Information guided attention network for bearing remaining useful life prediction adaptive to working conditions and fault modes

Bearing remaining useful life (RUL) prediction is a major concern in prognostics and health management for industrial system. Recently, deep learning models have significantly advanced the development of RUL prediction technologies. However, the accuracy of deep learning-based RUL prediction methods is easily affected by variable working conditions and multiple fault modes. In this paper, an information guided attention network (IGAN) is developed for bearing RUL prediction adaptive to working conditions and fault modes. First, the proposed IGAN builds the multiscale convolutional layers, which are multiple dilated convolutions with different dilation rates, to fully learn multiscale representations. This ensures that no degradation information of bearings under different working conditions and different fault modes is missed. Second, a novel plug-and-play attention block called information guided attention mechanism (IGAM) is designed to adaptively highlight the informative convolutional channels informed by working conditions and fault modes. Finally, a temporal attention mechanism is integrated into the IGAN to adaptively emphasize the degradation features at different temporal locations to further enhance the feature representation. Two case studies on a bearing dataset across different working conditions and a bearing dataset under time-varying working conditions are conducted to validate the effectiveness and superiority of the proposed method.