基于量子遗传算法的刮板输送机减速器的故障诊断研究

发布时间：2018-03-30 19:45

本文选题：量子遗传算法　切入点：BP神经网络　出处：《煤炭工程》2016年07期

【摘要】：针对刮板输送机减速器故障类型多、诊断准确率低的问题,基于量子遗传算法理论,提出了一种基于量子遗传算法的神经网络故障诊断方法。利用量子遗传算法对神经网络权值、阈值进行优化,加快目标的优化求解。初步研究表明将量子遗传算法与BP神经网络结合可以有效地解决神经网络收敛速度慢,易陷入局部最小等问题,有助于提高刮板输送机减速器的故障诊断精度。
[Abstract]:Aiming at the problem that the reducer of scraper conveyer has many kinds of faults and low diagnostic accuracy, it is based on quantum genetic algorithm (QGA) theory. A neural network fault diagnosis method based on quantum genetic algorithm (QGA) is proposed. The weights and thresholds of neural network are optimized by quantum genetic algorithm (QGA). The preliminary research shows that the combination of quantum genetic algorithm and BP neural network can effectively solve the problems such as slow convergence rate and easy to fall into local minimum. It is helpful to improve the fault diagnosis accuracy of the reducer of scraper conveyor.
【作者单位】：西安科技大学电气与控制工程学院;神华宁煤业集团矿山机械制造维修分公司;
【基金】：国家自然科学基金项目(51277149)
【分类号】：TD528.3

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