Research and Optimization of Intelligent Diagnosis Algorithm Based on Rope Tension

Abstract For less monitor and poor performance in the intelligence of the existing fault diagnosis system based on wire rope tension, an optimized intelligent diagnosis algorithm is proposed to diagnose the faults. These faults are difficult to monitor in the past, such as blocked cage, over-wind and slipped rope. Selecting the radial basis function (RBF) as the kernel function, two parameters of penalty factor and radial basis kernel parameter in the least squares support vector machine (LSSVM) are further optimized by artificial bee colony (ABC) algorithm. The results show that the LSSVM algorithm does not need a large number of original data, and has no overfitting and generalization ability. The prediction accuracy and the mean square error of the ABC-LSSVM algorithm are improved. It shows better pattern recognition performance, which can be used as a kind of intelligent diagnosis algorithm for the design of the rope tension fault diagnosis system.