In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation method based on structured sparsity modelling. The developed method relies on the reconstruction based contribution analysis and the process structure information can be incorporated into the reconstruction objective function in the form of structured sparsity regularization terms. The structured sparsity terms allow optimal selection of fault variables over structures like blocks or networks of process variables, hence more accurate fault isolation can be achieved. Four structured sparsity terms corresponding to different kinds of process information are considered, namely, partially known sparse support, block sparsity, clustered sparsity and tree-structured sparsity. The optimization problems involving the structured sparsity terms can be solved using the Alternating Multiplier Method (ADMM) algorithm, which is fast and efficient. In addition, the ADMM algorithm can be easily extended in a parallel/distributed way to handle large-scale systems with a large number of variables. Through a simulation example and an application study to a coal-fired power plant, it is verified that the proposed method can better isolate faulty variables by incorporating process structure information.

中文翻译：

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用于改进的基于故障隔离的多元统计分析的结构化稀疏建模

为了提高多元统计方法的故障诊断能力，本文介绍了一种基于结构化稀疏建模的故障隔离方法。所开发的方法依赖于基于重构的贡献分析，过程结构信息可以以结构化稀疏正则化项的形式纳入重构目标函数。结构化稀疏项允许在过程变量块或网络等结构上最佳选择故障变量，因此可以实现更准确的故障隔离。考虑了与不同类型的过程信息对应的四个结构化稀疏项，即部分已知稀疏支持、块稀疏、聚类稀疏和树结构稀疏。涉及结构化稀疏项的优化问题可以使用交替乘法器 (ADMM) 算法解决，该算法快速高效。此外，ADMM 算法可以通过并行/分布式方式轻松扩展，以处理具有大量变量的大型系统。通过对燃煤电厂的仿真实例和应用研究，验证了所提出的方法通过结合过程结构信息可以更好地隔离故障变量。