A novel fuzzy-based FMEA method for risk assessment of in-vehicle information system under uncertainty

Existing fuzzy-based failure mode and effect analysis (FMEA) is a widely used method to assess potential failure mode risks in a risk assessment. However, most fuzzy-based FMEA methods suffer from a problem that they only consider three classical risk factors (i.e., occurrence, severity, and detection) to evaluate the risk in risk priority number (RPN) but ignore economic factor (cost), which leads to an inaccurate risk ranking of failure modes with higher total potential maintenance cost. To solve the problem, a novel fuzzy-based FMEA method is proposed, which integrates the economic factor (cost) into the fuzzy-based FMEA framework. In the proposed method, the fuzzy linguistic variables are utilised to represent the imprecise assessment information. Then, a comprehensive weight method based on the fuzzy set and the fuzzy entropy methods is developed to obtain the subjective weights and the objective weights of risk factors, respectively. Additionally, a periodic detection model and fuzzy grey relational analysis (FGRA) are integrated to calculate the potential maintenance cost and prioritise the risk of potential failure modes. Eventually, the proposed method is applied to an in-vehicle information system (IVIS) to demonstrate its effectiveness.