LN-MRSCAE: A novel deep learning based denoising method for mechanical vibration signals

Vibration signals are used to monitor the running state of mechanical equipment, but always suffer from a lot of noise in the acquisition process. In order to eliminate noise interference as much as possible, a multilevel residual convolution autoencoder network based noise learning method (LN-MRSCAE) is proposed in this paper. The LN-MRSCAE consists of a deep convolutional autoencoder network and multilevel residual, in which the learning noise module encodes and decodes the noise components, combining with a residual module to obtain the denoised signal. The multilevel residual structure is designed to address the gradient disappearance in network learning and improve the training speed. In specific, the noise learning replaces the traditional clean signal learning. Instead of learning the characteristics of the original clean signal, just learn the characteristics of the noise signal. The effectiveness of LN-MRSCAE network denoising is verified by classic analog signal and actual bearing vibration signals with different faults. The results show that the LN-MRSCAE model is superior to the latest models, and the noise component of the signal is obviously suppressed.

Automated summary

In this paper, a noise learning method based on multilevel residual convolution autoencoder network (LN-MRSCAE) is proposed to eliminate noise interference in vibration signals of mechanical equipment. The LN-MRSCAE model, consisting of a deep convolutional autoencoder network and multilevel residual structure, encodes and decodes the noise components to obtain denoised signals. Experimental results demonstrate the superiority of LN-MRSCAE model in denoising compared to the latest models and the effective suppression of noise components in the signals.