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基于改进PSO优化模糊神经网络的数控机床故障诊断技术研究
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国家自然科学基金资助项目(21366017)


Research on CNC Machine Tool Fault Diagnosis Based on Improved Particle Swarm Optimized Fuzzy Neural Network
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    摘要:

    数控机床故障具有隐蔽性和复杂性的特点,为了快速准确地识别数控机床发生的故障,结合粒子群算法全局搜索能力强、寻优速度快及模糊神经网络容错能力强、自适应性强的特点,提出了将模糊逻辑、RBF神经网络及粒子群算法有机结合的数控机床故障诊断方法。为了改善粒子群算法局部搜索能力,在标准粒子群算法的基础上,改进粒子群的速度更新公式和惯性权重,以此优化模糊神经网络结构参数,从而建立起改进PSO优化模糊神经网络的数控机床主轴伺服系统故障诊断模型。实验和仿真结果表明:与RBF神经网络、标准PSO优化模糊神经网络相比,改进PSO优化模糊神经网络的故障辨识准确性更高、泛化能力更强。

    Abstract:

    As the faults of computer numerical control (CNC) machine tool having the characteristics of concealment and complexity, in order to quickly and accurately identify the faults, a fault diagnosis method for CNC machine tool was presented based on rational combination of the fuzzy logic, RBF neural network and particle swarm optimization (PSO) algorithm, which integrated with properties of strong selfadaptability, fast searching and strong compatibility of fuzzy neural network, and global strong searching ability of PSO algorithm. A modified velocity updating formula and inertia weight for particle swarm algorithm was proposed to improve the local searching ability to optimize the structure parameters of fuzzy neural network on basis of standard particle swarm algorithm. Thus, the fault diagnosis model of CNC machine spindle servo system with improved PSO optimization of fuzzy neural network was established. The experiment and simulation results show that the proposed method has higher fault identification accuracy and stronger generalization ability, as compared with RBF neural network and the standard PSO optimization of fuzzy neural network.

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王春暖,李文卿,吴庆朝.基于改进PSO优化模糊神经网络的数控机床故障诊断技术研究[J].机床与液压,2016,44(3):192-197.
. Research on CNC Machine Tool Fault Diagnosis Based on Improved Particle Swarm Optimized Fuzzy Neural Network[J]. Machine Tool & Hydraulics,2016,44(3):192-197

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  • 在线发布日期: 2016-04-07
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