An Improved Non-negative Matrix Factorization Method for Dynamic Industrial Fault Diagnosis

Abstract

Most classical fault diagnosis methods such as principal component analysis (PCA), are extracting comprehensive information to represent data features in fault diagnosis. In comparison, Non-Negative Matrix Factorization (NMF) is a method for dimension reduction and feature extraction, and because its characteristic matrix has sparsity, this method is superior in the ability of extracting the local feature and suppressing noises; however, the NMF method is not applicable for dynamic industrial processes. In this paper, we introduce the past information of industrial processes for fault diagnosis, proposing Canonical Variate Analysis - Non-Negative Matrix Factorization (CVA-NMF) methods to improve the dynamic performance of NMF. The experimental results via TE process indicate that the proposed approach could handle a dynamic production process such as Fault 2 and Fault 5 and retain the superior performance of NMF.