Morphological signal processing and computational intelligence for engineering system prognostics

Abstract A computational intelligence (CI)-based approach is presented for prognostics of machine conditions using morphological signal processing (MSP). The machine vibration signals are processed using MSP to extract a novel entropy-based health index (HI) characterizing the signal shape-size complexity for system prognostics. The progression of HI is predicted using CI techniques, namely, recursive neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR). Both single- and multi-step ahead predictions were evaluated through benchmark datasets of non-linear, non-stationary, and chaotic time series solutions of Mackey-Glass and Lorenz equations. The prognostic effectiveness of the CI techniques was illustrated using a vibration dataset of a helicopter drive-train system gearbox. For each CI predictor, both training datasets gave almost similar prediction performance. In training, the performance of ANFIS was the best, followed by SVR and RNN. In test, the best performance was obtained with SVR for both single- and multi-step ahead predictions. The results are helpful in understanding the relationship between the system conditions, the corresponding indicating feature, the level of degradation, and their progression.