Fault Detection and Diagnosis in Electric Motors Using Convolution Neural Network and Short-Time Fourier Transform

Purpose Fault diagnosis is vital to any maintenance sector since early fault detection can avoid catastrophic failures and also a waste of both time and money. Common defect diagnostic methods take just a few features from the vibration signal, which can lead to a wrong analysis. Deep Learning (DL) is well-known for its ability to extract features from a signal, and a Convolutional Neural Network (CNN) is one of the most successful deep learning approaches. Methods This paper uses a CNN with Short-time Fourier Transform (STFT), a time-frequency feature map, to extract as much information as possible from vibration signals. To validate the method, an experimental bench was used where it was possible to simulate up to six different faults. A vibration signal in the time domain was recorded to obtain the STFT response. Then, a CNN is trained to diagnose and predict the faults, considering the STFT as the only input. Results The findings suggest that the proposed method can properly identify the various faults. Conclusion Since the approach is based on frequency domain analysis, it can be easily replicated for different motors.

Automated summary

This paper proposes a method for fault diagnosis of vibration signals using a convolutional neural network combined with short-time Fourier transform. The experimental results show that the method can effectively identify different faults.