Application of an information fusion method to compound fault diagnosis of rotating machinery

Aiming at how to use the multiple fault features information synthetically to improve accuracy of compound fault diagnosis, an information fusion method based on the weighted evidence theory was proposed to effectively diagnose compound faults of rotating machinery. Firstly multiple fault features were extracted by the genetic programming. Each fault feature was separately used to act as evidence and the initial diagnosis accuracy was regarded as the weight coefficient of the evidence. Then through the negative selection algorithm, the diagnosis ability of the local diagnosis was advanced and an impersonal means of obtaining basic probability assignment was given. Finally the fusion result was obtained by utilizing the weighted evidence theory into the decision-making information fusion for the preliminary result. By comparing the diagnosis results with other artificial intelligence algorithm, experiment result indicates that using multiple weighted evidences fusion can improve the diagnostic accuracy of compound fault.