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首页> 外文期刊>IEEE transactions on industrial informatics >Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis
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Data-Driven Incipient Fault Detection via Canonical Variate Dissimilarity and Mixed Kernel Principal Component Analysis

机译：数据驱动的初期故障检测通过规范变化异化和混合内核主成分分析

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摘要

Incipient fault detection plays a crucial role in preventing the occurrence of serious faults or failures in industrial processes. In most industrial processes, linear, and nonlinear relationships coexist. To improve fault detection performance, both linear and nonlinear features should be considered simultaneously. In this article, a novel hybrid linear-nonlinear statistical modeling approach for data-driven incipient fault detection is proposed by closely integrating recently developed canonical variate dissimilarity analysis and mixed kernel principal component analysis (MKPCA) using a serial model structure. Specifically, canonical variate analysis (CVA) is first applied to estimate the canonical variables (CVs) from the collected process data. Linear features are extracted from the estimated CVs. Then, the canonical variate dissimilarity (CVD) which quantifies model residuals in the CVA state-subspace is calculated using the estimated CVs. To explore the nonlinear features, the nonlinear principal components are extracted as nonlinear features through performing MKPCA on CVD. Fault detection indices are formed based on Hotelling's T-2 as well as Q statistics from the extracted linear and nonlinear features. Moreover, kernel density estimation is utilized to determine the control limits. The effectiveness of the proposed method is demonstrated by the comparisons with other relevant methods via simulations based on a closed-loop continuous stirred-tank reactor process.
机译：早期故障检测起到防止在工业过程中发生严重故障，故障的发生至关重要的作用。在大多数工业过程，线性和非线性关系并存。为了提高故障检测的性能，线性和非线性特征应当被同时考虑。在本文中，为数据驱动早期故障检测的新的混合线性非线性统计建模方法是通过使用串行模型结构紧密结合最近开发的典型变量相异分析和混合核主成分分析（MKPCA）提出。具体地，典型变量分析（CVA）首先被施加以估计从收集的过程数据的规范变量（CV）。线性特征从所估计的CV萃取。然后，典型变量相异（CVD），其量化模型残差在CVA状态子空间使用估计的CV来计算。探索非线性特征，非线性主要组分通过对CVD执行MKPCA提取为非线性的特征。故障检测指数形成了基于霍特林T-2以及Q统计指数从所提取的线性和非线性特性。此外，核密度估计被用来确定所述控制极限。所提出的方法的有效性是通过经由基于闭环的连续搅拌釜式反应器过程模拟其他相关方法比较证明。

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