Abstract
In order to ensure the safety and reliable operation of equipment, reduce accidents and economic loss caused by the mechanical fault or failure, prediction and health management (PHM) technology has attracted more and more attention. As the basis and starting point of fault prediction, degradation state recognition is one of the key steps of PHM, which directly affect the reliability of the equipment failure prediction and the selection of corresponding maintenance strategy. As to the degradation state recognition problem of planetary gear set, firstly, select the proper prognosis feature by evaluating various time-frequency features. Secondly, utilize the learning vector quantization neural network to recognize degradation state of planetary gear set. Finally, validate the effectively of presented method with pre-planted chipped fault experiment of planetary gear set. The results show that the proposed algorithm recognizes the multi-level degradation state effectively, and provide a useful reference for subsequent fault prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He ZJ, Chen J, Wang TY, Chu FL (2010) Theories and application of machinery fault diagnostics. Higher Education Press, Beijing
Cheng Z, Niaoqing Hu, Gao J (2011) An approach to detect damage quantitatively for planetary gear sets based on physical models. Adv Sci Lett 4(4/5):1695–1701
Cheng Z, Niaoqing H, Zhang X (2012) Crack level estimation approach for planetary gearbox based on simulation signal and GRA. J Sound Vib 331(26):5853–5863
Sumathi S, Paneerselvam S (2010) Computational intelligence paradigms: theory and applications using MATLAB. CRC Press, New York
Samue Paul D, Pines Darryll J (2005) A review of vibration-based techniques for helicopter transmission diagnostics. J Sound Vib 282(1–2):475–508
Wu B, Saxena A, Patrick R , Vachtsevanos G (2005) Vibration monitoring for fault diagnosis of helicopter planetary gears. In: Proceedings of the 16th IFAC World congress on disc, Prague, Cesko, 4–8 July 2005
Abdulrahman SS, Sharaf-Eldeen YI (2011) A review of gearbox condition monitoring based on vibration analysis techniques diagnostics and prognostics. In: Conference proceedings of the society for experimental mechanics series 8
Yang BS, Han T, An JL (2004) ART-KOHONEN neural network for fault diagnosis of rotating machinery. Mech Syst Signal Process 18(3):645–657
Acknowledgment
Financial support: This investigation was partly supported by National Natural Science Foundation of China under Grant No. 51075391 and No. 51205401, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20114307110017.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Fan, B., Hu, N., Cheng, Z. (2015). Fault Degradation State Recognition for Planetary Gear Set Based on LVQ Neural Network. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-09507-3_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09506-6
Online ISBN: 978-3-319-09507-3
eBook Packages: EngineeringEngineering (R0)