title: Automatic identification of otological drilling faults: an intelligent recognition algorithm.
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.
authors.affiliation: School of Information Engineering, University of Science and Technology Beijing, Beijing, People's Republic of China.
authors.forename: Tianyang
authors.initials: T
authors.lastname: Cao
authors.forename: Xisheng
authors.initials: X
authors.lastname: Li
authors.forename: Zhiqiang
authors.initials: Z
authors.lastname: Gao
authors.forename: Guodong
authors.initials: G
authors.lastname: Feng
authors.forename: Peng
authors.initials: P
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000818:Animals; D007360:Intelligence; D012540:Scapula / Q000601:surgery*; D012984:Software; D013552:Swine