Automated fault classification of asynchronous motor using mobile phone accelerometer and Parallel Residual CNN-GRU - Ertargin, Merve; Orhan, Ahmet; Yildirim, Ozal; Gurgenc, Turan (Elsevier Sci Ltd, 2025)
In this study, a fault detection system that classifies motor faults using deep learning without requiring any sensor connections inside or on the motor setup is proposed. The focus is on the electrical and mechanical faults of asynchronous motors, which are widely used in the industry. A mobile application developed using Flutter can collect vibration data using the three-axis accelerometer sensor in smartphones. By placing a smartphone with the application installed on the motor for which fault detection is desired, vibration data on three axes (x, y, z) is collected to train deep models. Subsequently, test data is used to identify the fault status and class of the electric motor. Different conditions and scenarios were created to test the adequacy of smartphone accelerometer sensors in detecting motor faults. Fault classification with vibration data obtained under different motor operating speeds in loaded and unloaded conditions achieved accuracy rates of over 99%.
Keywords: Motor fault classification; Electrical and mechanical faults; Deep learning; Mobile phone accelerometer sensor