摘要
液压马达的故障具有非线性、复杂多样以及信号噪声多的特点,基于这些特点论文针对液压马达的故障诊断方法,提出了将T-S模糊推理模型与神经网络相结合的机器学习的研究。论文细致地阐述了T-S模糊推理数学模型的推理过程以及神经网络模型的工作原理。通过仿真实验验证,该方法用于液压马达的故障诊断是有效的。
Faults of hydraulic motor are characterized by nonlinearity,complexity and multi-noise. Based on these characteristics,this paper proposes a machine learning method which combines T-S fuzzy inference model with neural network for fault diagnosis of hydraulic motor. The reasoning process of T-S fuzzy reasoning mathematical model and the working principle of neural network model are elaborated in detail. The simulation results show that the method is effective for the fault diagnosis of hydraulic motor.
作者
高谦
肖维
GAO Qian;XIAO Wei(City University of Hong Kong,Hong Kong 999077)
出处
《计算机与数字工程》
2020年第12期3027-3030,共4页
Computer & Digital Engineering
