Early and Intelligent Bearing Fault Detection Using Adaptive Superlets

Faults in bearings interrupt the operation of three-phase induction motors (
$3\Phi $
IMs) and disturb factory production. Vibration signal analysis, feature extraction, and incorporation of artificial intelligence can enhance the ability of fault detection in bearings. However, implementing these techniques is challenging in industries due to harsh environmental conditions and massive computational power. Prioritizing the issue, this article presents a fusion method considering the time–frequency contents of vibration signals via the superresolution property of wavelets and a two-dimensional convolutional neural network (2-D-CNN). First, a laboratory-based real-time process is considered with variable frequency drive (VFD)-driven induction motor (IM), which is also exposed to environmental vibration. In the present work, a piezoelectric sensor is used to collect the vibration of IM during operation. Then, the collected time-series signals are stored in a computer through a data acquisition system (DAS). Furthermore, signals are segmented in small windows to extract respective feature images using continuous wavelet transform (CWT), Stockwell transform (ST), and adaptive superlet transform (ASLT). Thereafter, the image resolution is optimally resized and fed to a 2-D-CNN model for multilabel classification of the bearings. It is observed that ASLT in combination with an optimally designed 2-D-CNN (ASLT-2-D-CNN) with
$32\times 32\times $
3 resolution outperforms the other two fusion methods as it reports high accuracy and reduces runtime (training and testing) along with memory usage. The robustness of ASLT-2-D-CNN is also validated with two open-source bearing datasets. Thus, the result indicates that the proposed method for bearing fault detection can be deployed in the field to make IMs safe, which reduces the maintenance cost of the industry.