中国机械工程Issue(18)：2473-2477,5.DOI:10.3969/j.issn.1004-132X.2014.18.011
刀具磨损早期故障智能诊断研究
Early Fault Intelligent Diagnosis of Tool Wear
摘要
Abstract
In view of the difficulties of fault feature extraction from strong background noise in tool wear early fault diagnosis ,a method was proposed based on twice sampling SR and B-spline neural net-work .First ,SR was employed to remove noise in tool wear vibration signals because of its benefits for enhancing the signal-to-noise ratio ,then ,tool wears with the good fault features were identified by B-spline neural network .In order to improve the deficiency of a single parameter be optimized in the tra-ditional SR and achieve the best SR parameters ,an adaptive SR was proposed based on genetic algo-rithm ,which realized multi-parameter synchronous optimization .The experimental results show that this method can realize the weak signal detection and apply to tool fault diagnosis effectively .关键词
随机共振/遗传算法/信噪比/B样条神经网络Key words
stochastic resonance(SR)/genetic algorithm/signal-to-noise ratio/B-spline neural net-work分类
信息技术与安全科学引用本文复制引用
曹伟青,傅攀,李晓晖..刀具磨损早期故障智能诊断研究[J].中国机械工程,2014,(18):2473-2477,5.基金项目
中央高校基本科研业务费专项资金资助项目 （）
