Research on the performance degradation assessment method of a mine hoist braking system based on variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description

Abstract

According to the characteristics of the constant deceleration braking system of hoist, a variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description method is proposed in this article to evaluate the performance of the brake system. This method takes the pressure–time curve of safety braking as the characteristic data and extracts the candidate characteristic parameters from it. After the comprehensive evaluation of the characteristic parameters based on correlation, monotonicity, and predictability, the characteristic parameters are selected to evaluate the overall performance of the brake system. The proposed variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description model avoids the situation where the classical Drosophila optimization algorithm is easy to fall into the local optimum. The reliability of the proposed method is verified by the simulation data, and the practicability of the proposed method is verified by the test-bed data. The performance evaluation model of variable step-size fruit fly optimization algorithm–complex Gaussian wavelet support vector data description is constructed, which realizes the quantitative evaluation of the brake system health state and provides technical support for the condition-based maintenance and intelligent maintenance of the hoist brake system.

Neural adaptive braking control for single rope winding hoisting systems with disturbance compensation

“…6 Due to the advantages of negligible impact, high safety and better dynamic performance, the constant deceleration braking is widely used in the hoisting systems. 7 In addition, researchers proposed some advanced controllers to enhance the braking control performance, such as digital parameters self-tuning, 8 BP neural network, 9 fuzzy neural network 10 and genetic neural control 11 and the control parameters of the method can be adaptively adjusted and optimised, which solved the problem of difficult adjustment of control parameters and improved the performance of braking control to a certain extent. However, the above-mentioned controllers adopt standard model-free control methods and ignore the elastic characteristics of wire ropes, which restrict the improvement of braking control performance.…”