Discriminative non-negative matrix factorization (DNMF) and its application to the fault diagnosis of diesel engine. (October 2017)

• Record Type: Journal Article
• Authors: Yang, Yong-sheng; Ming, An-bo; Zhang, You-yun; Zhu, Yong-sheng
• Is Part Of: Mechanical systems and signal processing. Volume 95(2017)
• Page Start: 158
• Page End: 171
• Publication Date: 2017-10
• Subjects: Discriminative non-negative matrix factorization (DNMF) -- K-nearest neighborhood method -- Time-frequency distribution -- Diesel engine -- Fault diagnosis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.03.026
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Class information was involved in the non-negative matrix factorization (NMF). A novel fault diagnosis method is proposed by the combination of DNMF and KNN. The proposed method was applied on the fault diagnosis of diesel engine. Several comparisons have been used to validate the efficacy of the proposed method. Abstract: Diesel engines, widely used in engineering, are very important for the running of equipments and their fault diagnosis have attracted much attention. In the past several decades, the image based fault diagnosis methods have provided efficient ways for the diesel engine fault diagnosis. By introducing the class information into the traditional non-negative matrix factorization (NMF), an improved NMF algorithm named as discriminative NMF (DNMF) was developed and a novel imaged based fault diagnosis method was proposed by the combination of the DNMF and the KNN classifier. Experiments performed on the fault diagnosis of diesel engine were used to validate the efficacy of the proposed method. It is shown that the fault conditions of diesel engine can be efficiently classified by the proposed method using the coefficient matrix obtained by DNMF. Compared with the original NMF (ONMF) and principle component analysis (PCA), the DNMF can represent the class information more efficiently because the class characters of basis matrices obtained by the DNMF are more visible than those in the basis matrices obtained by the ONMF and PCA.