期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 9, 页码 9454-9466出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3060766
关键词
Correlation; Neural networks; Nonlinear systems; Linear programming; Fault diagnosis; Task analysis; Principal component analysis; Canonical correlation analysis (CCA); fault detection (FD); neural networks; nonlinear systems; single-side CCA (SsCCA)
类别
资金
- Natural Sciences and Engineering Research Council of Canada
This study introduces a new nonlinear fault detection method called SsCCA with the help of neural networks to enhance FD performance, by reformulating the objective function and designing a specific solution. Experimental results demonstrate that this method can effectively improve fault detection capability in nonlinear systems.
Recently, canonical correlation analysis (CCA) has been explored to address the fault detection (FD) problem for industrial systems. However, most of the CCA-based FD methods assume both Gaussianity of measurement signals and linear relationships among variables. These assumptions may be improper in some practical scenarios so that direct applications of these CCA-based FD strategies are arguably not optimal. With the aid of neural networks, this work proposes a new nonlinear counterpart called a single-side CCA (SsCCA) to enhance FD performance. The contributions of this work are four-fold: 1) an objective function for the nonlinear CCA is first reformulated, based on which a generalized solution is presented; 2) for the practical implementation, a particular solution of SsCCA is developed; 3) an SsCCA-based FD algorithm is designed for nonlinear systems, whose optimal FD ability is illustrated via theoretical analysis; and 4) based on the difference in FD results between two test statistics, fault diagnosis can be directly achieved. The studies on a nonlinear three-tank system are carried out to verify the effectiveness of the proposed SsCCA method.
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