Group-sparsity-enforcing fault discrimination and estimation with dynamic process data. (September 2021) - Shang, Chao; Zhao, Liang; Huang, Xiaolin; Ye, Hao; Huang, Dexian
Abstract: Fisher discriminative analysis (FDA) has been recognized a prototypical approach to fault classification and diagnosis. To enhance model performance with time-series data used, it is customary to encompass lag measurements into the model. This not only increases model complexity prohibitively but also reduces the interpretability of fault diagnosis strategies. To address this issue, we propose a novel group-sparsity-enforcing FDA model, which utilizes reweighted group Lasso penalty to prune out irrelevant variables at the variable level so as to improve interpretability of discriminant directions. A tailored algorithm based on alternating direction method of multipliers is developed to solve the non-smooth and non-convex optimization problem. In addition, to identify root cause variables and unveil the fault evolution over time, a sparse fault estimation approach based on reweighted group Lasso is developed. This eventually allows to develop a holistic online scheme yielding informative diagnostic verdicts with faulty variable information used. Experimental results demonstrate that, the proposed model significantly improves the discriminant capability between normal and faulty data, and yields more interpretable discriminative information than conventional methods using dynamic process data. Highlights: A new sparse dynamic FDA model is developed based on reweighted group Lasso. Irrelevant variables can be automatically removed from discrimination model. A new sparse fault estimation method is proposed to tackle time series data. A holistic online fault detection and diagnosis scheme is established.