EEMD method and WNN for fault diagnosis of locomotive roller bearings

By Yaguo Lei, Zhengjia He, Yanyang Zi. Expert systems with applications, 2011

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Wavelets for fault diagnosis of rotary machines: A review with applications (2014, background): “tion function to construct a wavelet neural network (WNN) for fault diagnosis of locomotive roller bearings [76].”
Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery (2018, background): “(LVQ) (Jiang and Liu, 2011), wavelet neural network (WNN) (Lei et al., 2011), extreme learning machine (ELM) (Tian et al.”
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018, methodology): “always been key to attaining the enhancement of security and reliability of industry equipment [1].”
Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, background): “More details about the 10 TD features and 13 FD features can be seen in [32] and [11], respectively.”
An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017, methodology): “[4] calculated ten statistical parameters to reflect bearing conditions, and then input the selected features into ANN for fault classification.”