Deep learning-based fault classification of rolling bearings under noisy conditions using CEEMD-VMD-IMF with magnitude scalogram images

Deep-learning robust feature learning ability makes it a valuable tool for automatic fault detection of rolling bearings in Industry 4.0. This work presents a novel approach for classifying faults in rolling bearings under highly noisy conditions using a deep learning algorithm. The proposed methodology utilizes a double modes decomposition method, specifically the complete ensemble empirical mode decomposition (CEEMD) and variational mode decomposition (VMD) techniques for signal denoising. The novelty of this study lies in its utilization of a double decomposition method, coupled with the selection of dominant intrinsic mode functions (IMFs), followed by continuous wavelet analysis (CWT) to generate magnitude scalogram images for input into a VGG16 deep learning architecture. First, bearing vibration signals are mixed with white Gaussian noise to simulate noisy real-world conditions. The noisy signal is then decomposed into intrinsic mode functions (IMFs) using the CEEMD technique, and a dominant IMF is selected based on its permutation entropy and correlation coefficient values. This dominant IMF is further decomposed into another set of IMFs using the VMD technique to obtain a final dominant IMF based on its CC value. After that, continuous wavelet analysis (CWT) is performed on selected IMF to obtain magnitude scalogram images, and these images are fed into VGG16 deep learning architecture for bearing fault classifications. The results obtained after applying the proposed methodology to the standard bearing dataset suggest that CEEMD-VMD-IMF with magnitude scalogram images perform well with deep learning technique and achieve a bearing fault classification accuracy above 99 % in extremely noisy conditions.