摘要
为提高加工过程中刀具磨损状态的识别精度,结合改进的变分模态分解算法(modified variational mode decomposition,MVMD)、自适应回溯搜索算法(adaptive backtracking search algorithm,ABSA)及最小二乘支持向量机(least squares-support vector machine,LS-SVM),提出一种刀具磨损快速识别模型.针对传统信号处理方法存在的模态混叠、噪声敏感等问题,采用瞬时频率均值法预先确定最佳分解模态数,引入降噪型变分模态分解算法进行信号分解;为提高优化效率与自适应性,提出一种改进的自适应回溯搜索算法,通过参数自适应选择提高算法的全局与局部搜索能力;基于自适应回溯搜索算法,采用LS-SVM多分类模型实现了刀具磨损状态的识别.实验结果表明,MVMD可以有效降低噪声、剔除虚假信息,同时验证了ABSA算法具有更强的全局探索和局部寻优能力,使得ABSA优化LS-SVM模型具有更高的准确性.
To monitor the tool wear state,an in-process tool wear state recognition system was developed using modified variational mode decomposition(MVMD),adaptive backtracking search algorithm(ABSA)and LS-SVM.To tackle problems of mode overlap and noise sensitivity in traditional signal processing methods,the instantaneous frequency mean judgment method was used to determine the optimal number of decomposed modes and the denoised variational mode decomposition(MVMD)was introduced to decompose the signal.To improve the optimization efficiency and adaptability,an adaptive backtracking search algorithm(ABSA)was proposed,which enhanced the global and local search ability of the algorithm through parameters adaptive selection.Based on ABSA,a multiclass model of LS-SVM was used to recognize tool wear state.The experimental results show that the MVMD can effectively reduce noise and eliminate false information and prove that ABSA has stronger ability of global exploration and local optimization,which makes the LS-SVM model optimized by ABSA get higher accuracy.
作者
蔡力钢
李海波
杨聪彬
刘志峰
赵永胜
CAI Ligang;LI Haibo;YANG Congbin;LIU Zhifeng;ZHAO Yongsheng(College of Mechanical and Electronic Engineering,Beijing University of Technology,Beijing 100124,China)
出处
《北京工业大学学报》
EI
CAS
CSCD
北大核心
2021年第1期10-23,共14页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(51805012)
国家科技重大专项课题资助项目(2018ZX04032002)。
关键词
刀具状态监测
振动信号
变分模态分解
特征优化
回溯搜索算法
最小二乘支持向量机
state monitoring of tool
vibration signal
variational mode decomposition
optimized features
backtracking search algorithm
least squares-support vector machine(LS-SVM)
