WBLG: a method for bearing fault diagnosis based on the fusion of spatial and temporal features

Abstract

Bearing fault diagnosis is a key strategy to ensure the stability of mechanical systems, optimize maintenance plans and improve operational reliability. Vibration signals are complex time series with unique properties. Most of the current methods only consider the spatial characteristics of the signal, but do not take into account its temporal characteristics. In fact, vibration signals contain both spatial and temporal information, offering not only rich temporal dynamic details but also spatial structural insights that reflect fault characteristics. Therefore, in order to fully mine and integrate the spatiotemporal feature information in vibration signals to enhance the accuracy and robustness of intelligent diagnosis, we propose a bearing fault diagnosis method based on the fusion of spatial and temporal features. Firstly, an improved wide kernel deep convolutional neural network method was proposed. By using an adaptive channel module, the model was enabled to focus on the key features of the vibration signal and suppress the interference of irrelevant information. At the same time, the superimposed bidirectional long short-term memory layer and the gated recurrent unit effectively capture the long-term dependencies of the time series in the dataset, covering both past and future signal features. This method can effectively utilize temporal information and combine it with spatial features, significantly improving the accuracy and robustness of bearing diagnosis. To verify the proposed method, ablation and comparative experiments were conducted on two publicly available bearing datasets: the Case Western Reserve University dataset and the Guangdong University of Petrochemical Technology dataset. The experimental results show that the average accuracy rates of fault diagnosis of the WBLG network on the two sets of datasets have reached 99.7% and 96.5% respectively. Compared with the existing models, the maximum improvement rates are 3.73% and 12.4% respectively. It demonstrates its superior classification performance and generalization applicable to bearing fault diagnosis.