Enhanced discriminate feature learning deep residual cnn for multi-task rotating machinery fault diagnosis with information fusion

Abstract

Deep learning-based diagnosis methods currently face some challenges and open problems. First, domain knowledge of fault modes and operating conditions are not integrated in most existing approaches, which results in low diagnosis accuracy and training efficiency. Second, existing methods treat all features with indiscriminate attention, which causes unnecessary computation and even false diagnosis results in some cases. Third, multi-task diagnosis becomes more important for health maintenance. To address these challenges, a deep residual convolutional neural network is provided with an enhanced discriminate feature learning capability and information fusion for multi-task bearing fault diagnosis. Domain knowledge is integrated with monitoring data to build the information map. Two attention modules are introduced to enhance the discriminate feature learning ability, and two classifiers are employed for multi-task diagnosis, providing significant improvements in diagnostic accuracy and training efficiency.