Fault Monitoring Based on Adaptive Partition Non-Negative Matrix Factorization for Non-Gaussian Processes

Abstract

In this paper, a new fault monitoring method based on adaptive partitioning non-negative matrix factorization (APNMF) is presented for non-Gaussian processes. Non-negative matrix factorization (NMF) is a new dimension reduction technique, which can effectively deal with Gaussian and non-Gaussian data. However, the NMF model of traditional fault monitoring method is time-invariant and cannot provide fault warning for the slowly changing industrial process. Therefore, this paper proposes an adaptive partition NMF algorithm with non-fixed sub-block NMF models. First, the process variables under different operating conditions of the system are divided into several sub-variable spaces adaptively by the complete linkage algorithm. Then, the global variables space and each sub-variable space are modeled by the NMF method. Finally, the kernel density estimation (KDE) method is adapted to calculate the control limits of the defined statistical metrics. The proposed method makes full use of intra-block local information and inter-block global information, which improves diagnostic performance. The experimental results of a numerical process and the Tennessee Eastman (TE) benchmark process show that the proposed method improves the accuracy of fault monitoring compared with the existing algorithms.