A Deep Learning Approach for Motor Fault Detection using Mobile Accelerometer Data

Abstract

Electrical machines, which provide many conveniences in our daily life, may experience malfunctions that may adversely affect their performance and the general functioning of the industrial processes in which they are used. These failures often require maintenance or repair work, which can be expensive and time consuming. Therefore, minimizing the risk of malfunctions and failures and ensuring that these machines operate reliably and efficiently play a critical role for the industry. In this study, a one-dimensional convolutional neural network (1D-CNN) based fault diagnosis model is proposed for electric motor fault detection. Motor vibration data was chosen as the input data of the 1D-CNN model. Motor vibration data was obtained from a mobile application developed by using the three-axis accelerometer of the mobile phone. Three-axis data (X-axis, Y-axis and Z-axis) were fed to the model, both separately and together, to perform motor fault detection. The results showed that even a single axis data provides error-free diagnostics. With this fault detection method, which does not require any connection on or inside the motor, the fault condition in an electric motor has been detected with high accuracy.

Keywords

• Motor fault diagnosis
• deep learning
• 1D-CNN

References

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