A normal cloud model-based method for failure mode and effect analysis utilizing heterogeneous information

Ensuring product reliability and safety is of utmost importance, with Failure Modes and Effects Analysis (FMEA) functioning as a vital risk assessment tool. Although traditional FMEA methodologies effectively identify and prioritize potential failures, they predominantly rely on precise numerical values, which fail to adequately capture the complexities inherent in expert evaluations. This paper addresses these limitations by proposing an innovative approach that utilizes the Normal Cloud Model (NCM) to integrate diversified forms of uncertainty, including fuzziness, randomness, and hesitation, into risk assessments. Heterogeneous information—including exact numbers, interval numbers, linguistic terms, and linguistic expressions—can all be represented and aggregated within our NCM-based FMEA method. The Risk Priority Number (RPN) for each failure mode is calculated as an NCM, thereby preserving uncertainty in evaluations and offering a more comprehensive risk assessment framework. A case study on information system failures illustrates the applicability and advantages of our proposed method, demonstrating its potential to enhance flexibility and accuracy. This research contributes to the evolving landscape of FMEA by integrating both qualitative and quantitative data, thereby advancing the methodology of reliability analysis.