A fault detection method based on sparse dynamic canonical correlation analysis

Fault detection based on canonical correlation analysis (CCA) has received increased attention due to its efficiency in exploring the relationship between input and output. However, traditional CCA may generate redundant features in both the input and output projections while maximizing the correlations. In this paper, sparse dynamic canonical correlation analysis (SDCCA) is developed for dealing with the fault detection of dynamic processes. Through posing sparsity in the extraction of features, the interpretability of canonical variates is enhanced attributed to the sparsity of canonical weights. Based on the SDCCA model, the T-2 monitoring metric is established for fault detection. Moreover, the upper control limit (UCL) based on T-2 monitoring metrics is determined by the kernel density estimation (KDE) method to avoid the violation of the Gaussian assumption. The superiority of the proposed SDCCA-based fault detection method is illustrated through a comparative study of the Tennessee Eastman process benchmark.

Automated summary

In this paper, a sparse dynamic canonical correlation analysis (SDCCA) method is proposed for fault detection. By introducing sparsity, the interpretability of canonical variates is enhanced. The superiority of the method is demonstrated through a comparative study of the Tennessee Eastman process benchmark.