A novel fault diagnosis system using pattern classification on kernel FDA subspace

► This paper proposes a novel fault diagnosis system to improve the performance of fault diagnosis. ► Kernel Fisher discriminant analysis (KFDA) is used in the first step for feature extraction, then Gaussian mixture model (GMM) and k-nearest neighbor (kNN) are applied for fault detection and isolation on the KFDA subspace. ► Since the performance of fault diagnosis system would be degraded in the fault detection stage, fault detection and identification are presented in a holistic manner without an intermediate step in the novel system.