Fault Detection of Mine Mobile Robot Based on Integrated Neural Network and Improved Extreme Learning Machine
As a kind of intelligent equipment with autonomous movement,mine mobile robot is widely used in mining,conveying and loading.However,due to its operation in harsh environments,it is often unable to be repaired and maintained for a long time,resulting in frequent failures and affecting safe and efficient production underground.How to detect the fault of the robot timely and accurately,improve its reliability and production efficiency has become an urgent problem to be solved.A fault detection method of mobile robot in mine is proposed based on integrated neural network and improved extreme learning ma-chine.This method integrates several neural network models,and improves the detection accuracy and efficiency by improving the algorithm of extreme learning machine.Secondly,based on the extreme learning machine,the adaptive weight adjustment strategy is introduced to improve the adaptive ability and accuracy of the algorithm.The proposed method is tested on a mine data set,the results show that the method has excellent performance in the case of low detection differentiation or more abnor-mal data,and is helpful to achieve high precision and high efficiency fault detection.
mine mobile robotfault detectionintegrated neural networkimproved extreme learning machine
NETL
NSTL
万方数据
