Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis
Multiple Fault Diagnosis Method in Multistation Assembly Processes Using Orthogonal Diagonalization Analysis
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DOI：
10.1115/1.2783228
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发表时间：
2008-02
影响因子：
4
通讯作者：
Z. Kong;D. Ceglarek;Wenzhen Huang
中科院分区：
文献类型：
--
作者：
Z. Kong;D. Ceglarek;Wenzhen Huang
尺寸控制对大型复杂多工位装配系统的整体产品质量和性能具有重要影响。迄今为止，识别与工艺相关的故障，导致大的变化的关键产品特性（KPC）仍然是最关键的研究课题之一，在尺寸控制。提出了一种将多元统计分析与工程模型相结合的多工位装配过程多故障诊断新方法。所提出的方法基于以下步骤：（i）对使用过程和产品信息的状态空间表示获得的故障模式进行建模，所述过程和产品信息明确地表示由关键控制特性（KCCS）表示的与过程相关的误差源与KPC之间的关系，以及（ii）使用主成分分析（PCA）对测量数据进行正交对角化将测量数据投影到由预定故障模式形成的仿射空间的轴上。正交对角化允许估计所识别故障的根本原因的统计显著性。多站装配过程故障诊断的案例研究说明并验证了所提出的方法。
Dimensional control has a significant impact on overall product quality and performance of large and complex multistation assembly systems. To date, the identification of process-related faults that cause large variations of key product characteristics (KPCs) remains one of the most critical research topics in dimensional control. This paper proposes a new approach for multiple fault diagnosis in a multistation assembly process by integrdting multivariate statistical analysis with engineering models. The proposed method is based on the following steps: (i) modeling of fault patterns obtained using state space representation of process and product information that explicitly represents the relationship between process-related error sources denoted by key control characteristics (KCCS) and KPCs, and (ii) orthogonal diagonalization of measurement data using principal component analysis (PCA) to project measurement data onto the axes of an affine space formed by the predetermined fault patterns. Orthogonal diagonalization allows estimating the statistical significance of the root cause of the identified fault. A case study of fault diagnosis for a multistation assembly process illustrates and validates the proposed methodology.