Fault diagnosis method based on power-average operator and evidence theory

The evidence theory can be used to deal with uncertainty information; thus it is advantageous for fault diagnosis, as it can improve the diagnosis accuracy. From the perspective of evidence theory, the information obtained from each sensor can be regarded as a piece of evidence. Engine fault diagnosis based on multi-sensor information is an evidence fusion problem. In this paper, evidence theory is used as a modeling method for multi-sensor information describing engine state. First, when processing the eigenvalue sample data, the method of power-average operator is introduced to improve the accuracy of the whole fault diagnosis system. The basic probability assignment is generated by measuring the distance between the eigenvalue and the fault prototype. Then the fusion results are evaluated by quantifying the evidence information. Finally, the method is verified using a case of engine failure and compared with other methods, and the comparison results prove the authenticity and reliability of the method.