Machinery fault diagnosis using supervised manifold learning

Abstract

Fault diagnosis is essentially a kind of pattern recognition. How to implement feature extraction and improve recognition performance is a crucial task. In this paper, a new supervised manifold learning algorithm (S-LapEig) for feature extraction is proposed first. Via combining preserving the consistency of local neighbor information and class labels information, S-LapEig can not only gain a perfect approximation of low-dimensional intrinsic geometric structure within the high-dimensional observation data, but also enhance local within-class relations. Based on S-LapEig, a novel fault diagnosis approach is proposed. The approach extracts the intrinsic manifold features from high-dimensional fault data by directly learning the data, and translates complex mode space into a low-dimensional feature space, in which pattern classification and fault diagnosis are carried out easily. Comparing with other feature extraction methods such as PCA, LDA and Laplacian eigenmaps, the proposed method obviously improves the classification performance of fault pattern recognition. The experiments on benchmark data and engineering instance demonstrate the feasibility and effectiveness of the new approach.