Improving compound fault diagnostic accuracy of drilling permanent magnet synchronous motor using information fusion

Abstract

The diagnostic accuracy of compound faults is low due to the similarity of fault symptoms. Accurate diagnostic results can only be obtained by fusing all available information on compound faults. However, a single information fusion method cannot fuse all available information well because the characteristics of fault information are different. A method combining three kinds of information fusion technology is proposed to improve the compound fault diagnostic accuracy of the drilling permanent magnet synchronous motor (DPMSM). The fault symptoms are fused by Bayesian inference using the Bayesian Network. The additional information is fused by fuzzy logic operation using the Fuzzy Theory. The fusion results obtained from the above two methods are fused as evidence by mathematical logic using Evidence Theory to obtain the final diagnosis results. The effectiveness of the proposed method is verified by Cases. After fusion, “RoEc” fault in Case A rises to 0.933 from 0.826, “ItSc” fault in Case B rises to 0.524 from 0.262, and “RoEc” fault in Case C rises to 0.344 from 0.046 while “ItSc” down to 0.172 from 0.458. Cases indicate it can effectively improve the fault diagnostic accuracy and correct the results of missed diagnosis and misdiagnosis in compound faults of the DPMSM in drilling engineering. The effect of this method is influenced by the selection of specific experts and the accuracy of statistical data.