Structured Joint Sparse Principal Component Analysis for Fault Detection and Isolation
Structured Joint Sparse Principal Component Analysis for Fault Detection and Isolation
复制标题
用于故障检测和隔离的结构化联合稀疏主成分分析
DOI：
10.1109/tii.2018.2868364
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发表时间：
2019-05
影响因子：
12.3
通讯作者：
Su Hongye
中科院分区：
文献类型：
--
作者：
Liu Yi;Zeng Jiusun;Xie Lei;Luo Shihua;Su Hongye
主成分分析在现代工业系统的过程监控中得到了广泛的应用。主元分析通过将过程数据映射到低维子空间并使用T2和SPE统计量跟踪过程行为来执行故障检测，而在故障隔离中，它严重依赖于基于贡献图或重构的方法。然而，传统的基于贡献图和重构的故障隔离方法存在故障隔离能力不足的问题。为了提高故障隔离性能，提出了一种基于结构化联合稀疏主元分析(SJSPCA)的故障检测与隔离方法。SJSPCA的目标函数包含两个正则项：12，1范数和图拉普拉斯。通过施加12，1范数项，SJSPCA能够实现行稀疏性，引入图的拉普拉斯正则化项可以包含结构化变量的相关性信息。在故障检测中，构造了常规的T2统计量和SPE统计量来检测异常情况。一旦检测到故障，就考虑两阶段故障隔离策略，并计算每个变量的得分指数。L2，1范数的行稀疏性确保了与正常变量相关的得分指数接近于零，而图的拉普拉斯约束有助于隔离相关的故障变量。通过在高炉炼铁过程中观察到的一个过程故障，说明了SJSPCA在故障检测和隔离方面的有效性。
Principal component analysis (PCA) has been widely applied in process monitoring of modern industrial systems. PCA performs fault detection by mapping the process data into a low dimensional subspace and tracking the process behavior using T2 and SPE statistics, whilst in fault isolation, it heavily relies on contribution plot or reconstruction based approaches. However, conventional methods based on contribution plot and reconstruction suffer from insufficient fault isolation capabilities. In order to improve the fault isolation performance, this article proposes a novel fault detection and isolation approach based on the Structured Joint Sparse PCA (SJSPCA). The objective function of SJSPCA involves two regularization terms: l2,1 norm and the graph Laplacian. By imposing the l2,1 norm term, SJSPCA is able to achieve row-wise sparsity, introducing the graph Laplacian regularization term can incorporate structured variable correlation information. In fault detection, conventional T2 and SPE statistics are constructed to detect abnormal situations. Once a fault is detected, a two stage fault isolation strategy is considered and a score index is calculated for each variable. The row-sparsity property of l2,1 norm ensures that the score indices associated to normal variables approach zero and the graph Laplacian constraint helps isolation of correlated faulty variables. The validity of SJSPCA in fault detection and isolation is illustrated by a process fault observed in an industrial blast furnace iron-making process.
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DOI：
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