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Automatic identification of otologic drilling faults: a preliminary report
Peng Shen1, Guodong Feng, Tianyang Cao
1Department of Otolaryngology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Background:
A preliminary study was carried out to identify parameters to characterize drilling faults when using an otologic drill under various operating conditions.
Methods:
An otologic drill was modified by the addition of four sensors. Under consistent conditions, the drill was used to simulate three important types of drilling faults and the captured data were analysed to extract characteristic signals. A multisensor information fusion system was designed to fuse the signals and automatically identify the faults.
Results:
When identifying drilling faults, there was a high degree of repeatability and regularity, with an average recognition rate of >70%.
Conclusions:
This study shows that the variables measured change in a fashion that allows the identification of particular drilling faults, and that it is feasible to use these data to provide rapid feedback for a control system. Further experiments are being undertaken to implement such a system.
