基于仿生感知的机床刀具故障诊断系统  

Machine Tool Fault Diagnosis System Based on Bionic Perception

作　　者：刘富[1] 宋阳 刘云[1] 康冰[1] 姜守坤 侯涛[1] LIU Fu;SONG Yang;LIU Yun;KANG Bing;JIANG Shoukun;HOU Tao(College of Communication Engineering,Jilin University,Changchun 130022,China)

机构地区：[1]吉林大学通信工程学院,长春130022

出　　处：《吉林大学学报（信息科学版）》2021年第2期127-135,F0002,共10页Journal of Jilin University（Information Science Edition）

基　　金：国家自然科学基金重点资助项目(51835006)。

摘　　要：现有刀具故障诊断系统具有系统庞大、成本高,精度低等问题,亟需开发一种高精度、低成本的刀具故障诊断系统。为此,提出一种基于仿生应变传感器的数控机床刀具故障诊断系统,该系统首先将精度高、价格便宜的仿生柔性裂纹阵列振动敏感元件封装成刚性的仿生应变传感器,使其适用于采集机床刀具振动信号;然后从采集的刀具振动信号中提取时域和频域特征,并使用支持向量机算法建立刀具故障诊断模型。通过实验对离线故障和实时机床加工环境中的在线故障进行诊断,结果表明,设计的基于仿生应变传感器的刀具故障诊断系统对机加故障诊断的准确率大于88%,在保证故障诊断性能的同时降低了检测成本。这是将灵敏度高、成本低的仿生柔性敏感元件应用于工业故障诊断的一次全新尝试。The existing tool fault diagnosis system has the problems of huge system,high cost,low precision and so on. It is urgent to develop a high precision and low cost tool fault diagnosis system. A tool fault diagnosis system which based on bionic strain sensors for CNC( Computerized Numerical Control) machine tools is proposed. Firstly,the bionic flexible crack array vibration sensitive component which has high-precision and inexpensive encapsulated into a rigid bionic strain sensor,making it suitable for collecting machine tool vibration signals. Then time-frequency features are extracted from the tool vibration signals and tool fault diagnosis model is built by support vector machine algorithm. Through the diagnosis of offline faults and online faults in the realtime machine-processing environment,the results show that the proposed tool fault diagnosis system has an accuracy of greater than 88% in machine-processing fault diagnosis. And the cost of system is reduced. This is a brand-new attempt to apply high-sensitivity and low-cost bionic flexible sensitive component to industrial fault diagnosis.

关 键 词：信息处理技术 刀具故障诊断 仿生柔性裂纹阵列振动敏感元件 支持向量机 评价指标全称 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]