Multiscale dynamic weighted multidimensional joint residual convolutional autoencoder-based rotating machinery signal denoising

Abstract Vibration signals are often severely disturbed by noise during the acquisition process. To effectively eliminate noise interference, a direct noise learning-based multiscale dynamic weighted (M-DW) multidimensional joint (M-DJ) residual convolutional autoencoder model is proposed. Firstly, the combination of a convolutional autoencoder and residual learning is utilized to learn and reconstruct noise. Meanwhile, a M-DW module is added to effectively extract multiscale features and adaptively adjust feature weights according to their denoising contribution. Furthermore, a one-dimensional to two-dimensional (1D–2D) and 2D–1D M-DJ framework is constructed, which effectively integrates adjacent M-DW features using a 2D convolutional neural network and reconstructs noise using a 1D convolutional neural network. The experimental results show that the network effectively filters out noise components in constant-speed, variable-speed and high-noise conditions.