Densely connected semi-Bayesian network for machinery fault diagnosis with non-ideal data

Abstract

Fault diagnosis techniques (FDT) face the challenge of implementing model learning in the presence of limited, imbalanced, or non-ideal data, which is a fundamental and crucial problem that hinders their applications in real industrial scenarios. In this paper, a novel deep neural network (DNN), densely-connected semi-Bayesian network (DSBNet) is proposed to implement feature learning of vibration signals for machinery fault diagnosis in non-ideal data scenarios. Firstly, deep Bayesian learning is embedded into the multi-scale semi-Bayesian block (MSBB) of DSBNet as a local feature extraction and enhancement module. The re-parameterization operations of Bayesian convolutional layers in MSBB perform uncertainty inference on the features by learning the mean and variance of the Gaussian convolution kernel, achieving local expansion and enhancement of network features. Furthermore, convolutional features are integrated into MSBB to generate multi-scale semi-Bayesian features. An adaptive selector based on the multi-class and multi-scale attention mechanism is proposed to enhance effective semi-Bayesian features and suppress redundant features. The proposed methodology facilitates the adaptive end-to-end training of DSBNet, which enables it to match the scale of the current dataset and achieve optimal performance. The effectiveness of DSBNet is verified on two testbeds and multiple in-service computing devices in real industry. The testing results illustrate that DSBNet outperforms other state-of-the-art DNNs, especially in the non-ideal training data scenarios.