A Review of Knowledge Graph-based Research Methods for Fault Diagnosis of Special Vehicles

Fault diagnosis of special vehicles is crucial in ensuring their reliability and safety. Traditional fault diagnosis methods mainly include analytical model-based methods, signal-based methods, and knowledge-based methods. In contrast, knowledge-based methods are more suitable for the fault diagnosis of special vehicles due to their advantages in dealing with complex environments. This paper reviews knowledge-based fault diagnosis methods for special vehicles and their applications, and analyzes the challenges faced by traditional methods in practical applications. To overcome these challenges, a new idea of applying knowledge graph technology to the fault diagnosis of special vehicles is proposed. This paper also discusses the key technologies, challenges, and opportunities in knowledge graph construction, and introduces the recommended diagnosis methods based on knowledge graphs and their related challenges. Finally, based on the challenges faced by current research, this paper provides an outlook on future research directions, and points out the potential development trends and research focus areas based on knowledge graph technology in the field of special vehicle fault diagnosis.