Knowledge features enhanced intelligent fault detection with progressive adaptive sparse attention learning for high-power diesel engine

Abstract

High-power diesel engines are core power equipment in some key fields, and fault diagnosis is of great significance for improving their long-term operational reliability and safety. The lack of sufficient fault samples, known as few-shot conditions, has resulted in the performance degradation of most current intelligent diagnostic methods under complex and variable working conditions. Moreover, the end-to-end deep learning diagnostic models based on implicit layer features do not have clear physical meanings, making it difficult for intelligent diagnostic models to be recognized by equipment managers in key fields. Mechanism-based fusion methods can effectively alleviate the above problems by incorporating fault-sensitive features with clear physical meanings. However, the lack of sufficient knowledge-related features that can characterize fault information is a challenge facing current mechanism fusion methods. In this work, we propose a progressive adaptive sparse attention mechanism-guided knowledge feature incremental mining method. Fault-sensitive segments are obtained based on attention-supervised learning with an improved activation function. Both knowledge-related features and hidden layer features from these segments are incorporated into the feature library. A differential constraint is designed between the distribution of knowledge features and hidden layer features, encouraging the model to focus on learning new knowledge-related features in the subsequent iteration process. Finally, a feature set containing a large amount of knowledge features is constructed to enhance fault diagnosis. Testing on a V12 diesel engine test bench under the conditions of rich and few-shot data sets, shows that the proposed method significantly enhances the fault recognition performance of diesel engines under complex variable working conditions and few-shot data set conditions.