Adaptive Semi-supervise Graph Neural Network for Fault Diagnosis of Tunnel Ventilation Systems

Tunnel fans are typical and key fire-fighting electromechanical equipment to ensure the ventilation and safety of tunnel traffic. Effective maintenance of such a group of complex electromechanical equipment servicing in hazard environment is challenging for vulnerable to unexpected failure. However, the widely applied deep learning methods lack the capability that extracting features from sample organization. A novel semisupervised graph neural network (ASGNN) is proposed that is adaptive to fluctuate fault features. First, a clustering method is proposed to develop a knowledge alignment layer for the construction of the graph. Then, the embedded representation of the graph network is introduced to aggregate the information of the whole graph. Finally, an expectation-maximization (EM) algorithm-based learning method is developed to realize the alternate learning of both signal and relationship features. The proposed fault diagnosis solution has been verified with experiments, and the results demonstrated that the proposed method outperformed the state-of-the-art solutions.