A novel industrial process fault monitoring method based on kernel robust non-negative matrix factorization

被引:8
作者
Wang, Yinsong [1 ]
Sun, Tianshu [1 ]
Ding, Mengting [1 ]
Liu, Yanyan [1 ]
机构
[1] North China Elect Power Univ, Dept Automat, Baoding 071003, Peoples R China
基金
中国国家自然科学基金;
关键词
kernel robust non-negative matrix factorization; nonlinearity; robustness; fault monitoring; industrial process; COMPONENT ANALYSIS; DECOMPOSITION; DIAGNOSIS;
D O I
10.1088/1361-6501/ac0de2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Industrial processes are characterized by large amounts of nonlinear and noisy data, which pose a critical challenge to the accuracy and rapidity of fault detection. In this paper, an industrial process fault monitoring method based on kernel robust non-negative matrix factorization is proposed. This method uses the kernel technique to map the nonlinear data to high-dimensional linear space, where the local features of the sample will be extracted by the non-negative matrix factorization (NMF) method. However, noise signals will inevitably be mixed. Therefore, a sparse error matrix is introduced to isolate fault and noise information. Finally, a new monitoring statistics and a fault detection framework are constructed. On the TE platform, the algorithm proposed in this paper is compared with kernel principal component analysis and kernel NMF methods in nonlinear experiments and robustness experiments through two performance indicators: fault detection rate and fault delay. The results prove the effectiveness of the algorithm in this paper.
引用
收藏
页数:11
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