RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network

TL;DR

This paper introduces RRCNN, a deep learning-based signal decomposition method that improves non-stationary signal analysis by reducing mode mixing, boundary effects, and noise sensitivity, outperforming traditional techniques.

Contribution

The paper presents a novel RRCNN model that directly uses deep learning for non-stationary signal decomposition, addressing limitations of existing methods like EMD.

Findings

Better handling of boundary effects and mode mixing.

Enhanced robustness to noise.

Decomposed components exhibit improved orthogonality.

Abstract

The decomposition of non-stationary signals is an important and challenging task in the field of signal time-frequency analysis. In the recent two decades, many signal decomposition methods led by the empirical mode decomposition, which was pioneered by Huang et al. in 1998, have been proposed by different research groups. However, they still have some limitations. For example, they are generally prone to boundary and mode mixing effects and are not very robust to noise. Inspired by the successful applications of deep learning in fields like image processing and natural language processing, and given the lack in the literature of works in which deep learning techniques are used directly to decompose non-stationary signals into simple oscillatory components, we use the convolutional neural network, residual structure and nonlinear activation function to compute in an innovative way the local average of the signal, and study a new non-stationary signal decomposition method under the framework of deep learning. We discuss the training process of the proposed model and study the convergence analysis of the learning algorithm. In the experiments, we evaluate the performance of the proposed model from two points of view: the calculation of the local average and the signal decomposition. Furthermore, we study the mode mixing, noise interference, and orthogonality properties of the decomposed components produced by the proposed method. All results show that the proposed model allows for better handling boundary effect, mode mixing effect, robustness, and the orthogonality of the decomposed components than existing methods.