IMAGE RECOGNITION FOR BEARING FAULT DETECTION BASED ON CONVOLUTIONAL NEURAL NETWORK

Bearing is an essential component for rotary machinery. The bearing serves as a fixture for position and provides stability for rotation. Bearing failure has detrimental effects on production schedules and operations. Consequently, detecting and diagnosing bearing issues in advance ensures the safety and reliability of rotating equipment systems, which will definitely save production costs and time. Therefore, this paper proposes the use of image recognition based on a convolutional neural network (CNN) for machine fault detection. Recent years have seen the development of deep learning-trained artificial intelligence, which aims to reduce human-induced errors and expenses. Initially, we acquire the vibration signals of the bearing from a test rig under four different conditions. We consider four bearing conditions: healthy bearings, inner race defects, outer race defects, and ball bearing defects. Each of the four conditions is recorded in the vibration time-series data, then converted into spectrogram images before feeding it to the CNN model for training. The performance of the CNN model is based on the comparison of two different models, which are Model A and Model B. Model B is developed based on the performance of Model A, where hyperparameter tuning is implemented to improve the performance. The result shows that the proposed model is capable of detecting and classifying the bearing faults up to 99.9% accuracy.