The paper deals with the research carried out at the University of Birmingham, UK and funded by London Underground Limited (LUL) into early-failure warning systems for safety-critical railway signalling equipment. The paper outlines the motivation for the research, a brief overview of the requirements for condition monitoring systems, a summary of various condition monitoring and fault diagnosis used in the study. The process of laboratory tests and field trials which led to the development of ideas and techniques for the employment of intelligent neural network-based sensors for online condition monitoring of railway equipment are briefly reported. Problems still to be tackled are addressed. (3 pages)
On-line condition monitoring of railway neural networks-based intelligent sensors
1995-01-01
1 pages
Article (Journal)
English
signalling , intelligent sensor , safety , London Underground , computerised monitoring , railways , neural networks , railway , safety systems , alarm systems , fault diagnosis , neural nets , real-time systems , intelligent sensors , University of Birmingham , traffic engineering computing , online condition monitoring , early-failure warning systems
On-line condition monitoring of railway equipment using neural networks
IET Digital Library Archive | 1995
|On-line condition monitoring of railway equipment using neural networks
British Library Conference Proceedings | 1995
|Condition monitoring of railway single-throw equipment employing neural networks
British Library Conference Proceedings | 1996
|Intelligent condition monitoring of railway signalling equipment using simulation
IET Digital Library Archive | 1998
|Intelligent railway transport radio communication based on neural networks
DOAJ | 2021
|