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| 基于声信号HMM的刀具磨损程度分级识别 | |
| 引用本文： | 艾长胜,王宝光,董全成,何光伟. 基于声信号HMM的刀具磨损程度分级识别[J]. 组合机床与自动化加工技术, 2007, 0(7): 26-29 |
| 作者姓名： | 艾长胜 王宝光 董全成 何光伟 |
| 作者单位： | 1. 天津大学,精密测试技术及仪器国家重点实验室,天津,300072;济南大学,机电系,济南,250022 2. 天津大学,精密测试技术及仪器国家重点实验室,天津,300072 3. 济南大学,机电系,济南,250022 |
| 摘 要： | 为有效地实时在线监测刀具的磨损状态，提出了基于声音识别技术的刀具磨损监测方法，进行了基于切削声信号HMM的刀具磨损程度的分级识别，监测系统能够对刀具的五级磨损划分进行准确识别，这为刀具的磨损监测提供了一条切实可行的途径。 |
| 关 键 词： | 切削过程 声信号 刀具磨损分级 |
| 文章编号： | 1001-2265（2007）07-0026-04 |
| 修稿时间： | 2007-01-05 |
Tool-wear Grade Classification Recognition Based on the Sound Signal HMM |
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| AI Chang-sheng,WANG Bao-guang,DONG Quan-cheng,HE Guang-wei. Tool-wear Grade Classification Recognition Based on the Sound Signal HMM[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2007, 0(7): 26-29 | |
| Authors: | AI Chang-sheng WANG Bao-guang DONG Quan-cheng HE Guang-wei |
| Abstract: | In order to monitor tool wear degree real time online effectively, a new tool-wear monitor method is based on the sound recognition technology, the tool-wear classification recognition based on the sound signal HMM is carried out. The monitor system can truly recognize the tool wear degree decomposed five wear levels, which provides an effective approach for tool-wear monitor. |
| Keywords: | HMM |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |