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.

Fisher 判别分析 (FDA) 已被公认为故障分类和诊断的典型方法。为了使用时间序列数据增强模型性能，习惯上将滞后测量包含到模型中。这不仅极大地增加了模型的复杂性，而且降低了故障诊断策略的可解释性。为了解决这个问题，我们提出了一种新的组稀疏性执行 FDA 模型，该模型利用重新加权的组套索惩罚来修剪变量级别的不相关变量，从而提高判别方向的可解释性。针对非光滑非凸优化问题，提出了一种基于乘法器交替方向法的定制算法。此外，为了识别根本原因变量并揭示故障随时间的演变，开发了一种基于重加权群 Lasso 的稀疏故障估计方法。这最终允许开发一个整体的在线方案，使用错误的变量信息产生信息性的诊断结论。实验结果表明，所提出的模型显着提高了正常数据和故障数据的判别能力，并且比使用动态过程数据的传统方法产生了更多可解释的判别信息。