Advanced deep learning approach for the fault severity classification of rolling-element bearings.

A hybrid methodology for bearing fault and severity analysis using param, eter-optimized variational mode decomposition (VMD) and deep learning (DL) algorithms is presented in this study. Different localized defects are artificially seeded at the contacting surfaces of a self-aligning bearings, and vibration data is generated (Case study II) under various radial-load and speed conditions for DL algorithm development. This data is pre-processed using the parameter-optimized VMD, where the intrinsic mode function with the highest kurtosis is processed using a deep learning (DL) algorithm. Particle swarm optimization is used for optimizing two VMD parameters. Further, seven different DL algorithms are implemented to classify various fault severity of rolling-element bearings. These algorithms are validated against the standard repository dataset of Case Western Reserve University (Case study I). The reliability of these algorithms is also tested using the generated dataset (Case study II) and the results show that 1D-CNN, WaveNet and gated recurrent unit have outperformed all other algorithms by achieving accuracies of 99.65%, 97.05% and 97.33%, respectively.