodes, but also to the appearance of strong differences in the form of oscillations of the WPT coefficients. The variations in WPT coefficients in low-frequency nodes reflect changes in the waveform due to pulse distortion, sags, swells, etc. The obtained results confirm the efficiency of using the wavelet decomposition method in order to detect distortions in the ASK continuous railway signals.

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Using the wavelet decomposition method for monitoring of amplitude-manipulated signals of railway automation

Purpose. The existing system of automatic locomotive signaling (ALS) was developed at the end of the last century. This system uses the principle of a numerical code which is implemented on the basis of relay engineering, and therefore, it is exposed to various types of interferences. Over the years, the system has been upgraded several times, but the causes of faults and failures in its operation are still the subject of research. It is known that the frequency and the phase modulation of signal has a higher interference immunity as compared to the amplitude modulation. Therefore, the purpose of the article is to study the possibility of using the frequency methods such as Fourier series expansion and wavelet decomposition to extract the informational component of the received code from ALS signals under the action of various types of interferences. Methodology. One can extract the information unavailable in time representation of the signal using the signal studies in the frequency domain. The wavelet decomposition has been used for this purpose. This makes it possible to represent the local characteristics of the signal and to provide time-frequency decomposition in two spaces at the same time. Due to the high accuracy of the signal representation it is possible to analyze the time localization of spectral components and eliminate interference components even in the case of coincidence of interference frequency with the signal carrier frequency. Findings. To compare informativity of the methods of Fourier expansion and wavelet decomposition it was studied the reference and noisy signal of green fire code using the software package MATLAB. Detailed analysis of the obtained spectral characteristics showed that the wavelet decomposition provides a more correct decoding of the signal. Originality. Replacing the electromagnetic relays in the ALS system by microprocessor hardware involves the use of some mathematical tool for decoding, in order to obtain more information about the code. More often than not, as a mathematical tool, the classical Fourier decomposition is used. But because of a number of drawbacks in this method, it was suggested to use the wavelet decomposition, which has a number of advantages and accounts the disadvantages of the Fourier transform. Practical value. The presented method of code signal research can be the basis for developing dynamic model of the ALS receiver and decoder using digital processing module, which will enable to increase the reliability and accuracy of extraction of the code information component.

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Application of fourier transform and wavelet decomposition for decoding the continuous automatic locomotive signaling code

Purpose. In the paper it is necessary to: 1) f ind out the causes of turnouts faults to determine diagnostic features failure s; 2) consider the requirements structure, purpose components of turnouts, work and technology of their maintenance to determine the construction of the economic activities related to system to the turnout’s maintenance; 3) substantiate the possibility, necessity and prospects of automated diagnostics turnout’s implementation; 4) elaborate a prototype of an automated hardware and software system for the turnouts control parameters and perform diagnostics on them. Methodology. In the paper possible turnouts faults were presented and manifestations and influence on its work were shown. According to the current technology works the process analyze of turnouts’ maintenance was conducted, were defined the basic performed operations during the examination of appearance, parameters and check the repair or replacement of parts and assemblies. Based on the analysis of reasons of turnouts malfunctioning and their fixes were systematized types of damages and ways to deal with them, an information scheme of troubleshooting were created, opportunities and limits of automating the process of diagnostics were identified and compared with the existing method of turnouts maintenance. A diagnostics system block diagram was created, an algorithm of its work was developed and established main basic principles of operation. Software and hardware to determine the turnout’s state considering diagnostic performance of points in use were applied. Findings. During the experiment was created a method of automated turnout’s diagnostics with AC electric drives, managed centrally. The results of automated hardware and software system make it possible to control turnout’s parameters and perform diagnostics on them. Originality. Authors created the method of turnout’s state determination by current curve and its spectral composition in the circle of turnout electric motor. The de hardware and software centralized point’s diagnostics complex was developed. Practical value. Thanks to the obtained results the operation safety due to a time workers decrease in the area of train’s movement is increased. The human factor in the performance of measurements is eliminated. Hiding and concealment the facts of turnouts malfunction are not possible.

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V. I. Havrilyuk

Papers

Application of fourier transform and wavelet decomposition for decoding the continuous automatic locomotive signaling code

Implementation of turnouts technical diagnostics systems