IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System: Vibration Analysis of Multi Cracked Rotating Shaft

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System: Vibration Analysis of Multi Cracked Rotating Shaft
View Sample PDF
Author(s): Rajeev Ranjan (Haldia Institute of Technology, India)
Copyright: 2017
Pages: 12
Source title: Fuzzy Systems: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1908-9.ch062

Purchase


Abstract

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.

Related Content

Kamel Mouloudj, Vu Lan Oanh LE, Achouak Bouarar, Ahmed Chemseddine Bouarar, Dachel Martínez Asanza, Mayuri Srivastava. © 2024. 20 pages.
José Eduardo Aleixo, José Luís Reis, Sandrina Francisca Teixeira, Ana Pinto de Lima. © 2024. 52 pages.
Jorge Figueiredo, Isabel Oliveira, Sérgio Silva, Margarida Pocinho, António Cardoso, Manuel Pereira. © 2024. 24 pages.
Fatih Pinarbasi. © 2024. 20 pages.
Stavros Kaperonis. © 2024. 25 pages.
Thomas Rui Mendes, Ana Cristina Antunes. © 2024. 24 pages.
Nuno Geada. © 2024. 12 pages.
Body Bottom