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XU Xusong
CAO Yanlong
YANG Jiangxin
Institute of Contemporary Manufacturing Engineering,
Zhejiang University,
Hangzhou 310027, China |
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CONDITION
MONITOR OF DEEP-HOLE DRILLING BASED ON MULTI-SENSOR INFORMATION
FUSION
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 0Cr17Ni4Cu4Nb 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.
Key words:
Information fusion Neural networks Condition monitoring Fault diagnosis |