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A data-driven approach for constructing the component-failure mode matrix for FMEA

Author

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  • Zhaoguang Xu

    (Dalian University of Technology)

  • Yanzhong Dang

    (Dalian University of Technology)

  • Peter Munro

    (BMW Brilliance Automotive Ltd.)

  • Yuhang Wang

    (Dalian University of Technology)

Abstract

Failure mode and effects analysis (FMEA) is one of the typical structured, systematic and proactive approaches for product or system failure analysis. A critical step in FMEA is identifying potential failure modes for product sub-systems, components, and processes, for which component-failure mode (CF) knowledge is necessarily needed as an important source of knowledge. However, this knowledge is usually acquired manually based on historical documents such as bills of material and failure analysis reports, which is a labor-intensive and time-consuming task, incurring inefficiency and plenty of mistakes. Nevertheless, few existing studies have developed an effective and intelligent approach to acquiring accurate CF knowledge automatically. To fill the gap, this paper proposes a method to construct the CF matrix automatically by mining unstructured and short quality problem texts and mapping as well as representing them as CF knowledge. Starting with mining the frequent itemsets of failure modes through Apriori algorithm, the method uses the semantic dictionary WordNet to find synonyms in the set of failure modes, based on which the standard set of failure modes is finally built. Subsequently, upon the previous work and components set, we design the component-failure mode matrix mining (CFMM) algorithm and apply it to establish the CF matrix from unstructured quality problem texts. Lastly, we examine the quality data of the seat module of an automobile company as a case study in order to validate the proposed method. The result shows that the failure mode extraction method with standardized features can extract failure modes more effectively than the FP-growth and K-means clustering methods. Meanwhile, the devised CFMM algorithm can extract more combinations of CF than the FP-growth method and build a richer CF matrix. Although different industries have distinct domain characteristics, our proposed method can be applicable not only to manufacturing but also to other fields needing FMEA to enhance product and system reliability.

Suggested Citation

  • Zhaoguang Xu & Yanzhong Dang & Peter Munro & Yuhang Wang, 2020. "A data-driven approach for constructing the component-failure mode matrix for FMEA," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 249-265, January.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:1:d:10.1007_s10845-019-01466-z
    DOI: 10.1007/s10845-019-01466-z
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    References listed on IDEAS

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    1. Chenxi Wu & Tefang Chen & Rong Jiang & Liwei Ning & Zheng Jiang, 2017. "A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1847-1858, December.
    2. Nitesh Khilwani & J. A. Harding, 2016. "Managing corporate memory on the semantic web," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 101-118, February.
    3. Ajith Tom James & O.P. Gandhi & S.G. Deshmukh, 2017. "Knowledge management of automobile system failures through development of failure knowledge ontology from maintenance experience," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 14(4), pages 425-445, October.
    4. Hu-Chen Liu & Yi-Zeng Chen & Jian-Xin You & Hui Li, 2016. "Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 805-816, August.
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    Cited by:

    1. Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.

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