Fault Diagnosis of Small Sample Automobile Planetary Gearboxes Based on Continuous Wavelet Transform and Model Agnostic Meta Learning

Abstract

Aiming at the problem that the vibration signal of planetary gearboxes has strong non-stationary characteristics， few fault samples and the dependence of traditional deep learning on data， an intelligent diagnosis method for planetary gearboxes based on continuous wavelet transform（CWT） and model agnostic meta learning（MAML） is proposed. First， the vibration signal of the planetary gearbox is converted into a time-frequency image through CWT， which effectively expresses the non-stationary characteristics of the planetary gearbox； then， the ability of “learning to learn” of MAML is used to train small samples of time-frequency images， and finally the “unseen” faults of planetary gearboxes are tested. Through fault diagnosis experiments of planetary gearboxes under different sample sizes， working conditions and noise environments， a conclusion is drawn that the proposed method has higher recognition accuracy， generalization and robustness compared with other methods.

Keywords

• Planetary gearboxes;Continuous wavelet transform;Model agnostic meta learning;Few shot learning;Fault diagnosis