基于IBA-SVR的滚动轴承性能退化趋势预测
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1.沈阳建筑大学机械工程学院;2.中国重汽集团汽车研究总院

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TH133.3,TP391

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国家自然科学基金项目(面上项目,重点项目,重大项目)


Prediction of Rolling Bearing Performance Degradation Trend based on IBA-SVR
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1.School of Mechanical Engineering,Shenyang Jianzhu University;2.China National Automobile Research Institute

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    摘要:

    建立准确的滚动轴承性能退化预测模型对于轴承故障分类、寿命预测等后续处理有着至关重要的作用。为了解决轴承性能退化模型预测不准确的问题,提出一种改进的蝙蝠算法(IBA)来提高退化模型预测的准确度。首先将Cat混沌映射应用到种群初始位置,增强种群的遍历性,提高初始解的质量;其次在迭代过程中加入类反正切控制因子,提高算法寻优精度;最后改进位置更新策略,防止陷入局部最优。通过与蝙蝠算法(BA)优化的支持向量回归机(SVR)、粒子群优化算法优化的SVR和灰狼优化算法优化的SVR所得的结果做对比,结果表明:IBA所优化预测模型的均值绝对误差分别下降了70.60%、67.19%、55.56%,均方根误差分别下降了76.64%、76.12%、30.29%,进一步证明了改进后的预测模型的准确性。

    Abstract:

    Establishing an accurate rolling bearing performance degradation prediction model plays a crucial role in subsequent processing such as bearing fault classification and life prediction. In order to solve the problem of inaccurate prediction of bearing performance degradation model, an improved bat algorithm (IBA) is proposed to improve the accuracy of degradation model prediction. Firstly, Cat chaotic mapping is applied to the initial position of the population to enhance the traversability of the population and improve the quality of the initial solution; secondly, an inverse tangent-like control factor is added in the iterative process to improve the algorithm"s accuracy in finding the optimum; finally, the position updating strategy is improved to prevent from falling into the local optimum. By comparing the results with those obtained from the support vector regression machine (SVR) optimized by Bat Algorithm (BA), SVR optimized by Particle Swarm Optimization Algorithm, and SVR optimized by Gray Wolf Optimization Algorithm, the results show that the absolute mean error of the prediction model optimized by the IBA decreases by 70.60%, 67.19%, 55.56%, and the root-mean-square error decreases by 76.64%, 76.12%, and 76.12%, respectively. 76.64%, 76.12%, and 30.29%, respectively, further proving the accuracy of the improved prediction model.

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黄亚州,邵萌,吴昊,等. 基于IBA-SVR的滚动轴承性能退化趋势预测[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-12-05
  • 最后修改日期:2024-06-26
  • 录用日期:2024-07-09
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