Abstract Quality accidents (QAs) of high frequencies in various fields have caused large economic and reputational losses to manufacturers, and identification of the root causes of vicious QAs is a top priority and a major challenge for manufacturers. Especially in the era of big data, the large number of data could be collected from the product life cycle easily, these high-dimensional big data always bear so many un-correlation noise information, which has caused serious problem. The accurate and heuristic root cause analysis for QAs is an important and challenging task in exploring this mechanism due to the fuzzy and vague nature of the collected big quality data. Thus, in this study, a heuristic root cause identification solution based on the fuzzy weighted association rule mining (FWARM) for QAs is proposed. First, the formation mechanism of QAs and big quality accident data is expounded, and a big data driven root cause analysis framework of QAs is presented with the aid of reverse cascading decomposition in Quality Function Deployment (QFD). Second, principal component analysis (PCA) is adopted to eliminate redundancy and reduce data dimension of original process feature parameters from raw data in low-dimensional space so that the key variables as the potential root cause candidates can be extracted. Third, considering the fuzzy mechanism and vague nature of big data, a heuristic root cause identification approach based on FWARM is established, and the weight of nodes on the accident-relevance tree is computed by fuzzy weight coefficient. Finally, the proposed approach is verified with a case study of a quality accident analysis of a washing machine. Results shows that the proposed approach is conducive to heuristically identify the root causes of QAs in the context of big data.

中文翻译：

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基于QFD反向级联分解和模糊权重ARM的质量事故根本原因分析方法

摘要 各领域高频质量事故（QA）给制造商造成了巨大的经济和声誉损失，找出恶性QA的根本原因是制造商面临的首要任务和重大挑战。尤其是在大数据时代，产品生命周期的海量数据很容易被采集，这些高维的大数据总是承载着如此多的非相关噪声信息，造成了严重的问题。由于收集的大质量数据的模糊性和模糊性，对 QA 进行准确和启发式的根本原因分析是探索这种机制的一项重要且具有挑战性的任务。因此，在本研究中，提出了一种基于模糊加权关联规则挖掘（FWARM）的启发式根本原因识别解决方案。第一的，阐述了质量保证和质量事故大数据的形成机制，并借助质量功能部署(QFD)中的反向级联分解，提出了质量保证大数据驱动的根本原因分析框架。其次，采用主成分分析（PCA）从低维空间的原始数据中消除冗余并降低原始过程特征参数的数据维数，从而提取关键变量作为潜在的根本原因候选。第三，考虑大数据的模糊机制和模糊性，建立了基于FWARM的启发式根本原因识别方法，通过模糊权重系数计算事故相关树上节点的权重。最后，通过洗衣机质量事故分析的案例研究验证了所提出的方法。