Artificial neural networks for vibration based inverse parametric identifications: A review

Abstract

Graphical abstract2 Highlights  ANNs-solved vibration based parametric identification studies are reviewed. Factors which affect identification result are discussed. Pros and cons of ANN approaches are mentioned. 1• Fundamentals of ANNs. 2• Dynamic behavior analysis for parametric identifications.• Reason for adopting ANNs to vibrational inverse identifications. 3• Earlier ANN approaches to different vibrational parametric identifications.--Signal pre-processing techniques --Input-output schemes --ANN models 4• Factors that affect ANNs performance for vibrational paramteric identification. 5• Advantages and disadvantages of ANN approaches. 6• Suggestions to potential researchers.• Experimental validation of authors' suggestion based on the literature.3  Suggestions are given to potential researchers based on the discussion. Analysis with experimental results is provided to justify some point of view. AbstractVibration 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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