基于PCA—SVDD方法的钻头异常钻进识别

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Abstract In order to detect bit abnormal drilling condition quickly and monitor the cluster wells timely, establish bit abnormal drilling recognition model based on PCA—SVDD, according to the characteristics of normalized amplitude frequency of bit vibration signals. Extract the normalized frequency characteristics, which was irrelevant to real amplitude value, of each frame of the bit normal drilling signal, which can adapt to every different situations. Taking the feature vector of signals in normal working conditions whose dimension was reduced by PCA as training samples, a PCA—SVDD bit abnormal drilling diagnostic model was established .The result of real experiment data showed that this method can detect abnormal condition effectively.

Key words： drill bit abnormal drilling condition feature extraction PCA SVDD

Received: 19 September 2014 Published: 15 July 2015

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2015/V34/I13/158

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