基于粒子滤波算法的直流电机电刷状态预测  

Prediction of DC Motor Brush State Based on Particle Filter Algorithm

作　　者：陈玲玉 张智杰[1] 王传清 肖从斌 CHEN Ling-yu;ZHANG Zhi-jie;WANG Chuan-qing;XIAO Cong-bin(Huazhong Institute of Electro-Optic-Wuhan National Laboratory for Optoelectronics,Wuhan 430223,China)

机构地区：[1]华中光电技术研究所—武汉光电国家研究中心,湖北武汉430223

出　　处：《光学与光电技术》2024年第4期49-54,共6页Optics & Optoelectronic Technology

摘　　要：针对直流有刷电机的电刷易磨损问题,提出了一种利用粒子滤波算法预测估计电刷状态的方法。搭建了直流电机的动态模型,通过改变电枢绕组的阻值模拟电刷磨损过程,仿真输出了电刷磨损过程的电流平均值数据。根据电流平均值数据的拟合结果,建立了电刷状态演变的基本模型,引入随机重采样的粒子滤波算法迭代估计出的模型未知参数b的值稳定在真值0.002附近。预估的模型较准确地反映出电刷的磨损过程,能够估计电刷的剩余使用寿命,对电机电刷的维护工作具有参考意义。Aiming at the problem of brush wear of DC brush motor,a method of predicting and estimating brush state using particle filter algorithm is proposed.The dynamic model of DC motor is built,the brush wear process is simulated by changing the resistance value of armature winding,and the average current data of the brush wear process is simulated.According to the fitting results of the average current data,a basic model of brush state evolution is established.The unknown parameter b of the model is iteratively estimated by the particle filter algorithm of random resampling,and the value is stable near the truth value 0.002.The predicted model reflects the wear process of the brush more accurately,and can estimate the remaining service life of the brush,which is of great significance for the maintenance of the motor brush.

关 键 词：直流电机 电刷磨损 粒子滤波 随机重采样 剩余使用寿命 

分 类 号：TM33[电气工程—电机]