Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems
Research output: Contribution to journal › Article
Colleges, School and Institutes
Railways are expected to operate with ever increasing levels of availability, reliability, safety and security. One way of ensuring high levels of dependability is through the use of condition monitoring systems. This paper presents the results of research on fault detection and diagnosis methods for railway track Circuits. The proposed method uses a hybrid quantitative/qualitative technique known as a neuro-fuzzy system. Such a hybrid fault detection and diagnosis system combines the benefits of both fuzzy logic and neural networks, i.e. the ability to deal with system imprecision and to learn by neural network training processes. It is shown that the proposed method correctly detects and diagnoses the most commonly occurring track circuit failures ill a laboratory test rig of one type of audio frequency jointless track circuit. (c) 2007 Elsevier Ltd. All rights reserved.
|Number of pages||12|
|Journal||Control Engineering Practice|
|Publication status||Published - 1 May 2008|
- fault diagnosis, railways, neural networks, fuzzy systems, fault detection