Due to the closed-loop feedback control system, the data collected in the industry process is always temporal serial correlated. Moreover, different types of sensor data (such as temperature, pressure, liquid level, flow and so on) would show different temporal serial correlations. Meanwhile, the current process monitoring method considers all types of sensor data equally, ignoring the difference in temporal serial correlations caused by various sensor types. In response to this, serial correlated–uncorrelated concurrent space method is proposed in this work, where process variable original space is divided into serial correlated space and serial uncorrelated space via the degree of temporal serial correlations. In addition, the proposed method not only considers the local monitoring within space via principal component analysis and slow feature analysis respectively, but also monitors the information between-space based on the moving window and the mutual information. Finally, on the basis of the monitoring statistic within space and between-space, the comprehensive monitoring indicator is constructed via the local outlier factor method. The advantage and superiority of the proposed method is illustrated via testing on Tennessee Eastman process and Continuous Stirred Tank Reactor process.

由于闭环反馈控制系统，工业过程中收集的数据始终是时间序列相关的。此外，不同类型的传感器数据（如温度、压力、液位、流量等）会表现出不同的时间序列相关性。同时，当前的过程监控方法平等地考虑所有类型的传感器数据，忽略了由各种传感器类型引起的时间序列相关性的差异。针对这一点，本文提出了序列相关-不相关并发空间方法，其中过程变量原始空间通过时间序列相关程度分为序列相关空间和序列不相关空间。此外，该方法不仅通过主成分分析和慢特征分析分别考虑空间内的局部监测，而且还基于移动窗口和互信息监测空间间的信息。最后，在空间内和空间间监测统计量的基础上，通过局部离群因子法构建综合监测指标。通过对田纳西伊士曼工艺和连续搅拌釜反应器工艺的测试，说明了所提出方法的优点和优越性。综合监测指标采用局部离群因子法构建。通过对田纳西伊士曼工艺和连续搅拌釜反应器工艺的测试，说明了所提出方法的优点和优越性。综合监测指标采用局部离群因子法构建。通过对田纳西伊士曼工艺和连续搅拌釜反应器工艺的测试，说明了所提出方法的优点和优越性。