摘要
介绍了刀具磨损状态监测中刀具状态信号采集、信号处理和信号识别三个步骤的应用研究情况,详细介绍了人工神经网络、模糊推理、贝叶斯网络-隐马尔科夫模型和支持向量机等智能算法在刀具磨损状态信号识别方面的应用。还从提高诊断系统应用的鲁棒性和实用性、提高信号采集和信号处理的智能化程度、利用工业物联网技术拓展刀具磨损状态智能识别技术的应用领域三个方面对刀具磨损状态智能监测技术的发展提出了展望。
This paper introduced the application research on three-steps,i.e.tool condition signal acquisition,signal processing and signal recognition in the tool wear condition monitoring,and introduced in detail the application of intelligent algorithms like artificial neural network,fuzzy inference,bayesian network-hidden Markov model and support vector machine in the tool wear status signal recognition.The prospect of the development of intelligent tool wear monitoring technology was also proposed from three aspects:improving the robustness and practicability of diagnostic system application,improving the intelligence degree of signal acquisition and signal processing,and expanding the application field of intelligent tool wear identification technology using industrial Internet of Things technology.
作者
田佩彬
王宝金
沈锦桃
TIAN Pei-bin;WANG Bao-jin;SHEN Jin-tao(College of Materials Science and Engineering,Nanjing Forestry University,Nanjing Jiangsu 210037,China;Zhenjiang Zhongfuma Machinery Co.,Ltd.,Zhenjiang Jiangsu 212127,China)
出处
《林业机械与木工设备》
2021年第12期4-8,20,共6页
Forestry Machinery & Woodworking Equipment
基金
江苏省高校优势学科建设工程资助项目(PAPD)
江苏省企业研究生工作站研究课题项目。
关键词
刀具磨损
信号识别
模式识别
智能监测
tool wear
signal recognition
pattern recognition
intelligent monitoring
