We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A novel fault detection scheme for a nonlinear dynamic process based on generalized non‐negative matrix projection‐maximum mean discrepancy: Application on the DAMADICS benchmark process.
- Authors
Cheng, Zhang; Cheng‐Long, Deng; Yun‐Fei, Wang; Xiao‐Ping, Guo; Yuan, Li
- Abstract
In order to address the issue of minor fault detection in nonlinear dynamic processes, this paper proposes a fault detection method based on generalized non‐negative matrix projection‐maximum mean discrepancy (GNMP‐MMD). Firstly, the GNMP is employed to acquire the residual scores of the samples. Subsequently, a sliding window approach is integrated with MMD for real‐time monitoring of sample status within the residual subspace. In this study, GNMP is utilized to mitigate the impact of non‐Gaussianity in data distribution, while MMD serves to alleviate autocorrelation among samples. A numerical case and experimental data collected from the DAMADICS process are utilized to simulate and validate the proposed method. Compared to traditional principal component analysis (PCA), dynamic principal component analysis (DPCA), dynamic kernel principal component analysis (DKPCA), non‐negative matrix factorization (NMF), GNMP, and MMD, the experiment results clearly illustrate the feasibility of the proposed method.
- Subjects
NONNEGATIVE matrices; PRINCIPAL components analysis; MATRIX decomposition; DATA distribution
- Publication
Canadian Journal of Chemical Engineering, 2024, Vol 102, Issue 4, p1483
- ISSN
0008-4034
- Publication type
Article
- DOI
10.1002/cjce.25129