Automatic identification of otologic drilling faults: a preliminary report

✍ Scribed by Peng Shen; Guodong Feng; Tianyang Cao; Zhiqiang Gao; Xisheng Li

• Publisher: Wiley (Robotic Publications)
• Year: 2009
• Tongue: English
• Weight: 336 KB
• Volume: 5
• Category: Article
• ISSN: 1478-5951
• DOI: 10.1002/rcs.259

✦ Synopsis

Abstract

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. Copyright © 2009 John Wiley & Sons, Ltd.