Abstract
statistical process monitoring has been a continuing research hotspot in recent years, especially in fault diagnosis domain. Principal component analysis (PCA), as a prominent approach, has been extensive researched by scholars recently. In this paper, an improved PCA method is presented by combining the existing PCA with machine learning based autoencoder and dynamic inner-model, which could improve the fault diagnosis performance apparently. By applying our proposed fault diagnosis strategy into a wind turbine process, the experimental results revealed that, compared with traditional PCA, our proposed approach could achieve better fault detection and diagnosis performance in both detection efficiency and accuracy.
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