Summary of fault diagnosis methods for belt conveyor systems

The conveyor belt and driving device are the main components of the belt conveyor and are the high-risk areas for faults. Taking conveyor belt faults and driving device faults as the starting point, this paper analyzes the mechanisms of conveyor belt deviation, slipping, damage, stacking and scattering, as well as the faults of the driving device roller, idler and reducer. It focuses on the research progress of knowledge-driven and data-driven fault diagnosis methods for belt conveyors. Based on the knowledge processing technology, the knowledge-driven method realizes the unification of symbol processing and numerical processing, the unification of reasoning process and algorithm process. It mainly includes expert system and fault tree analysis. The data-driven method uses machine learning and data mining techniques to analyze and process historical data. It establishes diagnostic models, and achieves fault diagnosis purposes. It mainly includes support vector machines (SVM), comparison method, and diagnosis methods based on sound and vision. This paper analyzes the current challenges and future development trend of belt conveyor fault diagnosis methods. ① The historical fault data and real-time data should be combined to infer equipment health. The early minor faults should be predicted so as to remind the staff to carry out predictive maintenance. ② The correlation between coupling faults of belt conveyors should be revealed. The emerging technologies such as artificial intelligence should be used to study joint diagnosis methods for coupling faults. ③ The multimodal machine learning technology should be utilized to study the mechanism of multimodal information fusion and utilization of belt conveyors. Fault diagnosis methods for multimodal information fusion of belt conveyors needs to develop. ④ The fault knowledge graph and the belt conveyor domain knowledge should be combined to realize the belt conveyor equipment fault tracking and fault early warning. Through the knowledge query, knowledge reasoning and auxiliary decision-making functions, the capability of fault handling and precise mining of potential fault risks of equipment can be improved.