Abstract
Abstract The present research studied fault diagnosis of composite sheets using vibration signal processing and artificial intelligence (AI)-based methods. To this end, vibration signals were collected from sound and faulty composite plates. Using different time-frequency signal analysis and processing methods, a number of features were extracted from these signals and the most effective features containing further information on these composite plates were provided as input to different classification systems. The output of these classification systems reveals the faults in composite plates. The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm. The research results were reflective of the superiority of ANFIS in terms of precision, while this method had the highest process duration with an equal number of iterations. The precision of the proposed improved XCS method was lower than that of ANFIS, but the duration of the process was shorter than the ANFIS method with an equal number of iterations.
Highlights
Demand for materials with high strength and stiffness and low weight has increased in various industries since aboutNumerous studies have been carried out on fault diagnosis in composite plates using experimental analyses
The different types of classification systems used in this research were the support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), k-nearest neighbor (k-NN), artificial neural networks (ANNs), Extended Classifier System (XCS) algorithm, and the proposed improved XCS algorithm
Fault detection of composite sheets using vibratory signal processing and methods based on the artificial intelligence (AI) has been performed in such a way that vibratory signals have been taken from healthy and faulty composite sheets
Summary
Demand for materials with high strength and stiffness and low weight has increased in various industries since aboutNumerous studies have been carried out on fault diagnosis in composite plates using experimental analyses. Song et al [8] introduced a complete methodology based on the Laplace transform for the analysis of free bending vibrations in laminated composite cantilevers with surface cracks. They used Hamilton’s principle of variation and Timoshenko’s beam theory to develop the damage identification technique. Just-Agosto et al [9] used the neural network model in combination with the effects of thermal and vibratory damage identification to develop the damage identification method. They used the enhanced technique to sandwich composites for crack detection. Perera et al [10] applied the genetic algorithm (GA) to the multi-
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
