Automatic identification of otological drilling faults: an intelligent recognition algorithm

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

• Publisher: Wiley (Robotic Publications)
• Year: 2010
• Tongue: English
• Weight: 317 KB
• Volume: 6
• Category: Article
• ISSN: 1478-5951
• DOI: 10.1002/rcs.312

✦ Synopsis

Abstract

Background

This article presents an intelligent recognition algorithm that can recognize milling states of the otological drill by fusing multi‐sensor information.

Methods

An otological drill was modified by the addition of sensors. The algorithm was designed according to features of the milling process and is composed of a characteristic curve, an adaptive filter and a rule base. The characteristic curve can weaken the impact of the unstable normal milling process and reserve the features of drilling faults. The adaptive filter is capable of suppressing interference in the characteristic curve by fusing multi‐sensor information. The rule base can identify drilling faults through the filtering result data.

Results

The experiments were repeated on fresh porcine scapulas, including normal milling and two drilling faults. The algorithm has high rates of identification.

Conclusions

This study shows that the intelligent recognition algorithm can identify drilling faults under interference conditions. Copyright © 2010 John Wiley & Sons, Ltd.