Improving Fault Diagnosis of the Drilling Permanent Magnet Synchronous Motor (DPMSM) in Harsh Environments: A Novel Approach Using Object-Oriented Bayesian Network (OOBN).

This article focuses on developing a fault diagnosis and localization model for the drilling permanent magnet synchronous motor (DPMSM), which consists of multiple identical subsystems operating under harsh downhole conditions. To address difficulties in building and modifying traditional Bayesian network (BN) models for such complex systems, the study introduces an Object-oriented Bayesian network (OOBN) approach that integrates forward and backward BN submodels as instance nodes, enabling efficient fault type identification and localization. Fault features are extracted from current signals using empirical wavelet transform, Fast Fourier transform, and principal component analysis, while conditional probabilities are derived from both expert knowledge and data-driven methods. Sensitivity analyses and case studies demonstrate that the OOBN-based system diagnostic model effectively reduces modeling complexity and improves diagnostic accuracy for single and compound faults in the DPMSM, though challenges remain regarding computational complexity and automated structure learning in large-scale applications.