With the rapid improvement of equipment manufacturing technology and the ever increasing cost of fuel, engine health management has become one of the most important parts of aeroengine, industrial and marine gas turbine. As an effective technology for improving the engine availability and reducing the maintenance costs, anomaly detection has attracted great attention. In the past decades, different methods including gas path analysis, on-line monitoring or off-line analysis of vibration signal, oil and electrostatic monitoring have been developed. However, considering the complexity of structure and the variability of working environments for engine, many important problems such as the accurate modeling of gas turbine with different environment, the selection of sensors, the optimization of various data-driven approach and the fusion strategy of multi-source information still need to be solved urgently. Besides, although a large number of investigations in this area are reported every year in various journals and conference proceedings, most of them are about aeroengine or industrial gas turbine and limited literature is published about marine gas turbine. Based on this background, this paper attempts to summarize the recent developments in health management of gas turbines. For the increasing requirement of predict-and-prevent maintenance, the typical anomaly detection technologies are analyzed in detail. In addition, according to the application characteristics of marine gas turbine, this paper introduces a brief prospect on the possible challenges of anomaly detection, which may provide beneficial references for the implementing and development of marine gas turbine health management.
On-line anomaly detection and resilience in classifier ensembles
