Design a Hybrid Model of Mel-Spectrogram and VGG16 Based Convolutional Neural Network for Diagnosing Motor Bearing Faults (Aintelia Science Notes, 2024)
Owing to rolling bearings' significance as one of the most frequently utilized components of industrial machinery. Therefore, it is crucial to establish a system to check the bearing's condition. In this research, a hybrid method of spectral feature extraction combined with Convolutional Neural Network (CNN) designed to classify faults. Firstly, Mel-spectrogram applied as a method for pre-processing by transforming the raw vibration data. Secondly, a Mel-VGG16 employed as a classifier to detect the bearing faults. The proposed technique tested on the CWRU benchmark dataset to bearings in different rotating speeds. The case study results demonstrated that the proposed model could obtain a higher testing accuracy.
Keywords: Rolling bearing; Mel-spectrogram; VGG16; Deep Learning; Predictive maintenance