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首页> 外文期刊>IEEE transactions on industrial informatics >Principal Component Analysis-Based Ensemble Detector for Incipient Faults in Dynamic Processes
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Principal Component Analysis-Based Ensemble Detector for Incipient Faults in Dynamic Processes

机译：基于主成分分析的集合探测器，用于动态过程中的初始故障

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摘要

The significant advancement in data-driven fault detection has been made, but incipient faults such as faults 3, 9, and 15 in Tennessee Eastern process (TEP) still remain difficult for the current approaches. In this article, a powerful principal component analysis (PCA)-based ensemble detector (PCAED) is developed for detecting incipient faults. To begin with, multiple PCA-based detectors are designed based on bootstrap sampling in the training dataset. It can generate two matrices according to principal component and residual subspaces. Then, two sensitive detection indices are developed using maximal singular values of one-step sliding windows along the rows of the above two matrices. With this kind of detection index, PCAED can effectively detect incipient faults, specially faults 3, 9, and 15 in TEP, which cannot be detected by an individual PCA detector. Simulations of TEP and a practical coal pulverizing system fully verify the effectiveness of PCAED. Faults can be successfully detected at the incipient stage, which is very helpful to avoid possible economic or human loss.
机译：已经进行了数据驱动故障检测的显着进步，但诸如田纳西州东部进程（Tep）中的故障3,9和15等初期的故障仍然难以实现当前方法。在本文中，开发了一个强大的主成分分析（PCA）基数探​​测器（PCAED），用于检测初始故障。首先，基于训练数据集中的引导抽样设计了多个基于PCA的检测器。它可以根据主成分和残差子空间生成两个矩阵。然后，使用沿着上述两个矩阵的行的一步滑动窗口的最大奇异值开发了两个敏感的检测指标。利用这种检测指标，PCAED可以有效地检测TEP中的初始故障，特殊的故障3,9和15，其无法通过单独的PCA检测器检测。 TEP的模拟和实际煤粉碎系统完全验证了PCAED的有效性。在初期阶段可以成功检测故障，这非常有助于避免可能的经济或人为损失。

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