A Novel Fault Diagnosis Method for Marine Blower with Vibration Signals

Abstract The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in engine room is numerous affects each other, which makes it difficult to extract fault features of time domain. This paper proposes a diagnosis method based combination Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) correlation coefficient method. Firstly, series Intrinsic Function (IMF) components were obtained after signal was decomposed by EEMD. Secondly, effective IMF selected AR models established power spectrum analysed. It verified that blower failure can be accurately diagnosed. intelligent proposed EEMD energy Back Propagation Neural Network (BPNN), with get components, calculated, normalised as feature vector. Finally, vector sent BPNN for training state recognition. results indicated EEMD-BPNN suitable higly accurate blowers.