Abstract
Crack and unbalance are two important effects experienced by a rotor system during its motion. These are a common source of high vibration and undesirable functioning in rotating machinery. The methods of detection of rotor faults have improved from time being such as single fault identification at any given instance, but generally more than one fault is existent in a rotor system simultaneously which has not been discussed so far. In this work, a method is being devised to critically identify unbalance and crack in rotor system using artificial neural networks (ANN) by statistical features. Moreover, a confusion matrix is also obtained by statistical features. Then, by the help confusion matrix, the class of crack and unbalance was decided. Validation and testing of the neural network have been done with simulation data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Braun S (1986) Mechanical signature analysis: theory and application. Academic Press, London
Lei Y, He Z, Zi Y (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst Appl 35(1):1593–1600
Genta G, Bona FD (1989) Unbalance response of rotors: a modal approach with some extensions to damped natural systems. J Sound Vib 140(1):129–153
Goldman P, Muszynska A (1991) Analytical and experimental simulation of loose pedestal dynamic effects on a rotating machine vibrational response. In: Rotating machinery and vehicle dynamics. ASME DE, vol 35, pp 11–17
Chu F, Zhang Z (1998) Bifurcation and chaos in a rub-impact Jeffcott rotor system. J Sound Vib 210(1):1–18
Kim YB, Noah ST (1991) Stability and bifurcation analysis of oscillators with piecewise-linear characteristic: a general approach. Trans ASME J Appl Mech 58(1):545–553
Chu F, Holmes R (1998) Efficient computation on nonlinear responses of a rotating assembly incorporating the squeeze-film damper. Comput Methods Appl Mech Eng 164(1):363–373
Curti G, Raffa FA, Vatta F (1992) Unbalance response of rotor systems by dynamic stiffness method. Mech Res Commun 19(4):351–359
Sekhar AS (2005). Identification of unbalance and crack acting simultaneously in a rotor system: modal expansion versus reduced basis dynamic expansion. J Vib Control 11(9):1125–1145
Kankar PK, Satish CS, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38(1):1876–1886
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, R.B., Singh, S.K. (2019). Detection of Crack and Unbalancing in a Rotor System Using Artificial Neural Network. In: Prasad, A., Gupta, S., Tyagi, R. (eds) Advances in Engineering Design . Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6469-3_56
Download citation
DOI: https://doi.org/10.1007/978-981-13-6469-3_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6468-6
Online ISBN: 978-981-13-6469-3
eBook Packages: EngineeringEngineering (R0)