Classification of Distributed Bearing Faults Using a Novel Sensory Board and Deep Learning Networks With Hybrid Inputs

Abstract

Distributed bearing faults are the most common ones in industry and create random vibration patterns, which make their detection difficult. They are caused by lubrication issues, contamination issues, electrical erosion, bearing roughness, or the spread of a local fault. This research mainly focuses on the distrusted bearing faults diagnosis using a multi-sensory kit. For this purpose, a novel deep-learning framework is proposed to detect these faults using 3 axis vibrations and one stray magnetic flux signal. The data is collected at 50 operating points, i.e., 10 speed and 5 torque levels. The proposed architecture benefits from a multi-input pipeline consisting of time-frame signals and extracted features. A feature-rich architecture is proposed combining convolutional and high-level information. Although a deep learning structure coherently learns from the features through convolutional and LSTM layers, 20 predefined features sampled from each instance are also fed into the network to improve accuracy. The robustness of the overall system is validated with train/test split data. Deep learning results are compared with two more classification algorithms, SVM and XGBoost. The high accuracy of the proposed model demonstrates the superiority of the deep learning architecture for distributed bearing fault detection.