Fault diagnosis of planetary gearbox with incomplete information using assignment reduction and flexible naive Bayesian classifier

In planetary gearbox operation, there are many uncertain factors that may result in incomplete diagnostic information, such as measurement instrument faults, limitation of transmission capacity, and data processing. Therefore, it has been one of the greatest obstacles to fault diagnosis of planetary gearbox. To address this issue, a novel fault diagnosis method of planetary gearbox with incomplete information using assignment reduction and Flexible naive Bayesian classifier (FNBC) is proposed. Characteristic relation was utilized to preprocess incomplete diagnostic information. Then, assignment reduction algorithm based on characteristic relation was used to remove irrelevant or redundant condition attribute values. Finally, FNBC was constructed to reason diagnosis results. To validate the performance of the proposed method, a fault diagnosis experiment was conducted. The experimental studies demonstrate the proposed method can be utilized to diagnose planetary gearbox faults with incomplete diagnostic information, reduce computational complexity, and enhance reasoning accuracy.