Multisensor Fault Diagnosis Modeling Based on the Evidence Theory

Abstract

Fault diagnosis is a typical multisensor information fusion problem. The information obtained from different sensors, such as sound, pressure, vibration, and temperature, can be considered as a piece of evidence. From the viewpoint of the evidence theory, the problem of multisensor fault diagnosis can be viewed as the problem of evidence fusion and decision. However, the information obtained from different sensors may be inaccurate, uncertain, fuzzy, or even conflict, so how to set up the fault diagnosis architecture of a distributed multisensor system and combine the conflict evidence should be taken into consideration. In this paper, the classical Dempster–Shafer evidence theory is described and the disadvantage of a classical Dempster's combination rule is discussed. In order to solve the counter-intuitive result when using the classical Dempster's combination rule, the Euclidean distance is proposed to characterize the differences between different pieces of evidence, and then the support degree of each evidence is generated and the weighted pieces of evidence can be combined directly using the classical Dempster's combination rule. Numerical simulation examples indicate that the proposed method has a better performance of analyzing the conflict between different pieces of evidence, especially for high conflict evidence. Therefore, compared with the existing methods, it has better applicability. According to the requirement of the Dempster–Shafer evidence theory, the fault diagnosis architecture of a distributed multisensor system is analyzed in detail, and a fault case of a rotating machine is used to illustrate that the proposed model is effective and superior, which can be used in practice.