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Real-time recognition of ball bearing states for the enhancement of precision,quality, efficiency,safety, and automation of manufacturing |
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| Authors: | Tien-I Liu Junyi Lee Palvinder Singh George Liu |
| Affiliation: | 1. College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan 2. Department of Mechanical Engineering, California State University, Sacramento, CA, USA 3. DMG Mori Seiki Manufacturing USA, Davis, CA, USA |
| Abstract: | Real-time recognition of ball bearing states can enhance precision, quality, efficiency, safety, and automation of manufacturing. Three features, including peak amplitude of the frequency domain, percent power, and peak RMS, have been extracted from the radial acceleration of ball bearings. The sequential forward search algorithm has been applied to select the best vibration features. Adaptive neuro fuzzy inference systems (ANFIS) have been used. A 2?×?2 ANFIS using the π-shaped built-in membership function can distinguish normal bearing states from defective bearings states with 100 % success rate. Furthermore, a 3?×?5 ANFIS can classify ball bearings into six different states with a success rate of over 95 % for diagnostic purpose. In this manner, real-time recognition of ball bearing states for manufacturing can be performed efficiently and effectively. |
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