An Adaptive Fusion Convolutional Denoising Network and Its Application to the Fault Diagnosis of Shore Bridge Lift Gearbox

Abstract

Traditional fault diagnosis methods are limited in the condition detection of shore bridge lifting gearboxes due to their limited ability to extract signal features and their sensitivity to noise. In order to solve this problem, an adaptive fusion convolutional denoising network (AF-CDN) was proposed in this paper. First, a novel 1D and 2D adaptive fused convolutional neural network structure is built. The fusion of both 1D and 2D convolutional models can effectively improve the feature extraction capability of the network. Then, a gradient updating method based on the Kalman filter mechanism is designed. The effectiveness of the developed method is evaluated by using the benchmark datasets and the actual data collected for the shore bridge lift gearbox. Finally, the effectiveness of the proposed algorithm is proved through the experimental validation in the paper. The main contributions of this paper are described as follows: the proposed AF-CDN can improve the diagnosis accuracy by 1.5–9.1% when compared with the normal CNN methods. The robustness of the diagnostic network can be significantly improved.