Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection
Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection
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DOI：
10.1109/tii.2018.2810822
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发表时间：
2018-02
影响因子：
12.3
通讯作者：
K. Pilario;Yi Cao
中科院分区：
文献类型：
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作者：
K. Pilario;Yi Cao
及早发现工业过程中的早期故障变得越来越重要，因为这些故障可能会慢慢发展为严重的异常事件、紧急情况，甚至关键设备的故障。目前已建立了用于突变故障检测的多变量统计过程监测方法。其中，典型变量分析(CVA)被证明是动态过程监测的有效方法。然而，传统的CVA指标对于早期故障可能不够敏感。在这项工作中，提出了一种扩展的CVA，称为典型变量相异度分析(CVDA)，用于检测变工况下的非线性动态过程的早期故障。为了处理非高斯分布的数据，使用核密度估计来计算检测限。基于CVA相异度的指标被证明优于传统的CVA指标和其他基于相异度的指标，即相异度分析、递归动态变换分量统计分析和广义典型相关分析。在闭环系统控制和操作条件变化的情况下，对缓慢发展的乘性和加性故障进行测试时，基于CVA相异度的指标的灵敏度优于其他基于相异度分析的指标。
Early detection of incipient faults in industrial processes is increasingly becoming important, as these faults can slowly develop into serious abnormal events, an emergency situation, or even failure of critical equipment. Multivariate statistical process monitoring methods are currently established for abrupt fault detection. Among these, the canonical variate analysis (CVA) was proven to be effective for dynamic process monitoring. However, the traditional CVA indices may not be sensitive enough for incipient faults. In this work, an extension of CVA, called the canonical variate dissimilarity analysis (CVDA), is proposed for process incipient fault detection in nonlinear dynamic processes under varying operating conditions. To handle the non-Gaussian distributed data, the kernel density estimation was used for computing detection limits. A CVA dissimilarity based index has been demonstrated to outperform traditional CVA indices and other dissimilarity-based indices, namely the dissimilarity analysis, recursive dynamic transformed component statistical analysis, and generalized canonical correlation analysis, in terms of sensitivity when tested on slowly developing multiplicative and additive faults in a continuous stirred-tank reactor under closed-loop control and varying operating conditions.