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<p>Failure Mode and Effects Analysis (FMEA) is a widely adopted risk management tool, yet its conventional form suffers from limitations in evaluating failure modes, weighting risk factors, and prioritizing risks. To address these challenges, this study proposes an advanced FMEA framework integrating Multi-Attribute Decision-Making (MADM) with k-means clustering. The approach begins by capturing expert linguistic evaluations of failure modes through T-Spherical Fuzzy Numbers (TSFNs). Next, dependent operators and the CRITIC method dynamically assign weights to experts and risk factors. A T-Spherical Fuzzy Schweizer-Sklar aggregation operator then calculates risk indices, followed by an optimized k-means algorithm to classify failure mode priorities. A case study on machining center risk assessment demonstrates the model’s effectiveness: it outperforms traditional FMEA in conveying nuanced expert judgments, resolving risk factor ambiguity, and generating pragmatic failure rankings. The results validate the framework as a more robust and adaptable tool for industrial risk decision-making.</p>

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A novel model for failure mode and effect analysis based on K-means clustering under a T-spherical fuzzy environment

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Lei Zheng,
  • Lifeng Wang,
  • Zheng Wang

摘要

Failure Mode and Effects Analysis (FMEA) is a widely adopted risk management tool, yet its conventional form suffers from limitations in evaluating failure modes, weighting risk factors, and prioritizing risks. To address these challenges, this study proposes an advanced FMEA framework integrating Multi-Attribute Decision-Making (MADM) with k-means clustering. The approach begins by capturing expert linguistic evaluations of failure modes through T-Spherical Fuzzy Numbers (TSFNs). Next, dependent operators and the CRITIC method dynamically assign weights to experts and risk factors. A T-Spherical Fuzzy Schweizer-Sklar aggregation operator then calculates risk indices, followed by an optimized k-means algorithm to classify failure mode priorities. A case study on machining center risk assessment demonstrates the model’s effectiveness: it outperforms traditional FMEA in conveying nuanced expert judgments, resolving risk factor ambiguity, and generating pragmatic failure rankings. The results validate the framework as a more robust and adaptable tool for industrial risk decision-making.