Towards better benchmarking using the CWRU bearing fault dataset

Applications of machine learning to machine fault diagnosis: A review and roadmap

(2020) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

(2019) Bin Yang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

(2018) Wei Zhang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis

(2018) Yuanhang Chen et al.   NEUROCOMPUTING

A Convolutional Neural Network Based on a Capsule Network with Strong Generalization for Bearing Fault Diagnosis

(2018) Zhiyu Zhu et al.   NEUROCOMPUTING

Deep learning and its applications to machine health monitoring

(2018) Rui Zhao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

(2017) Wei Zhang et al.   SENSORS

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

(2016) Feng Jia et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

(2015) Wade A. Smith et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Rolling element bearing diagnostics—A tutorial

(2010) Robert B. Randall et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING