Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
PLoS One. 2020 Dec 17;15(12):e0243146. doi: 10.1371/journal.pone.0243146. eCollection 2020.
The control charts with the Principal Component Analysis (PCA) approach and its extension are among the data-driven methods for process monitoring and the detection of faults. Industrial processing data involves complexities such as high dimensionality, auto-correlation, and non-stationary which may occur simultaneously. An efficient fault detection technique is an approach that is robust against data training, sensitive to all the feasible faults of the process, and agile to the detection of the faults. To date, approaches such as the recursive PCA (RPCA) model and the moving-window PCA (MWPCA) model have been proposed when data is high-dimensional and non-stationary or dynamic PCA (DPCA) model and its extension have been suggested for autocorrelation data. But, using the techniques listed without considering all aspects of the process data increases fault detection indicators such as false alarm rate (FAR), delay time detection (DTD), and confuses the operator or causes adverse consequences. A new PCA monitoring method is proposed in this study, which can simultaneously reduce the impact of high-dimensionality, non-stationary, and autocorrelation properties. This technique utilizes DPCA property to decrease the effect of autocorrelation and adaptive behavior of MWPCA to control non-stationary characteristics. The proposed approach has been tested on the Tennessee Eastman Process (TEP). The findings suggest that the proposed approach is capable of detecting various forms of faults and comparing attempts to improve the detection of fault indicators with other approaches. The empirical application of the proposed approach has been implemented on a turbine exit temperature (TET). The results demonstrate that the proposed approach has detected a real fault successfully.
基于主成分分析（PCA）方法及其扩展的控制图是用于过程监测和故障检测的数据驱动方法之一。工业处理数据涉及到许多复杂性，例如高维性、自相关性和非平稳性，这些复杂性可能同时出现。一种有效的故障检测技术是一种方法，该方法对数据训练具有鲁棒性，对过程的所有可行故障都敏感，并且能够快速检测故障。迄今为止，当数据具有高维性和非平稳性或动态性时，已经提出了递归 PCA（RPCA）模型和移动窗口 PCA（MWPCA）模型等方法，并且已经提出了自相关数据的动态 PCA（DPCA）模型及其扩展。但是，在不考虑过程数据所有方面的情况下使用这些技术会增加故障检测指标，例如误报率（FAR）、检测延迟时间（DTD），并会使操作人员感到困惑或造成不良后果。本研究提出了一种新的 PCA 监测方法，该方法可以同时降低高维性、非平稳性和自相关性的影响。该技术利用 DPCA 特性来降低自相关性的影响，以及 MWPCA 的自适应行为来控制非平稳特性。该方法已在田纳西伊曼过程（TEP）上进行了测试。结果表明，该方法能够检测到各种形式的故障，并尝试与其他方法相比提高故障指标的检测能力。该方法已在涡轮出口温度（TET）上进行了实证应用。结果表明，该方法成功地检测到了一个实际故障。
