Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification

By Chen Lu, Zhenya Wang, Bo Zhou. Advanced Engineering Informatics, 2017

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A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “Gear/bearing, wind turbine/bearing, pump [51] / [52] Motor/compressor Images [53] [54] [55] [56] Bearing [57] [58] [59] / [60] Bearing, gear/bearing, pump Time or frequency domain transform [62] [63] [64] [65] [66] [67] [68] [69] / [61] / [70] [71] [72] Bearing/bearing, gear/gear Wavelet transform [”
1D Convolutional Neural Networks and Applications: A Survey (2019, methodology): “Furthermore, they have been frequently used in processing sequential data including Natural Language Processing and Speech Recognition [34,35] and even 1D signals e.g., vibration [36,37].”
A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, background): “[32] studied a hierarchical CNN to the 6112 Neural Computing and Applications (2020) 32:6111–6124”
Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, methodology): “90% [107] CNN 4 Softmax Noise-resilient 90% 92.”
Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “• The classical CNN exhibits a good denosing capability [106].”