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Design and Analysis for Fall Detection System Simplification

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Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion

Xinyang Deng¹, Wen Jiang²

¹School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China. xinyang.deng@nwpu.edu.cn.

Sensors (Basel, Switzerland)

|September 13, 2017

Summary

This study introduces D numbers for fuzzy risk evaluation in Failure Mode and Effect Analysis (FMEA). This novel approach improves reliability by addressing non-exclusive fuzzy evaluations in risk assessment.

Keywords:

D numbers dempster-shafer evidence theory failure mode and effects analysis fuzzy risk evaluation fuzzy uncertainty multi-sensor information fusion

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Area of Science:

Engineering
Risk Management
Data Science

Background:

Failure Mode and Effect Analysis (FMEA) is crucial for enhancing system reliability.
Traditional risk priority number (RPN) methods have limitations in risk evaluation.
Fuzzy risk evaluation is an emerging area in FMEA research.

Purpose of the Study:

To develop a novel model for fuzzy risk evaluation in FMEA.
To address the non-exclusiveness of fuzzy linguistic variable evaluations.
To improve the accuracy of risk assessment in FMEA.

Main Methods:

Utilizing D numbers to model non-exclusive fuzzy evaluations.
Applying a D numbers-based multi-sensor information fusion method.
Comparing the proposed model with existing methods using an illustrative example.

Main Results:

The D numbers model effectively handles non-exclusive fuzzy evaluations.
The proposed fusion method enhances fuzzy risk evaluation in FMEA.
The new model demonstrates superior effectiveness compared to traditional approaches.

Conclusions:

D numbers provide a robust framework for fuzzy risk evaluation in FMEA.
Multi-sensor information fusion with D numbers improves risk prioritization.
This research offers a more effective tool for enhancing product and system reliability.