Real-time recognition of ball bearing states for the enhancement of precision, quality, efficiency, safety, and automation of manufacturing

• Published in: International journal of advanced manufacturing technology Vol. 71; no. 5-8; pp. 809 - 816
• Main Authors: Liu, Tien-I, Lee, Junyi, Singh, Palvinder, Liu, George
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2014; Springer Nature B.V
• Subjects: Acceleration; Adaptive systems; Artificial neural networks; Automation; Ball bearings; CAE) and Design; Computer-Aided Engineering (CAD; Diagnostic systems; Engineering; Fuzzy logic; Fuzzy systems; Industrial and Production Engineering; Manufacturing; Mechanical Engineering; Media Management; Original Article; Real time; Recognition; Safety; Search algorithms; Neuro fuzzy systems; Sequential forward search
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-013-5497-5

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.