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An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference
Journal of Intelligent Manufacturing ( IF 8.3 ) Pub Date : 2021-03-12 , DOI: 10.1007/s10845-021-01752-9
Yanning Sun , Wei Qin , Zilong Zhuang , Hongwei Xu

In recent years, fault detection and diagnosis for industrial processes have been rapidly developed to minimize costs and maximize efficiency by taking advantages of cheap sensors and microprocessors, data analysis and artificial intelligence methods. However, due to the nonlinear and dynamic characteristics of industrial process data, the accuracy and efficiency of fault detection and diagnosis methods have always been an urgent problem in industry and academia. Therefore, this study proposes an adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window kernel principle component analysis (KPCA) and information geometric causal inference (IGCI). The proposed scheme has three main contributions. Firstly, a research scheme combining moving window KPCA with adaptive threshold is presented to handle the nonlinear and dynamic characteristics of complex industrial processes. Then, the multiobjective evolutionary algorithm is employed to select the optimal hyperparameters for fault detection, which not only avoids the blindness of hyperparameters selection, but also maximize model accuracy. Finally, the IGCI-based fault root-cause analysis method can help field operators to take corrective measures in time to resume the normal process. The proposed scheme is tested by the Tennessee Eastman platform. Its results show that this scheme has a good performance in reducing the faulty false alarms and missed detection rates and locating fault root-cause.



中文翻译:

利用移动窗口KPCA和信息几何因果推断的复杂工业过程自适应故障检测和根本原因分析方案

近年来,通过利用廉价的传感器和微处理器,数据分析和人工智能方法的优势,工业过程的故障检测和诊断已得到快速发展,以最大程度地降低成本和提高效率。然而,由于工业过程数据的非线性和动态特性,故障检测和诊断方法的准确性和效率一直是工业界和学术界的迫切问题。因此,本研究提出了一种利用移动窗口核主成分分析(KPCA)和信息几何因果推断(IGCI)的复杂工业过程的自适应故障检测和根本原因分析方案。拟议的计划有三个主要贡献。首先,提出了一种结合移动窗口KPCA和自适应阈值的研究方案来处理复杂工业过程的非线性和动态特性。然后,采用多目标进化算法选择最优的超参数进行故障检测,不仅避免了超参数选择的盲目性,而且使模型的准确性达到了最大化。最后,基于IGCI的故障根本原因分析方法可以帮助现场操作人员及时采取纠正措施,以恢复正常过程。拟议的方案已由田纳西州伊士曼平台进行了测试。结果表明,该方案在减少误报,漏检率和定位故障根源方面具有良好的性能。采用多目标进化算法选择最优的超参数进行故障检测,不仅避免了超参数选择的盲目性,而且使模型的准确性达到了最大化。最后,基于IGCI的故障根本原因分析方法可以帮助现场操作人员及时采取纠正措施,以恢复正常过程。拟议的方案已由田纳西州伊士曼平台进行了测试。结果表明,该方案在减少误报,漏检率和定位故障根源方面具有良好的性能。采用多目标进化算法选择最优的超参数进行故障检测,不仅避免了超参数选择的盲目性,而且使模型的准确性达到了最大化。最后,基于IGCI的故障根本原因分析方法可以帮助现场操作人员及时采取纠正措施,以恢复正常过程。拟议的方案已由田纳西州伊士曼平台进行了测试。结果表明,该方案在减少误报,漏检率和定位故障根源方面具有良好的性能。基于IGCI的故障根本原因分析方法可以帮助现场操作人员及时采取纠正措施,以恢复正常过程。拟议的方案已由田纳西州伊士曼平台进行了测试。结果表明,该方案在减少误报,漏检率和定位故障根源方面具有良好的性能。基于IGCI的故障根本原因分析方法可以帮助现场操作人员及时采取纠正措施,以恢复正常过程。拟议的方案已由田纳西州伊士曼平台进行了测试。结果表明,该方案在减少误报,漏检率和定位故障根源方面具有良好的性能。

更新日期:2021-03-12
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