Aero-engine fault diagnosis based on multi-scale Independent Component Analysis

Independent signal is stricter than the non-correlated signal in math. Independent component analysis (ICA) can extract independent signals, so it is better than principal component analysis (PCA) when they are used to diagnose faults. However ICA isn´t suited for no-obvious faults which are caused by inputs´ small changes. In order to solve this problem, multi-scale ICA (MSICA) is investigated in this paper, which is applied to aero-engine fault diagnosis. MSICA is used to extract independent components are used to train support vector machine (SVM) for classification. Experiments demonstrate the benefits of this representation.