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A novel approach integrating dimensional analysis and neural networks for the detection of localized faults in roller bearings
Affiliation:1. School of Instrument Science and Opto-Electronic Engineering, Hefei University of Technology, Hefei 230009, China;2. College of Electronic and Information Engineering, Anhui JIANZHU University, Hefei 230601, China;1. National Metrology Institute of Japan, National Institute of Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8563, Japan;2. The Graduate School for the Creation of New Photonics Industries, Hamamatsu, Shizuoka 431-1202, Japan;3. Graduate School of Mechanical Engineering, Pusan National University 2, Busandaehak-ro 63 beon-gil, Geumjeong-gu, Busan 46241, Repubilc of Korea;1. Dept. of Information Technology and Electrical Engineering, University of Naples “Federico II”, via Claudio 21, 80125 Naples, Italy;2. Dept. of Electrical and Information Engineering, University of Cassino and Southern Lazio, via G. Di Biasio, 43, 03043 Cassino, Italy;1. AGH University of Science and Technology, Department of Electronics, Krakow, Poland;2. Silicon Creations, 49 Highway 23 NE, Suwanee, GA 30024, USA;3. Singulus Technologies AG, 63796 Kahl am Main, Germany;4. INESC-MN and IN, 1000-029 Lisbon, Portugal;5. Physics Department, Instituto Superior Tecnico, Universidade de Lisboa, Portugal;6. INL-International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga s/n, 4715-330 Braga, Portugal
Abstract:The detection of the defective/worn out bearing components used in rotating machines is one of the main concerns in various applications. To improve the computational efficiency in the nonlinear dynamic analysis for the rolling contact bearings, a new methodology based on dimensional analysis (DA) theory is proposed in this paper. The developed model is used to predict the vibration responses due to artificially spalled bearing components to quantify the level of structural damages into these components. The use of a back propagation neural network (BPNN) has been made that also predicted responses from the network trained by developed algorithm using the experimental data obtained from the defective bearing components on the developed test rig. A comparison between the responses predicted by proposed DA method and the BPNN showed a fair amount of the agreement between the two approaches and validated the proposed model and proved outstanding tool for identification of spalled/damaged bearing components.
Keywords:Dimensional analysis  Rolling contact bearings  Dynamic modeling  Neural network
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