Risk assessment based on FMEA combining DEA and cloud model: A case application in robot-assisted rehabilitation

Abstract

Failure Mode and Effects Analysis (FMEA) is a systematic and proactive risk assessment method based on multidisciplinary background for developing the safety and reliability of the application working system. However, FMEA approach comes with its inherent drawbacks, such as the inappropriate Risk Priority Number (RPN), equal weight of different risk factors, and neglect of uncertainty expert judgement. Our study aims to remove the bottlenecks and improve the risk assessment of FMEA in an uncertainty environment. In the proposed FMEA method, SHELL model is used to identify and describe the potential failure modes and the 7-level cloud model scale is introduced into the cognitive expression of linguistic evaluation to deal with the uncertainty of the concepts in human knowledge. Then Data Envelopment Analysis (DEA) model is extended to capture the efficiency score of each Decision-making Unit (DMU, failure mode) in FMEA, combined with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The risk priority and the level classification of failure modes are thus given. The proposed FMEA method integrates the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessment information and the merits of the modified DEA model in solving multiple decision-making problems, considering the subjective and objective weight distribution of both experts and risk factors. The risk problem of robot-assisted rehabilitation can be regarded as a vital multi-criteria issue. Therefore, through an application to rehabilitation, a comparative and simulation analysis is carried out to demonstrate the feasibility and effectiveness of this method.