摘要
为提高数控机床主轴传动系统润滑不良故障的预测能力和预知性维护能力,针对现有故障预测方法的局限性以及主轴零部件耦合、故障并发等特征,提出一种基于故障先兆判定模型和动态置信度匹配的主轴润滑故障预测方法。根据关联程度约简故障先兆表征参数,基于故障历史数据集定义故障先兆状态序列,结合小波分析和概率神经网络技术构建故障先兆判定模型,设计动态置信度匹配算法,进而从可靠性和正确性的角度融合各故障先兆参数状态匹配结果,在线预测故障发生的概率及时间。试验结果表明,该方法能够有效实现主轴传动系统润滑不良故障的预测。
Considering the limitations of the available methods and the features of parts coupling and fault concurrent, a method for spindle lubrication fault prediction based on fault symptom decision model and dynamic confidence matching is proposed, to improve the ability of lubrication fault prediction and predictive maintenance on spindle transmission system of a computer numerical control （CNC） machine tool. Fault symptom parameters of spindle are reduced first according to correlation degree and fault symptom condition sequences of parameters are defined on basis of historical fault data set. Fault symptom decision models are built based on wavelet analysis and probabilistic neural network technologies, which are used to identify real-time condition of fault symptom parameters. Dynamic confidence matching algorithm is designed and multiparameter matching results with fault symptom condition sequences are fused from reliability and accuracy point of view. On this basis, fault probability and occurrence time can be predicted online. Experimental results show that the method can accurately predict spindle lubrication fault.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2012年第17期75-82,共8页
Journal of Mechanical Engineering
基金
国家科技重大专项资助项目(2011ZX04016-071)
关键词
故障预测
主轴润滑
故障先兆判定模型
动态置信度匹配
数控机床
Fault prediction Spindle lubrication Fault symptom decision model Dynamic confidence matching CNC machine tool
