Title :
Drill condition monitoring using ART-1
Author :
Ly, Sidney ; Choi, Jai J.
Author_Institution :
Boeing Commercial Airplanes, Seattle, WA, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A neural network is applied for the detection/identification of worn cutting tools on turning center. The vibration signal collected from accelerometer is first transformed into a time-frequency spectrogram. The spectrogram is then normalized based on either a statistical thresholding method or a stack representation of the spectrogram. A set of processed binary input image is then clustered adaptively using ART-1 neural network
Keywords :
ART neural nets; machine tools; machining; monitoring; pattern recognition; spectroscopy; statistical analysis; vibrations; ART-1 neural network; accelerometer; binary input image; clustering; drill condition monitoring; statistical thresholding; time-frequency spectrogram; turning center; vibration signal; worn cutting tools detection; Accelerometers; Adaptive systems; Airplanes; Computer networks; Condition monitoring; Cutting tools; Neural networks; Spectrogram; Switches; Time frequency analysis;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374360
