Integrating Outlier-Type Prior Knowledge Into Convolutional Neural Networks Based on an Attention Mechanism for Fault Diagnosis

Convolutional neural networks (CNNs) have been widely used in fault diagnosis due to their superiority in feature extraction. Traditional CNNs are a type of closed-box techniques with little interpretability, and their effectiveness is greatly affected when fault mechanisms and modes are extremely complex. To cope with such issue, this article presents a way to integrate outlier-type prior knowledge into CNNs based on an attention mechanism for fault diagnosis. First, outliers of the image-like data obtained by a sliding window processing from the raw data are formally defined as prior knowledge. Then, the defined outlier-type prior knowledge is integrated into any layer of CNNs by a parameter-free attention mechanism. Compared with existing similar methods, the proposal realizes a novel and flexible definition of prior knowledge and achieves deep fusion of prior knowledge and CNNs with low computational cost. The performance of the proposal was evaluated on the Tennessee Eastman process dataset and the real wind turbine blade icing dataset, which indicates that the proposal could not only realize accurate results but also had good model interpretability in terms of achieving high accuracy. The acquisition of outlier-type prior knowledge was discussed and the results demonstrate the effectiveness of the proposed prior knowledge integration method.