Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
2019
Jiang, Guoqian He, Haibo Yan, Jun Xie, Ping
fault diagnosis feature extraction gears learning (artificial intelligence) mechanical engineering computing neural nets pattern classification vibrational signal processing wind turbines signal processing CNN architecture intelligent fault diagnosis method health conditions raw vibration signals wind turbine gearbox multiscale convolutional neural networks WT gearbox test rig MSCNN approach hierarchical learning structure multiscale learning classification multiscale feature extraction multiscale convolutional neural network architecture end-to-end learning-based fault diagnosis system Feature extraction Fault diagnosis Vibrations Convolutional neural networks Wind turbines Machine learning Signal processing Convolutional neural network (CNN) classification deep learning intelligent fault diagnosis multiscale feature extraction wind turbine (WT) gearbox
IEEE Trans. Ind. Electron.
In this paper, our goal is to develop an end-to-end fault diagnosis system based on CNNs, which is motivated by its excellent feature learning ability. The desirable system can automatically learn and discover discriminative features from raw temporal vibration signals and then classify different health conditions of the WT gearbox.
convolutional neural networks
The key idea of the proposed MSCNN is to incorporate multiscale feature learning ability into the traditional CNN architecture.
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