The motivation for this paper is the need for improved reliability/availability of different electrical drives in a locomotive for improving the reliability and quality of service to the passengers as well as the requirement for cost-effective and more efficient condition-based maintenance management. A failure of a locomotive online not only affects the profits of the railways but also adversely affects the image of the system. To avoid failure of locomotives online, various strategies are devised but a zero failure scenario has not been achieved so far. To achieve the reliability of the highest order, various maintenance practices are being used for different components. This paper deals with the maintenance practice based on the online condition monitoring of different components so that the timely and cost-effective attention is paid to the equipment whenever it is needed. This predictive maintenance strategy can be further incorporated with some intelligent monitoring system like Artificial Neural Networks (ANN) or fuzzy logic. The embedded software would implement the ANN-based learning algorithms which model the behavior of the equipment during healthy operation and provide `pre-failure warning' when there is a significant deviation from the expected performance.

RCM can be described as a "systematic, zero-based review of what must be done to ensure that all key assets continue to perform as their users want them to perform". The RCM process involves identifying assets with poor reliability history, a comprehensive Fault Mode and Effects Criticality Analysis (FMECA) and an appropriate action to tackle each fault process. Critics believe that this may not be always cost effective, though tools are being developed to address RCM's cost-benefit analysis.

ATM, also sometimes called availability centered maintenance or proactive maintenance, is based on review of current procedures with the specific aims of improving availability by targeting assets which are more likely to disrupt the production/service and are critical in overall operations.

This approach is based on considering the maintenance issue as an integral element of the total operation process and applies techniques similar to Total Quality Management (TQM). Electronic condition monitoring is an essential element for the implementation of this maintenance practice.

Electrical and Electronics Engineering Journal, Artificial Neural Networks, ANN, Reliability-Centered Maintenance, RCM, Availability Targeted Maintenance, ATM, Total Productive Maintenance, TPM, Total Quality Management, TQM, Statistical Parameters, Vibration Patterns, Fault Mode and Effects Criticality Analysis, FMECA.