Journal of Intelligent Mechanics and AutomationOpen Access
Journal of Intelligent Mechanics and Automation[JIMA] is an international peer-reviewed journal that publishes original and high-quality research papers in all related areas. All papers published will be open access to all readers. To build a platform for scientific research and academic exchange for scholars, focusing on the development and research, introducing worldwide research, theory and practice, and promoting international exchanges.
ISSN: N/A (Print)
Frequency: Bimonthly
ISSN: 2771-9960 (Online)
Website: https://doi.org/10.55571/jima
Email: journal@4dpublishinggroup.com / jima@4dpublishinggroup.com
Indexing and Abstracting: CrossRef; ResearchBib; WorldCat; CiteFactor
Email: journal@4dpublishinggroup.com / jima@4dpublishinggroup.com
Indexing and Abstracting: CrossRef; ResearchBib; WorldCat; CiteFactor
Unbalance Localization of a Multi-Disc Rotor by Hybridizing Wavelet Transformation and Neural Network
DOI: https://doi.org/10.55571/jima.2022.04022
Authors: Mohammad Gohari
Affiliation:Faculty of Mechanical Engineering, Arak University of Technology, Arak, Iran
Information: Journal of Intelligent Mechanics and Automation. 2022 Vol.1, No.1, pp 10-20
Authors: Mohammad Gohari
Affiliation:Faculty of Mechanical Engineering, Arak University of Technology, Arak, Iran
Information: Journal of Intelligent Mechanics and Automation. 2022 Vol.1, No.1, pp 10-20
Abstract:Rotating machinery is widely working within the industry, and fault diagnosis and prognosis of them may be a vital issue that can save money continually. Unbalance is a crucial fault in rotary systems, and it is focused by many researchers to develop methods to detect that for correcting before global failure happening within the machine. Hence, the establishment of a procedure that will estimate the unbalance location and its specifics are going to be valued and practical for correcting operations. The recent study exemplifies a model that can detect the unbalance parameters, for example, the location of unbalance mass and value of that based on the hybridizing Wavelet Transformation and artificial neural network (ANN) model. The inputs of the model are wavelet coefficients derived from the bearing acceleration signal. It includes two hidden layers constructed by six neurons within each layer. The parameters estimation accuracy was attained 95%, 97%, and 96% for the disc number, the eccentric radius, and unbalance mass values, correspondingly.
Keywords:Unbalance mass and eccentric radius, Artificial neural network (ANN), Fault detection, Wavelet Trans
Cite This Article:Safari M., Eydi A.M(2022). Unbalance Localization of a Multi-Disc Rotor by Hybridizing Wavelet Transformation and Neural Network. Journal of Intelligent Mechanics and Automation, Vol.1, No.1, pp 10-20
Keywords:Unbalance mass and eccentric radius, Artificial neural network (ANN), Fault detection, Wavelet Trans
Cite This Article:Safari M., Eydi A.M(2022). Unbalance Localization of a Multi-Disc Rotor by Hybridizing Wavelet Transformation and Neural Network. Journal of Intelligent Mechanics and Automation, Vol.1, No.1, pp 10-20
