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Canonical Variate Dissimilarity Analysis for Process Incipient Fault Detection

机译：用于过程初始故障检测的规范变量差异分析

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摘要

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.
机译：早期发现工业过程中的早期故障变得越来越重要，因为这些故障会慢慢发展为严重的异常事件，紧急情况，甚至是关键设备故障。当前建立了用于突变故障检测的多元统计过程监测方法。其中，典型变量分析（CVA）被证明对动态过程监控有效。但是，传统的CVA指数对于初期故障可能不够敏感。在这项工作中，提出了CVA的扩展，称为规范变量差异分析（CVDA），用于在变化的工况下非线性动态过程中的过程初期故障检测。为了处理非高斯分布数据，内核密度估计用于计算检测极限。在缓慢发展的乘法和加法测试时，在敏感性方面，已经证明了基于CVA差异的索引优于传统的CVA索引和其他基于差异的索引，即差异分析，递归动态变换分量统计分析和广义典范相关分析。闭环控制和变化运行条件下连续搅拌釜反应器中的故障。

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