Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

• Published in: Mechanical systems and signal processing Vol. 38; no. 1; pp. 113 - 124
• Main Authors: Lei, Yaguo, Han, Dong, Lin, Jing, He, Zhengjia
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05-07-2013
• Subjects: Adaptive stochastic resonance; Aircraft components; Automotive components; Fault diagnosis; Feature extraction; Gear teeth; Gearboxes; Planetary gearboxes; Signal processing; Stochastic resonance; Transmissions (automotive); Vibration; Weak feature extraction; Fault diagnosis; Planetary gearboxes; Weak feature extraction; Adaptive stochastic resonance
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2012.06.021

Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness.
► An adaptive stochastic resonance method is proposed to extract weak features. ► Ant colony algorithms are utilized to optimize stochastic resonance parameters. ► The experimental results demonstrate the effectiveness of the proposed method.