Improving Fault Diagnosis of the Drilling Permanent Magnet Synchronous Motor (DPMSM) in Harsh Environments: A Novel Approach Using Object-Oriented Bayesian Network (OOBN)
Affiliations: [a] College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao, China
| [b] National Engineering Research Center of Marine Geophysical Prospecting and Exploration and Development Equipment, China University of Petroleum, Qingdao, China
Abstract: The drilling permanent magnet synchronous motor (DPMSM) contains multiple subsystems with identical structures and has a high probability of failure because the downhole working conditions are harsh. Therefore, the quick localization of faults is difficult to determine although the fault type may be identified in time. The system diagnostic model based on the Bayesian network (BN) can be used for fault diagnosis and localization for components in subsystems, but it is difficult to build and modify due to the complex system in practice. New methods are necessary to reduce the difficulty of building and modifying models. In this study, object-oriented ideas are introduced into the BN to establish a system diagnostic model based on an Object-oriented Bayesian network (OOBN) for the DPMSM. First, the fault diagnostic models for subsystems based on BN are established, respectively. Then, submodels of forward and backward based on BN are instantiated as instance nodes. Next, instance nodes are connected through input nodes and output nodes to establish the OOBN-based system diagnosis model. Finally, the system diagnosis model is validated by sensitivity analysis and the effectiveness is discussed in Cases. The system diagnosis model can effectively reduce the difficulties of modeling and modifying.
