摘要
研究利用从机械控制过程中获得的运行参数开发一种齿轮箱监测方法,而非振动与声音的传统测量方法。为了检测齿轮箱状态,采用一种自适应模糊神经推理系统来获取电机电流和控制参数之间的非线性相关性。比较自适应模糊神经推理系统模型产生的预测值和实测值来预测齿轮箱异常状态。试验结果表明,自适应模糊神经推理系统模型能够作为齿轮箱状态监测与故障检测的一种有效工具。
This study force to develop the gear-box monitoring methods using the operating pa-rameters obtained from machine control processesrather than the traditional measurements such asvibration and acoustics. To monitor the gearboxconditions,an adaptive neuro- fuzzy inference sys-tem （ANFIS） is used to capture the nonlinear con-nections between the electrical motor current andcontrol parameters such as load settings and tem-perature. The experimental results show that AN-FIS model is able to serve as an efficient tool forgearbox condition monitoring and fault detection.
出处
《机械与电子》
2015年第2期51-55,共5页
Machinery & Electronics
关键词
状态监测
自适应模糊神经推理系统
齿轮箱故障
故障检测
运行参数
condition monitoring
adaptive neu-ro - fuzzy inference system
gearbox fault
fault de-tection
operating parameters
