论文标题
改进基于多元统计分析的故障隔离的结构化稀疏模型
Structured Sparsity Modeling for Improved Multivariate Statistical Analysis based Fault Isolation
论文作者
论文摘要
为了提高多元统计方法的故障诊断能力，本文介绍了基于结构化稀疏性建模的故障隔离框架。开发的方法依赖于基于重建的贡献分析，并且可以将过程结构信息以结构化的稀疏正规化项的形式纳入重建目标函数。结构化的稀疏项允许选择故障变量，而不是过程变量的结构或网络，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相对应的四个结构化的稀疏项，即，部分已知的稀疏支撑，块稀疏性，簇状的稀疏性和树结构的稀疏性。可以使用乘数（ADMM）算法的交替方向方法来解决涉及结构化稀疏项的优化问题，该算法快速有效。通过模拟示例和对燃煤发电厂的申请研究，可以证明该方法可以通过结合过程结构信息来更好地隔离错误变量。
In order to improve the fault diagnosis capability of multivariate statistical methods, this article introduces a fault isolation framework based on structured sparsity modeling. 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 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 Direction Method of Multipliers (ADMM) algorithm, which is fast and efficient. 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.
