Fault detection and recognition by hybrid nonnegative matrix factorizations

Fault detection and recognition are to recognize which type of operating mode the current operating mode belongs to, among the normal and faulty operating modes of an industrial process. This paper develops a method for fault detection and recognition using hybrid nonnegative matrix factorizations (HNMF) where the term 'hybrid' refers to the fact that these models utilize nonnegative matrix factorization objective functions built upon ideas from graph theory and information theory. Although HNMF absorb a variety of advanced theories and are significantly different from the existing nonnegative matrix factorizations (NMF), they are still convergent in theory. To achieve fault detection and recognition by HNMF, this paper designs a feasible technical roadmap for performing fault detection and recognition using HNMF. Due to the incorporation of NMF, graph theory, and information theory, HNMF show advantages over the existing NMF in terms of fault detection and recognition. More importantly, the proposed fault detection and recognition approach has advantages over the NMFs-based approaches, which is demonstrated through a case study on a penicillin fermentation process.