Fault detection and diagnosis using empirical mode decomposition based principal component analysis
Fault detection and diagnosis using empirical mode decomposition based principal component analysis
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DOI：
10.1016/j.compchemeng.2018.03.022
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Yuncheng Du;D. Du
中科院分区：
文献类型：
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作者：
Yuncheng Du;D. Du
本文提出了一种新的算法来识别和诊断Tennessee Eastman（TE）过程的随机故障。该算法结合了嵌入式经验模态分解（EEMD）与主成分分析（PCA）和累积和（Cumulative Sum，Cumulative Sum）来诊断一组以前报道的技术不能正确检测和/或诊断的故障。该算法包括三个步骤：量测预滤波、故障检测和故障诊断。首先，测量变量分解到不同的尺度使用EEMD的PCA，从故障特征可以提取故障检测和诊断（FDD）。基于T2和QPCA的特征指纹进一步应用于改进故障检测，其中从历史数据开发一组PCA模型来表征与每个故障相关的异常指纹，以进行准确的故障诊断。本文提出的算法可以成功地识别和诊断单个和同时发生的随机故障。
This paper presents a new algorithm to identify and diagnose stochastic faults in Tennessee Eastman (TE) process. The algorithm combines Ensemble Empirical Mode Decomposition (EEMD) with Principal Component Analysis (PCA) and Cumulative Sum (CUSUM) to diagnose a group of faults that could not be properly detected and/or diagnosed with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, fault detection, and fault diagnosis. Measured variables are first decomposed into different scales using the EEMD-based PCA, from which fault signatures can be extracted for fault detection and diagnosis (FDD). TheT2andQstatistics-based CUSUMs are further applied to improve fault detection, where a set of PCA models are developed from historical data to characterize anomalous fingerprints that are correlated with each fault for accurate fault diagnosis. The algorithm developed in this paper can successfully identify and diagnose both individual and simultaneous occurrences of stochastic faults.