FaultNet: A Deep Convolutional Neural Network for Bearing Fault Classification

• Published in: IEEE access Vol. 9; pp. 25189 - 25199
• Main Authors: Magar, Rishikesh, Ghule, Lalit, Li, Junhan, Zhao, Yang, Farimani, Amir Barati
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Channels; Convolutional neural network; Convolutional neural networks; Deep learning; Fault detection; FaultNet; Feature extraction; featurization; Machine learning; Mechanical systems; Neural networks; Signal classification; Signal processing; Two dimensional displays; Vibration analysis; Vibrations

The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal data can provide us a greater understanding of different faults...