转子系统支承松动故障非线性参数识别
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TH213.3

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(国家重点基础研究发展计划(“九七三”计划)资助项目(2015CB057400);国家自然科学基金资助项目(11672201)


An Improved Genetic Algorithm for Pedestal Looseness Parameter Identification in Rotor-Bearing Systems
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    摘要:

    针对一端支承松动的转子滚动轴承系统,利用遗传算法对松动端的故障非线性参数进行识别。针对传统遗传算法的早熟收敛问题,提出了一种改进的遗传算法。通过适应度函数的构建,将参数识别问题转化为参数优化问题,改进了遗传算法中新一代种群的生成机制。父代种群进行交叉与变异操作后,并不直接产生新一代种群,而是取父代种群与生成的种群中适应度排序靠前的个体组成新一代种群。改进的遗传算法能以较大的变异率进行遗传进化,克制了遗传算法的早熟收敛问题,加快进化速度。用改进遗传算法识别了转子支承松动参数,并研究了变异率和噪声对识别结果的影响。研究表明,改进的方法能有效提高松动参数的识别效率,变异率最高可达0.3,噪声不超过10%时能具有理想的识别精度。基于支承松动转子实验台的实测信号,利用改进遗传算法进行了参数识别,验证了改进算法的有效性。

    Abstract:

    In this paper, a non-linear dynamic equation of a rotor-bearing system with looseness between the pedestal and the casing is established. The non-linear parameters of the pedestal looseness fault are identified by means of the method of parameter optimization of genetic algorithm (GA), based on the displacement response signal in the vertical direction of the shaft end which has looseness. In view of the traditional genetic algorithm having problems of slow evolution and premature convergence, an improved method based on the genetic process of the traditional genetic algorithm is proposed. The method can take a greater mutation rate, restrain the premature convergence problem, and to speed up the evolution. Studies show that the improved method can effectively improve the efficiency of loosening parameters identification. The non-linear parameters of pedestal looseness fault are identified by the improved genetic algorithm, and the influence of mutation rate on recognition results is studied. Finally,the looseness parameters identification based on true measured signal is performed.

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  • 在线发布日期: 2018-07-04
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