Multi-periodicity and Multi-scale Network for Motor Fault Diagnosis

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Intelligent fault diagnosis of motor is of tremendous significance to ensuring reliable industrial production, and deep learning methods have gained notable achievements recently. Most researches automatically extracted fault information from raw monitoring signals with deep models, whereas the strong periodic temporal information containing in the signals were ignored. To tackle this limitation, a multi-periodicity and multi-scale network is proposed in this paper. 1D monitoring signals are transformed into 2D space with multiple various periods, allowing for the straightforward reflection and modeling of variations both within and between different periods. Multi-scale learning is introduced to extract temporal information from the multi-periodicity representations with multiple scales in a parameter-efficient way. Experiments carried out on a motor fault dataset verified the effectiveness of the proposed method. The results demonstrate that over 99% diagnosis accuracy can be achieved with onechannel vibration signals, and superior performance is obtained under diverse noise conditions compared with other methods.