Abstract:In order to reduce the dependence of traditional vibration signal denoising methods on the prior information in time or frequency domain, a blind vibration signal denoising method based on deep autoencoder was proposed. In the absence of clean signals as the training target of the neural network, the adjacent sampling and expansion strategy was used to construct the training sample pair of the denoising deep neural network from the original signal. The deep neural network that can effectively denoise the original signals was obtained through self-supervised learning. And the applicability evaluation index was proposed to guide the setting of signal sampling frequency in practical engineering application. The denoising analysis of simulation signals and measured signals shown that the proposed method does not depend on the prior information of real signals and has great adaptive denoising effect for both steady and unsteady signals.
万若青,张纯,江汇强,黎寅斌. 基于深度自编码器的振动信号盲去噪方法[J]. 振动与冲击, 2023, 42(12): 118-125.
WAN Ruoqing,ZHANG Chun,JIANG Huiqiang,LI Yinbin. A blind denoising method of vibration signals based on a deep autoencoder. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(12): 118-125.
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