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New reduced kernel PCA for fault detection and diagnosis in cement rotary kiln
Chemometrics and Intelligent Laboratory Systems ( IF 3.7 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.chemolab.2020.104091
F. Bencheikh , M.F. Harkat , A. Kouadri , A. Bensmail

Abstract Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in industrial process monitoring. It intends to promptly detect and identify abnormalities and enhance the reliability and safety of the processes. Kernel Principal Component Analysis (KPCA) is a powerful FDD based data-driven method. It has gained much interest due to its ability in monitoring nonlinear systems. However, KPCA suffers from high computing time and large storage space when a large-sized training dataset is used. So, extracting and selecting the more relevant observations could provide a good solution to high computation time and memory requirements costs. In this paper, a new Reduced KPCA (RKPCA) approach is developed to address that issue. It aims to preserve one representative observation for each similar and selected Euclidean distance between training samples. Afterwards, the obtained reduced training dataset is used to build a KPCA model for FDD purposes. The developed RKPCA scheme is tested and evaluated across a numerical example and an actual involuntary process fault and various simulated sensor faults in a cement plant. The obtained results show high monitoring performance with highest robustness to false alarms and maximum fault detection sensitivity compared to conventional PCA, KPCA and other well-established RKPCA techniques. Furthermore, the unified contribution plot method demonstrates superior potentials in identifying faulty variables.

中文翻译:

用于水泥回转窑故障检测和诊断的新型简化内核 PCA

摘要 基于数据驱动技术的故障检测与诊断(FDD)在工业过程监控中起着至关重要的作用。它旨在及时发现和识别异常情况,提高流程的可靠性和安全性。核主成分分析 (KPCA) 是一种强大的基于 FDD 的数据驱动方法。由于其监测非线性系统的能力,它引起了人们的极大兴趣。然而,当使用大型训练数据集时,KPCA 存在计算时间长和存储空间大的问题。因此,提取和选择更相关的观察结果可以为高计算时间和内存要求成本提供一个很好的解决方案。在本文中,开发了一种新的简化 KPCA (RKPCA) 方法来解决该问题。它旨在为训练样本之间的每个相似和选定的欧几里德距离保留一个代表性观察。之后,获得的缩减训练数据集用于构建用于 FDD 目的的 KPCA 模型。开发的 RKPCA 方案通过数值示例和水泥厂中的实际非自愿过程故障和各种模拟传感器故障进行了测试和评估。与传统的 PCA、KPCA 和其他完善的 RKPCA 技术相比,所获得的结果表明具有最高的误报鲁棒性和最大的故障检测灵敏度的高监控性能。此外,统一贡献图方法在识别错误​​变量方面表现出优越的潜力。开发的 RKPCA 方案通过数值示例和水泥厂中的实际非自愿过程故障和各种模拟传感器故障进行了测试和评估。与传统的 PCA、KPCA 和其他完善的 RKPCA 技术相比,所获得的结果表明具有最高的误报鲁棒性和最大的故障检测灵敏度的高监控性能。此外,统一贡献图方法在识别错误​​变量方面表现出优越的潜力。开发的 RKPCA 方案通过数值示例和水泥厂中的实际非自愿过程故障和各种模拟传感器故障进行了测试和评估。与传统的 PCA、KPCA 和其他完善的 RKPCA 技术相比,获得的结果表明具有最高的虚假警报鲁棒性和最大的故障检测灵敏度的高监控性能。此外,统一贡献图方法在识别错误​​变量方面表现出优越的潜力。KPCA 和其他完善的 RKPCA 技术。此外,统一贡献图方法在识别错误​​变量方面表现出优越的潜力。KPCA 和其他完善的 RKPCA 技术。此外,统一贡献图方法在识别错误​​变量方面表现出优越的潜力。
更新日期:2020-09-01
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