Bearing damage detection techniques and their enhancement: comparison over real data

The Dynamics & Identification Research Group at POLITO has been involved in the challenge of bearing damage identification for aero-engines for more than ten years. The role of gearboxes in future aircraft scenario is well focused by the Horizon 2020 - Green Engine program, where some important issues are addressed. One is surely the future of fan propeller engines, where the entire engine powers is flowing through the gearbox as for the case of helicopters. This is a real challenge for early diagnostic techniques both for gears and bearings. In this frame, the DIRG has developed and tested a huge number of algorithms seeking for the ―best performances‖ on real application; this statement must be interpreted as a trade-off among many factors such as: the limited number of sensors, the missing direct torque measure, the short time allowed for real time analysis, the decisional path compromise, the number of false and missed alarms, and so on. Some of these methods are quite ancient (RMS, Kurtosis, Skewness, Crest, and similar evolutions such as FM0 - NA4 - NA4* - FM4 -M6A - NB4) but still can be used as a reference to check how more recent techniques can improve the results and the sensibility to early damages, as well as the localisation (in spite of longer elaboration time). Among the new methods, EMD / EEMD [1], SK [3], Stochastic Resonance [2] and Principal Component Analysis (PCA) [4] are compared in the paper in terms of detection capability but also location identification, minimum number of sensors, false alarms and so on. In practice, however, it should be borne in mind that most of these methods are usually employed in sequence or parallel, so that a meaningful comparison cannot be done and, in this case, it would generate a huge number of combination sets. The elaboration time is also considered as one of the parameters toward its adoptability along the paper