Research on Fault Diagnosis and Optimization of Crusher Based on Atom Search Algorithm-BP Neural Network

• Published in: Chinese Control and Decision Conference pp. 671 - 676
• Main Authors: MA, Liancheng, ZHANG, Yong, SUN, Pu, CAO, Fang, LIU, Yuhang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2020
• Subjects: Atom search algorithm; BP neural network; Crusher; Fault diagnosis; Heuristic algorithms; Neural networks; Optimization; Rotors; Training
• ISSN: 1948-9447
• DOI: 10.1109/CCDC49329.2020.9164583

Crusher is an important production equipment for the crushing production of metal minerals. The crusher plays an important role in the production of the mine, which not only directly affects the entire production line, but also causes major economic losses and even accidents caused by machine damage. To ensure the safe operation of the equipment, reduce equipment maintenance costs and increase equipment utilization, this paper proposed an ASO-BP neural network optimization model. By improving the BP neural network model with atom search algorithm, through the analysis of the six failure tests of the crusher, the proposed ASO-BP neural network can improve the accuracy of crusher fault diagnosis. By improving the BP neural network, the speed and accuracy of crusher fault diagnosis are improved, which is of great significance for the safe operation and production management of the crusher.