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

• Published in: Journal of mechanical science and technology Vol. 32; no. 1; pp. 37 - 47
• Main Authors: Yu, Jun, Bai, Mingyou, Wang, Guannan, Shi, Xianjiang
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Mechanical Engineers 2018; Springer Nature B.V; 대한기계학회
• Subjects: Algorithms; Bayesian analysis; Classifiers; Control; Data processing; Diagnostic systems; Dynamical Systems; Engineering; Fault diagnosis; Gearboxes; Industrial and Production Engineering; Mechanical Engineering; Reduction; Vibration; 기계공학; Fault diagnosis; Characteristic relation; Flexible naive Bayesian classifier; Incomplete information; Planetary gearbox; Attribute reduction
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-017-1205-y

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.