A Novel Fault Diagnosis Approach Integrated LRKPCA with AdaBoost.M2 for Industrial Process

Abstract

Facing the safety problems in industrial process, how to effectively diagnose process faults has become quite necessary and important. In this paper, a novel fault diagnosis approach integrated local reconstructed kernel principal component analysis(LRKPCA) with AdaBoost.M2 is proposed. Firstly, kernel principal component analysis(KPCA) is adopted to extract the global features through non-linear projection transformation. And local feature extraction based on t-distributed stochastic neighbor embedding(TSNE) is realized by minimizing the similarity of probability distribution of samples in high-dimensional space and low-dimensional space. Secondly, LRKPCA-based feature extraction method is proposed, in which the reconstruction error is calculated based on local features and mapped to the global feature space so that data dimension is reduced through coordinate reconstruction. Thirdly, AdaBoost.M2 is adopted to establish multi-classification model to realize fault diagnosis. Finally, the experimental results based on Tennessee Eastman process(TEP) show that the proposed method has higher diagnosis accuracy.