Abstract :
PCMSEng have conducted several studies worldwide into the use of predictive maintenance techniques on rolling stock. This study has been carried out at a depot in the UK where maintenance is conducted on a fleet of 28 DMU trains. The DMU fleet was at the time relatively new, where the oldest train in the fleet has been in service for about 18 months. Current maintenance problems that the depot face tend to be more design and contract related and at this stage there are few age related problems encountered in the fleet. Reliability Centred Maintenance process drives maintenance for this fleet and a detailed study has been completed for many of the train types currently under maintenance by current maintainers. The outcome of the RCM study has been used as a foundation for this study. Typically many maintenance practises are driven by OEM recommendations, warranty requirements and contract details and in our experience, little notice is taken of condition based and other advanced asset maintenance practices. These advanced techniques are generally aimed at measuring and controlling factors that may accelerate wear conditions that result in component failure, for example lubricant management and vibration analysis. However, to gain added value from these, often high cost practices, the techniques need to be correctly applied to the right assets and at the appropriate time or frequency. Central to the study is gaining a thorough understanding of the criticality of train assets. Many of the maintenance techniques recommended in this report have relatively high set-up and on-going costs and will give good return on investment only on assets that are highly critical to the business. The criticality of systems will direct the level and frequency of maintenance to be performed, for example, highly critical assets will benefit from high frequency of inspection and on-line monitoring whilst low criticality assets can be left to run to failure without any applied maintenance r- source.The aims of this study were to: Assess the criticality of systems on a typical DMU train; Recommend appropriate condition monitoring techniques, frequency and method of data collection; Estimate return on investment for each recommendation, where possible; Recommend applicable condition monitoring for inclusion in the RCM study.
Keywords :
condition monitoring; preventive maintenance; railway engineering; wear; DMU trains; United Kingdom; advanced maintenance inspection; condition monitoring techniques; lubricant management; predictive maintenance techniques; reliability centred maintenance process; return on investment; wear conditions acceleration; Condition Monitoring; DMU; Intelligent Maintenance; Remote; Stress wave;
