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On the the use of reconstruction-based contribution for fault diagnosis

机译：论基于重构的贡献在故障诊断中的应用

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In the multivariate statistical process monitoring (MSPM) area, principal component analysis (PCA) and reconstruction-based contribution (RBC) are two commonly used techniques for fault detection and fault diagnosis problems, respectively. This paper starts with a review of the two methods. It is then pointed out that, when the dimensionality of the principal component subspace or the residual subspace in the PCA model is equal to 1, several fault detection indices based RBC will be invalid for fault diagnosis. Corresponding geometric interpretations of the invalidation cases are illustrated intuitively according to the definition of RBC. In order to perform effective fault diagnosis in such invalidation cases, three methods including the available combined index based RBC, the derived Mahalanobis distance based RBC, and the proposed chi-square contribution (CSC) are introduced. The CSC is constructed by employing a moving window and the effect of the window width on its diagnosis performance is investigated. The failure cases of the RBC, the effectiveness of the proposed CSC, as well as the comparison of these three methods for fault diagnosis are demonstrated by case studies on two numerical examples and a simulated three-tank system. (C) 2016 Elsevier Ltd. All rights reserved.
机译：在多元统计过程监视（MSPM）领域，主成分分析（PCA）和基于重构的贡献（RBC）是分别用于故障检测和故障诊断问题的两种常用技术。本文首先回顾了这两种方法。然后指出，当PCA模型中的主分量子空间或残差子空间的维数等于1时，基于RBC的多个故障检测指标对于故障诊断将是无效的。根据RBC的定义直观地说明了无效案例的相应几何解释。为了在这种失效情况下执行有效的故障诊断，介绍了三种方法，包括可用的基于组合索引的RBC，派生的基于Mahalanobis距离的RBC和拟议的卡方贡献（CSC）。通过使用移动窗口构造CSC，并研究窗口宽度对其诊断性能的影响。 RBC的故障案例，拟议中的CSC的有效性以及这三种故障诊断方法的比较通过两个数值示例和一个模拟的三缸系统的案例研究得以证明。 （C）2016 Elsevier Ltd.保留所有权利。

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