KMSA-Net: A Knowledge-Mining-Based Semantic-Aware Network for Cross-Domain Industrial Process Fault Diagnosis

Process fault diagnosis is of great importance to ensure the safe and stable operation of industrial systems. Many existing deep-learning-based process fault diagnosis methods assume that the samples are sufficient and obey the same distribution; however, it is almost impossible to achieve in practical industrial applications due to changing working conditions and the high cost of acquiring fault samples, which leads to a prominent performance degradation. In essence, those methods do not fully exploit the intrinsic and relevant knowledge under different working conditions. To address the above issue, a knowledge-mining-based semantic-aware network (KMSA-Net) is proposed in this article. First, a self-correlation knowledge mining subnet is proposed, where unshared attention mechanism is designed to extract knowledge inherent in each working condition so that the discriminative features can be captured. Second, a cross-correlation knowledge mining subnet is proposed, where we develop a fault relational knowledge graph so as to explicitly constrain the local consistency between the source domain, target domain, and cross-domain. Third, a semantic-aware knowledge transfer subnet is designed to impose a semantic constraint during knowledge transfer by encouraging the output of KMSA-Net to be consistent and distinguishable. These three subnets are jointly trained and then applied for cross-domain industrial process fault diagnosis. Finally, benchmark simulated experiments and real-world application experiments are conducted, and the experimental results validate the effectiveness and superiority of the proposed method.