| CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION |
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| 作者姓名： | XU Xusong CAO Yanlong YANG Jiangxin Institute of Contemporary Manufacturing Engineering Zhejiang University Hangzhou China |
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| 作者单位： | XU Xusong CAO Yanlong YANG Jiangxin Institute of Contemporary Manufacturing Engineering，Zhejiang University，Hangzhou 310027，China |
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| 摘 要： | A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel OCr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal.
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| 关 键 词： | 深孔钻孔 传感器 数据融合 人工神经网络 故障诊断 监控系统 |
CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION |
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| XU Xusong CAO Yanlong YANG Jiangxin Institute of Contemporary Manufacturing Engineering,Zhejiang University,Hangzhou ,China.CONDITION MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION FUSION[J].Chinese Journal of Mechanical Engineering,2006,19(1):140-142. |
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| Authors: | XU Xusong CAO Yanlong YANG Jiangxin |
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| Affiliation: | Institute of Contemporary Manufacturing Engineering, Zhejiang University, Hangzhou 310027, China |
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| Abstract: | A condition monitoring method of deep-hole drilling based on multi-sensor information fusion is discussed. The signal of vibration and cutting force are collected when the condition of deep-hole drilling on stainless steel OCr17Ni4Cu4Nb is normal or abnormal. Four eigenvectors are extracted on time-domain and frequency-domain analysis of the signals. Then the four eigenvectors are combined and sent to neural networks to dispose. The fusion results indicate that multi-sensor information fusion is superior to single-sensor information, and that cutting force signal can reflect the condition of cutting tool better than vibration signal. |
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| Keywords: | Information fusion Neural networks Condition monitoring Fault diagnosis |
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