Convolutional neural network based rolling-element bearing fault diagnosis for naturally occurring and progressing defects using time-frequency domain features

Convolutional Neural Networks (CNN) are becoming increasingly popular for bearing fault diagnosis due to their ability to automatically capture the sensitive fault information without the need for expert knowledge. Most of these applications are developed considering vibration data from artificially induced faults. However, bearing failure in real-life can show huge damage variations even within a single category of failure which artificially induced failures are unable to represent. Thus, in this paper, the performance of classical CNN is evaluated on bearings with naturally occurring and progressing defects from the Paderborn University Dataset. A three-class (Healthy, Inner Race Fault & Outer Race Fault) classification problem is solved considering five bearing conditions within each class. These conditions vary in terms of bearing operating hours, damage mode, damage repetition pattern, the extent of damage, etc. The classification accuracy is evaluated under two cases: 1) at least a portion of data from each bearing condition from all classes is used in training; 2) data from all available conditions are considered for training except from one condition which is used explicitly for testing. Within each case, the effect of changing the domain of the input data is evaluated on the achieved accuracy. Three input signals based on vibration data (raw time domain signal, envelope spectrum, and spectrogram) were explored for their representation effectiveness. The proposed CNN with a spectrogram of the vibration signal as input achieves better results than similar architectures. Finally, the potential challenges that come along with the implementation of Deep Learning technologies for industrial applications are discussed and future research directions are proposed.