Artificial neural network based detection of neutral relay defects

Dnipro National University of Railway Transport named after Academician V. Lazaryan, dept. of automatic and telecommunication, 49010 Dnipro, Lazaryan Street 2, Ukraine

^* Corresponding author: vl.havryliuk@gmail.com

Abstract

The problem considered in the work is concerned to the automatic detecting and identifying defects in a neutral relay. The special design of electromechanical neutral relays is responsible for the strong asymmetry of its output signal for all possible safety-critical influences, and therefore neutral relays have negligible values of dangerous failures rate. To ensure the safe operation of relay-based train control systems, electromechanical relays should be periodically subjected to routine maintenance, during which their main operating parameters are measured, and the relays are set up in accordance with technical regulations. These measurements are mainly done manually, so they take a lot of time (up to four hours per relay), are expensive, and the results are subjective. In recent years, fault diagnosis methods based on artificial neural networks (ANN) have received considerable attention. The ANN-based classification of relay defects using the time dependence of the transient current in the relay coil during its switching is very promising for practical utilization, but for efficient use of ANN a lot of data is required to train the artificial neural network. To reduce the ANN training time, a pre-processing of the time dependence of relay transient current was proposed using wavelet transform and wavelet energy entropy, which makes it possible to reveal the features of the main defects of the relay armature, contact springs, and magnetic system. The effectiveness of the proposed approach for automatic detecting and identifying of the neutral relays defects was confirmed during testing of the relays with various artificially created defects.