Deep learning segmentation models for estimating the health status of induction motor bearing.

The demand for accurate health status assessment of bearings in rotating electrical machines is rising. However, traditional fault diagnosis methods based on deep learning are not effective in efficiently and intelligently determining the health condition of the bearings and also requires large memory, hence constraining their use in low-cost microcontrollers. In this work, combination of short time Fourier transform (STFT) with Ostu’s threshold method and MobileNet-V2 with U-Net (MV2UNet) is proposed for rolling bearing fault health status. The proposed method obtains the STFT images of the raw vibration signals from the laboratory experimental setup. Then the Ostu’s threshold method is used to make the labels for STFT images. Finally, the proposed method is trained with these labelled images to detect the health status of each bearing fault. The proposed model achieved the Dice coefficient is 0.9834 and the intersection over union is 0.9479. The main advantage of the proposed model has low memory requirements, which makes it well-suited for implementation in affordable hardware devices like microcontrollers. Finally, the proposed method result is compared with U-Net, DeepLabV3+ with MobileNet-V2 and DeepLabV3+ with ResNet-50. [ABSTRACT FROM AUTHOR]