Prognostics for drilling process with wavelet packet decomposition
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On-line tool condition monitoring is highly needed in drilling production process. Input current has been employed to monitor the drilling tool wear by many researchers. But few cases can represent the wear status and recognize the breakage simultaneously. The remaining life of tool has not been discussed sufficiently. This paper presents a strategy of on-line tool monitoring system for drilling machine using wavelet packet decomposition of spindle current signature. A moving window technique is used to extract the real drilling parts of data from sampled data sequence. The wavelet packet decomposition is used to extract features from non-stationary current signal. Critical features are selected according to their ability of discriminating the wear progress under Fisher criterion. Logistic regression combined with autoregressive moving average models are used to evaluate the failure possibility and remaining life of the drill bit. Experimental results show good performance of the proposed algorithm.
KeywordsTool wear Wavelet packet decomposition Feature selection Prognostics
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