A Prognostics Approach for Gearbox based on Spectrogram and Deep Learning



Dengwei Song Hua Su Jian Ma Likun Chao Yu Ding Chen Lu


This paper presents a method and solution for Asia Pacific Conference of the Prognostics and Health Management 2017 Data Challenge. Considering the complex of gearbox signal, a novel approach utilizing spectrogram and deep learning is proposed. Spectrogram can transform the raw signal into time-frequency space that is an effective method to process vibration signal of gearbox. Deep learning is a useful method that has the ability of automatically feature learning. The spectrogram is obtained by using short-time Fourier transform (STFT). Then, the way of the stacked auto-encoders (SAE), is trained to auto-extract feature and realize the prediction. For diagnosis, the cepstrum algorithm is performed to simplify the spectrum and find the most severely-degraded component. This solution earned the 3rd highest score of the results from all teams in the competition, which demonstrate the effective of proposed method.

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Data Challenge Papers