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Root cause analysis for quality accidents paper has been published by CAIE journal


Title:Root cause analysis approach based on reverse cascading decomposition in QFD and fuzzy weight ARM for quality accidents

Authors:Paiting Duan,Zhenzhen He,YihaiHe,Fengdi Liu,Anqi Zhang,Di Zhou.

Journal :Computers & Industrial Engineering.
SCI Impact factor:4.135(Q1) .
Published date:July 7

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.