IEEE Trans Cybern. 2022 Sep;52(9):9454-9466. doi: 10.1109/TCYB.2021.3060766. Epub 2022 Aug 18.
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.
最近，规范相关分析（CCA）已被探索用于解决工业系统的故障检测（FD）问题。然而，大多数基于 CCA 的 FD 方法都假设测量信号的高斯性和变量之间的线性关系。在某些实际情况下，这些假设可能不恰当，因此直接应用这些基于 CCA 的 FD 策略可能不是最优的。在神经网络的帮助下，这项工作提出了一种新的非线性对应方法，称为单边 CCA（SsCCA），以提高 FD 性能。这项工作的贡献有四点：1）首先重新制定了非线性 CCA 的目标函数，在此基础上提出了广义解；2）为了实际应用，开发了 SsCCA 的特定解；3）为非线性系统设计了基于 SsCCA 的 FD 算法，通过理论分析说明了其最优 FD 能力；4）基于两个检验统计量之间的 FD 结果差异，可以直接进行故障诊断。通过对非线性三容系统的研究，验证了所提出的 SsCCA 方法的有效性。
