Research on the Safety and Stability of Scraper Conveyor Drive Based on Radial Basis Function Neural Network

Abstract

In order to finally achieve the purpose of online detection of scraper conveyor jam and chain breakage fault, this paper proposes a method to determine the occurrence of scraper conveyor jam and chain breakage fault by identifying the sudden change characteristics of drive motor current. In this paper, radial basis function neural network is used as the basis for the kernel algorithm calculation of the scraper conveyor drive current characteristic recognition model, and the expansion principle and data fusion method are used to complete the diagnosis and fault tolerance control of the speed sensor, and the diagnosis and fault tolerance of the current sensor are realized by the expansion observer compensation state formula. To verify the feasibility of the method, the current data simulation of the scraper conveyor operation process is combined with the field provided. The simulation results show that the control strategy proposed in this paper can achieve power down diagnosis and fault-tolerant control of speed and current sensors, which is of practical significance for the stable operation of the scraper conveyor.