Abstract
Contrary to time-sampled acceleration signals (TA), angular measurements like instantaneous angular speed (IAS), transmission error (TE), and angular sampled acceleration (AA) represent all potential sources of relevant information in fault detection and diagnosis systems, but also to construct feature vector (FV) to make the methods of classification robust and effective even for different running speed or load conditions. In this work, we propose to use angular measurements and support vector machines (SVM) to detect and diagnose gear faults in normal and nonstationary conditions. For this purpose, features are extracted from angular and angle frequency domains of AA, TE, and IAS. Then, the classification is performed by SVM in order to improve the detection and identification of gear defects.
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
Randall B (2011) Vibration-based condition monitoring; industrial, aerospace and automotive applications. Wiley, New York
Zwingelstein G (1995) Diagnostic des défaillances. Théorie et pratique pour les systèmes industriels, Editions Hermès
Dubuisson B (1990) Diagnostic et reconnaissance des formes. Traité des nouvelles technologies. Editions Hermès, Paris
Vachtsevanos G, Lewis FL, Roemer M, Hess A, Wu B (2006) Intelligent fault diagnosis and prognosis for engineering systems. Wiley, New Jersey
Rémond D (1998) Practical performances of high-speed measurement of gear transmission error or torsional vibrations with optical encoders. Meas Sci Tech 9:347–353
Bonnardot F, El Badaoui M, Randall RB, Danière J, Guillet F (2005) Use of the acceleration of a gearbox in order to perform angular resampling with limited speed fluctuation. Mech Syst Sign Proc 19:766–785
André H, Antoni J, Daher Z, Rémond D (2010) Comparison between angular sampling and angular resampling methods applied on the vibration monitoring of a gear meshing in non stationary conditions, (ed). In: Proceedings of the 2010 international conference on noise and vibration engineering, Leuven, Belgium, 20–22 Sept 2010
Renaudin L, Bonnardot F, Musy O, Doray JB, Rémond D (2010) Natural roller bearing fault detection by angular measurement of true instantaneous angular speed. Mech Syst Sign Proc 24:998–2011
Li Y, Gu F, Harris G, Ball A, Bennett N, Travis K (2005) The measurement of instantaneous angular speed. Mech Syst Sign Proc 19:786–805
Fedala S, Rémond D, Zegadi R, Felkaoui A (2013) Contribution of angular measurements in the diagnosis of gear faults by artificial neural networks. In: Surveillance 7, Chartre, France, 29–30 Oct 2013
Vapnik VN (1998) The statistical learning theory. Springer, Berlin
Widodo A, Yang B (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Sign Proc 21:2560–2574
Samanta B (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Sign Proc 18:625–644
Bordoloi DJ, Tiwari R (2014) Optimum multi-fault classification of gears with integration of evolutionary and SVM algorithms. Mech Mach Theory 73:49–60
Bordoloi DJ, Tiwari R (2014) Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data. Measurement 55:1–14
Yuan Sheng-Fa, Chua Fu-Lei (2007) Fault diagnostics based on particle swarm optimization and support vector machines. Mech Syst Sign Proc 21:1787–1798
Li Ning, Zhou Rui, Qinghua Hu, Liu Xiaohang (2012) Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine. Mech Syst Sign Proc 28:608–621
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based classification. IEEE Trans Neural Netw 10:1055–1064
Weston J, Watkins C (1998) Multi-class support vector machines. Technical Report CSD-TR-98-04, Royal Holloway, University of London, Department of Computer Science
Hsu CW, Lin CJ (2002) A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Netw. 13:415–425
Acknowledgments
This work was achieved at the laboratories LaMCoS (INSA-Lyon, France) and LMPA (IOMP, Sétif 1 University, Algeria). The authors would like to thank the French and Algerian Ministries of Higher Education and Scientific Research for their financial and technical support in the framework of program PROFAS 2011–2012.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Fedala, S., Rémond, D., Zegadi, R., Felkaoui, A. (2016). Gear Fault Diagnosis Based on Angular Measurements and Support Vector Machines in Normal and Nonstationary Conditions. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-20463-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20462-8
Online ISBN: 978-3-319-20463-5
eBook Packages: EngineeringEngineering (R0)