Efficient and Fast Joint Sparse Constrained Canonical Correlation Analysis for Fault Detection

The canonical correlation analysis (CCA) has attracted wide attention in fault detection (FD). To improve the detection performance, we propose a new joint sparse constrained CCA (JSCCCA) model that integrates the <inline-formula> <tex-math notation="LaTeX">\ell_{2,0}</tex-m...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 35; číslo 3; s. 1 - 11
Hlavní autoři: Xiu, Xianchao, Pan, Lili, Yang, Ying, Liu, Wanquan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
0}</tex-math> </inline-formula>-norm joint sparse
<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> ell_{2
Algorithms
canonical correlation analysis (CCA)
Computing time
Constraints
Convergence
Correlation
Correlation analysis
Fault detection
fault detection (FD)
Iterative methods
Manifolds
Minimization
Optimization
optimization algorithm
Sparse matrices
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí:The canonical correlation analysis (CCA) has attracted wide attention in fault detection (FD). To improve the detection performance, we propose a new joint sparse constrained CCA (JSCCCA) model that integrates the <inline-formula> <tex-math notation="LaTeX">\ell_{2,0}</tex-math> </inline-formula>-norm joint sparse constraints into classical CCA. The key idea is that JSCCCA can fully exploit the joint sparse structure to determine the number of extracted variables. We then develop an efficient alternating minimization algorithm using the improved iterative hard thresholding and manifold constrained gradient descent method. More importantly, we establish the convergence guarantee with detailed analysis. Finally, we provide extensive numerical studies on the simulated dataset, the benchmark Tennessee Eastman process, and a practical cylinder-piston process. In some cases, the computing time is reduced by 600 times, and the FD rate is increased by 12.62% compared with classical CCA. The results suggest that the proposed approach is efficient and fast.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2022.3201881