摘要
数控机床作为一种现代化制造的核心设备,其发生故障不仅会对工作人员的自身安全造成威胁,而且还会给企业造成巨大的经济损失,仅通过工作人员的维护是很难实现的。由于数控机床故障诊断技术在我国起步较晚,功能与精度水平与发达国家存在着较大的差距,提高国产数控机床的诊断技术迫在眉睫。本文作者在系统总结国内外研究成果的基础上,结合(BAM)神经网络修正模型、非线性动态系统、神经网络与"在停机时冷却液压力的瞬态响应特征"、Case Based Reasoning、蚁群算法、集成KPCA与PSO-RBF等方法的数控机床故障诊断技术7个方面分析了数控机床故障诊断技术理论与研究进展。
Computer numerical control （CNC） machine tool as a core equipment of modem manufacturing, its fault will not only pose a threat to the safety of the staff, but also cause great economic losses to the enterprise. Therefore, it is difficult to implement only by the maintenance of staff. Because of the fault .diagnosis technology of CNC machine tools is backward in China, the function and accuracy level are far away from that of the developed countries. Hence, it is urgent to improve the diagnosis technology of domestic CNC machine tools. Based on the systematic summary of the research results at home and abroad, the theory and research progress of fault diagnosis technology of CNC machine tools are analyzed from seven aspects, which combined of Bidirectional Associate Memory （BAM） neural network compensation model, nonlinear dynamic system, neural network and ＂Transient response characteristics of coolant pressure during shutdown＂, and Case Based Reasoning （CBR）, Ant Colony Optimization （ACO）, integrated Kernel Principal Component Analysis （KPCA） and Particle Swarm Optimization algorithm-Radical Basis Function （PSO-RBF）, and etc.
出处
《机床与液压》
北大核心
2018年第3期161-164,共4页
Machine Tool & Hydraulics
基金
江苏高校品牌专业建设工程资助项目(PPZY2015A086)
关键词
数控机床
故障诊断
现代化制造
研究进展
CNC Machine tool
Fault diagnosis
Modern manufacturing
Research progress
