A New Evidence Reasoning Rule Assessment Model Considering the Credibility of Results

Evidence reasoning (ER) rule can effectively integrate qualitative knowledge and quantitative data and simultaneously flexibly deal with various types of uncertain information. Although ER rule has been applied in many engineering fields, many challenges still need to be addressed. For example, due to the lack of labeled data samples and the inherent fuzziness in information transformation of semantic reference values in health assessment of complex electromechanical systems, the accuracy of the assessment results is difficult to be measured quantitatively, which reduces the credibility of the ER model. To address this issue, a new ER model considering the credibility of results is proposed in this article, denoted as the ER-c model. First, a method for calculating the assessment results credibility is designed by establishing the relationship between the internal and external measurement information of a complex electromechanical system. Second, the objective function for the ER-c model optimization process is established according to the credibility and optimized using the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. Finally, in the fields of instrument measurement in practical engineering, the problem that the hidden information such as health status of complex electromechanical system cannot be accurately measured is improved in this article. A case study of a body-in-white welding robot is conducted, and the results demonstrate the efficacy of the proposed method and provide a reliable foundation for measuring the credibility of assessment results.