Abstract：Build fault diagnosis model based on deep learning by time domain or frequency domain as a low-level input information directly can effectively weaken the interference of man-made factors and improve the development of artificial intelligence in mechanical fault diagnosis. However, time domain signal length is difficult to draw, while frequency domain signalis too length lossing computation efficiency. Aiming at the problem, put forward to extract frequency domain signal envelope, which would get the trend of frequency information, then combined with sparse autoencoder to constructs the fault diagnosis mode. Gearbox fault diagnosis experiments indicate that, comparing with the original input frequency domain, the proposed method can effectively speed up the computation process and decrease the memory space, while keeping the ability of condition recognition.
张绍辉 罗洁思. 基于频谱包络曲线的稀疏自编码算法及在齿轮箱故障诊断的应用[J]. 振动与冲击, 2018, 37(4): 249-256.
Zhang Shaohui Luo Jiesi . Sparse autoencoder algorithm based on spectral envelope curve and Its application in gearbox fault diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(4): 249-256.
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