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A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

  • Advances in Intelligent Data Processing and Analysis
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Abstract

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups.

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Acknowledgments

The authors gratefully acknowledge the Exploratory Research Grant Scheme (ERGS/1/11/TK/UNIMAS/03/05), Fundamental Research Grant Scheme (FRGS/ICT02(01)/997/2013(38)), and Research Acculturation Collaborative Effort (RACE/F2/TK/UNIMAS/5) for supporting this research work and Universiti Malaysia Sarawak for the facilities.

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Authors and Affiliations

  1. Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan, Malaysia

    Kai Meng Tay & Chian Haur Jong

  2. Centre for Intelligent Systems Research, Deakin University, Victoria, Australia

    Chee Peng Lim

Authors
  1. Kai Meng Tay
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  2. Chian Haur Jong
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  3. Chee Peng Lim
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Correspondence to Kai Meng Tay.

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Tay, K.M., Jong, C.H. & Lim, C.P. A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry. Neural Comput & Applic 26, 551–560 (2015). https://doi.org/10.1007/s00521-014-1647-4

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  • DOI: https://doi.org/10.1007/s00521-014-1647-4

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