Artificial neural networks for vibration based inverse parametric identifications: A review |
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| Affiliation: | 1. Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;3. Advanced Shock and Vibration Research Group, Applied Vibration Laboratory, Block R, Faculty of Engineering, University of Malaya, Malaysia;4. School of Design, Engineering and Computing, Bournemouth University, Poole, Dorset, BH12 5BB, UK;1. School of Aerospace, Transport Systems and Manufacturing, Cranfield University, College Road, Bedfordshire MK43 0AL, UK;2. College of Engineering, Mathematics and Physical Systems, University of Exeter, EX4 4SB, UK;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Department of Computer Engineering, Hashtgerd Branch, Islamic Azad University, Alborz, Iran;1. Institute of Industrial Research, Unit 1 St Andrews Court, University of Portsmouth, Hampshire, PO1 2PR, United Kingdom;2. Seagate Technology, Langstone Road, Havant, Hampshire, PO9 1SA, United Kingdom;1. Biomedical Group, Department of Electrical and Computer Engineering, Hakim Sabzeari University, Sabzevar, Iran;2. Medical Science Department, Faculty of Medical Science University of Sabzevar, Sabzevar, Iran;3. Electrical and Electronics Engineering Department, Shiraz University of Technology, Shiraz, Iran;1. School of Computer and Information, Anqing Normal University, Anqing 246133, China;2. School of Engineering and Computer Science, Victoria University of Wellington, Kelburn 6012, New Zealand;3. USTC-Birmingham Joint Research Institute in Intelligent Computation and its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China |
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| Abstract: | Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes. |
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| Keywords: | Artificial neural networks Inverse problems Parametric identification Vibration |
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