北京航空航天大学学报 ›› 2018, Vol. 44 ›› Issue (7): 1514-1519.doi: 10.13700/j.bh.1001-5965.2017.0546

• 论文 • 上一篇    下一篇

BLDC电机温度退化多段Wiener过程建模

袁庆洋, 叶建华, 李晓钢   

  1. 北京航空航天大学 可靠性与系统工程学院, 北京 100083
  • 收稿日期:2017-08-31 出版日期:2018-07-20 发布日期:2018-07-25
  • 通讯作者: 叶建华.E-mail:yjhuan@buaa.edu.cn E-mail:yjhuan@buaa.edu.cn
  • 作者简介:袁庆洋 男,硕士研究生。主要研究方向:产品环境工程;叶建华 男,硕士,工程师。主要研究方向:可靠性与环境试验技术;李晓钢 男,博士,教授,硕士生导师。主要研究方向:可靠性试验技术、产品环境工程、加速试验。

Multistage temperature degradation modeling for BLDC motor based on Wiener process

YUAN Qingyang, YE Jianhua, LI Xiaogang   

  1. School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2017-08-31 Online:2018-07-20 Published:2018-07-25

摘要: 无刷直流(BLDC)电机应用广泛,其温度退化过程呈现多段性,需建立多段退化模型,而模型参数较多时,参数估计过程对初始值敏感且易陷入局部最优。首先,针对电机的加速退化数据进行研究,采用正态加权平均(Gauss滤波)的方式滤波,改进实际数据在模型参数的估计中的应用。然后,引入转换函数对Wiener模型改进,建立多段Wiener模型。其次,以极大化似然函数进行参数估计,计算时采用改进粒子群优化(PSO)算法得到估计值,对比非线性模型的残差的正态性,同时分析各模型理论寿命分布及实际该批次失效分布,确定多段模型合理性;得到的模型结果说明电机在退化过程中发生了退化机理的改变,且变换速度快。最后,以非线性模型不同时刻的寿命分布给出该应力下电机的寿命预测,这对电机的预测与健康管理(PHM)有重要意义。

关键词: 无刷直流(BLDC)电机, 退化建模, 多段Wiener过程, 单纯形自优化粒子群优化(PSO)算法, 预测与健康管理(PHM)

Abstract: Brushless DC(BLDC) motor is widely used and its temperature degradation process is multistage. It is necessary to establish a multistage degradation model. When the model has several parameters, the parameter estimation process is sensitive to the initial value and easy to end up with a local optimization. This study was based on accelerated degradation data of motors. The normal weighted average filter (Gauss filter) was used to improve the results of estimation for the model parameters. A multistage Wiener model was established by using the transition function to modify linear model. Then, to maximize likelihood function for parameter estimation, the numerical optimization method, improved particle swarm optimization (PSO), was used for cycle calculation. The rationality of multistage model is verified by comparison of the normality of residual with widely used nonlinear Wiener model, and by analysis of theoretical life distribution of models and actual failure distribution of this batch. The modeling results show that the degradation mechanism changes at high speed during the degradation of the motor. Finally, prediction for motor life under this stress was gained by life distribution in different moments of time calculated by nonlinear model, which is important for the prognostics and health management (PHM) of motors.

Key words: brushless DC(BLDC) motor, degradation modeling, multistage Wiener process, particle swarm optimization (PSO) algorithm with simplex method, prognostics and health management (PHM)

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