Comparison of Metrics for Peaks Enhancement in Variable Speed Conditions

International audienc

Diagnostics of Bearings in Rolling Stocks: Results of Long Lasting Tests for a Regional Train Locomotive

The application of Prognostics and Health Monitoring (PHM) concepts in rail vehicles is a rapidly growing field of research, and extensive efforts are being spent with the aim of improving the reliability and availability of railway systems and of substantially reducing maintenance costs by switching from time-based to event-driven maintenance policies. This paper is aimed at proving that effective PHM can be applied also on already existing rolling stocks. To do this, and focusing on bearings of the traction system, a prototype monitoring system, described in the paper, was developed and installed on a E464 locomotive. This regional train locomotive class, despite being recent since they were built between 1999 and 2015, is not equipped by any monitoring system for the bearings. The bearings have been monitored for about three years of service, during which the locomotive has undergone to a major maintenance and all the bearings of the traction system has been replaced. This allowed to examine them and to assess if damage indexes corresponded to actual faults. A huge amount of vibration data has been collected and it was possible to assess that the overall system cannot be considered as in stationary operation, neither when the train speed is constant nor when the same track is travelled. Many different techniques have been developed and tested with the aim of detecting bearing damages, considering that fault signals are heavily masked noise. The results are here described and it is shown that the introduced fault indexes are able to monitor the damage evolution in non-stationary conditions

Condition based maintenance operation of wind turbines

With application of advanced sensing technology, the condition based maintenance and operation has been made possible to many industrial systems. In a wind turbine, there are a few hundreds of sensing signals used to monitor the component performance and operational condition. The condition information is utilized in operational control of wind turbines and the wind farm in order to reduce the down time and Cost of Energy (CoE). In this chapter, a framework of condition based maintenance and operation of wind turbines is presented. This framework starts with data collection of sensing signals through SCADA and includes data processing and modeling, failure pattern recognition, remaining useful life/health condition prediction, load prediction (prediction of wind trend), integrated decision making for maintenance and operation of wind turbines and the wind farm, and maintenance planning. The research challenges involved in each step of the framework are discussed. The framework presented in this chapter serves as a guideline which is also useful to other systems