LM算法BP神经网络的数控机床主轴系统故障诊断
发布时间：2018-10-15 17:36
【摘要】：针对目前数控机床故障复杂、诊断困难的问题,提出基于人工神经网络的故障诊断方法。在研究传统BP神经网络故障诊断模型基础上,引入改进的BP算法-LM算法,建立机床主轴系统LM-BP神经网络故障诊断模型,对机床主轴系统故障进行分析与诊断,再通过Matlab仿真与传统BP神经网络相对比,仿真结果表明:传统BP神经网络存在较难实现快速、准确的故障定位问题,而BP神经网络LM算法作为故障诊断的核心算法收敛速度快、识别准确。该方案设计合理可行,有较好的应用前景,并给出应用了实例。
[Abstract]:A fault diagnosis method based on artificial neural network (Ann) is proposed to solve the problem of complex fault and difficult diagnosis of CNC machine tool. On the basis of studying the traditional BP neural network fault diagnosis model, an improved BP algorithm-LM algorithm is introduced to establish the LM-BP neural network fault diagnosis model of machine tool spindle system, and to analyze and diagnose the machine tool spindle system fault. By comparing Matlab simulation with traditional BP neural network, the simulation results show that the traditional BP neural network is difficult to realize fast and accurate fault location problem, and LM algorithm of BP neural network, as the core algorithm of fault diagnosis, converges quickly. The identification is accurate. The design of this scheme is reasonable and feasible, and it has a good application prospect, and an example is given.
【作者单位】： 四川理工学院自动化与电子信息学院;
【基金】：四川理工学院学科建设项目(2014JC02) 人工智能四川省重点实验室重点项目(2012RZY22) 四川理工学院学科特色培育项目(2013PMG04)
【分类号】：TP183;TG659
[Abstract]:A fault diagnosis method based on artificial neural network (Ann) is proposed to solve the problem of complex fault and difficult diagnosis of CNC machine tool. On the basis of studying the traditional BP neural network fault diagnosis model, an improved BP algorithm-LM algorithm is introduced to establish the LM-BP neural network fault diagnosis model of machine tool spindle system, and to analyze and diagnose the machine tool spindle system fault. By comparing Matlab simulation with traditional BP neural network, the simulation results show that the traditional BP neural network is difficult to realize fast and accurate fault location problem, and LM algorithm of BP neural network, as the core algorithm of fault diagnosis, converges quickly. The identification is accurate. The design of this scheme is reasonable and feasible, and it has a good application prospect, and an example is given.
【作者单位】： 四川理工学院自动化与电子信息学院;
【基金】：四川理工学院学科建设项目(2014JC02) 人工智能四川省重点实验室重点项目(2012RZY22) 四川理工学院学科特色培育项目(2013PMG04)
【分类号】：TP183;TG659
【参考文献】
相关博士学位论文 前1条
1 任锟;高速数控加工的前瞻控制理论及关键技术研究[D];浙江大学;2008年
【共引文献】
相关博士学位论文 前10条
1 沈洪W，
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