Abstract
This paper is concerned with the fault detection problem of planetary gearbox in wind energy conversion systems (WECSs). An effective method based on empirical mode decomposition (EMD) and multi-scale convolutional neural network (MSCNN) is proposed. Specifically, the non-stationary vibration signals of the gearbox are decomposed by using the EMD, so that its signal-to-noise ratio (SNR) and characteristic information are improved. Then, a hierarchical convolutional neural network is applied to adaptively extract multi-scale features from the decomposed signal components. Finally, a binary classifier based on cross entropy is employed to automatically realize the classification of the obtained multi-scale features. The effectiveness and superiority of the proposed method are verified on the experiments with vibration data sets from a true WECS.
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