基于自适应滑窗递归稀疏主成分分析的工业过程故障监测

本文提出一种自适应滑窗递归稀疏主成分分析方法，用于时变工业过程的在线故障监测.首先，通过滑窗提取正常过程数据空间的特征信息，并对当前窗口数据块矩阵进行稀疏主成分分析，构建稀疏主成分分析故障监测模型；然后，根据相邻窗口的相似度实时调整遗忘因子以自适应更新滑窗大小，使得所建立的稀疏主成分故障监测模型可以有效追踪复杂的时变过程；最后，通过递归更新滑窗稀疏载荷矩阵来动态更新故障监测模型.非线性数值仿真系统与田纳西-伊斯曼过程的故障监测结果表明，所提方法可以有效提高故障检测的准确率，适应于长流程时变工业过程在线故障监测.

Industrial Process Fault Monitoring Based on Adaptive Sliding Window-Recursive Sparse Principal Component Analysis

This paper presents an adaptive sliding window recursive sparse principal component analysis method for the on-line fault monitoring of time-varying industrial processes.Firstly,feature information of normal process data space is extracted by the sliding window,and the sparse principal component analysis is applied to the current window block matrix to construct the sparse principal component analysis-based process fault monitoring model.Then,the forgetting factor is adjusted in real time according to the similarities of adjacent windows to update the sliding window size adaptively,so that the sparse principal component fault monitoring model can effectively track the time-varying process.Finally,the sparse load matrix of the sliding window is renewed recursively to update the fault monitoring model dynamically.Fault monitoring results of the nonlinear numerical simulation system and the Tennessee-Eastman process show that the proposed method can effectively improve the fault detection accuracy and adapt to the on-line fault monitoring of long process industries with time-varying processes.