A novel dense residual network based on Adam-S optimizer for fault diagnosis of bearings under different working conditions

Abstract

In recent years, residual network has been widely used in the field of intelligent diagnosis because of its powerful function. This paper proposes a novel dense residual network (DRNet), which combines the advantages of dense connections and residual learning to prevent gradient disappearance and network degradation caused by network deepening for efficient fault diagnosis of rolling bearings. First, each sub-block in the dense network is deeply processed so that it has better nonlinear expressive ability to extract deep fault features. Then, the residual learning is embedded in each sub-block of the dense network, so that each sub-block processed by deepening will not show the phenomenon of network degradation. Finally, an Adam-Subtracted momentum (Adam-S) optimization algorithm is proposed, which adds the first-order momentum and the second-order momentum of the previous gradient into the expression of the second-order momentum of the current gradient, which enhances the connection between the parameters in the two adjacent gradients in the Adam algorithm. It makes the algorithm more reliable and the gradient prediction more accurate. Without adding additional parameters, the training stability of the algorithm in complex environments is further improved. Experiments on two kinds of data sets under different working conditions are carried out for many times, and comparison with Random Forest, Support Vector Machine, Dense Network, Residual Network, AlexNet and DRNet-Adam proves the effectiveness and feasibility of the proposed model and optimization algorithm.

A multi-layer feature fusion fault diagnosis method for train bearings under noise and variable load working conditions

“…To diagnose bearing faults under variable radial force load and torque load conditions, Su et al [36] conducted hierarchical diagnosis of bearing faults through multiple output layers. Han et al [37] proposed a dense residual network to extract the nonlinear features of bearing signals. Zhang et al [38] fused CNNs with domain adaptive algorithms to achieve fault classification under variable working conditions.…”