Abstract
For large equipment, not only itself, the workpieces are also usually large and expensive. Therefore, online monitoring plays an important role in reducing equipment downtime costs, improving reliability and protecting the production operation. In this paper, a novel condition monitoring method for large equipment based on reference power curve fitting by multi-sensors fusion is proposed. This condition monitoring system can monitor the operation status and can generate alerts once the improper operation occurred. Firstly, the basic principle of the method is described briefly. And then, the fundamentals of establishment of reference power curve are introduced. Next, the data processing model and fault prediction method are discussed in detail. Finally, the feasibility of this method is demonstrated via a case of large machine tool.
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
Preview
Unable to display preview. Download preview PDF.
References
Andrew, K.S.J., Daming, L., Dragan, B.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 7, 1483–1510 (2006)
Zhaoming, S., Qiling, Y.: Equipment condition monitoring and fault diagnosis technology and its application. Chemical Industry Press, Beijing (2003)
Open standards for condition-based maintenance and prognostic systems, http://www.osacbm.org
Gang, N., Bo-Suk, Y.: Intelligent condition monitoring and prognostics system based on data-fusion strategy. Expert Systems with Applications 12, 8831–8840 (2010)
Aiwina, H., Sheng, Z., Andy, C.C.T., Joseph, M.: Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing 3, 724–739 (2009)
Dheeraj, B., David, J., Evans, B., Barrie, J.: A real-time predictive maintenance system for machine systems. International Journal of Machine Tools & Manufacture 44, 759–766 (2004)
Matthew, J.C., Wenbin, W.: An approximate algorithm for prognostic modelling using condition monitoring information. European Journal of Operational Research 1, 90–96 (2011)
Aiwina, H., Andy, C.C.T., Joseph, M., Neil, M., Dragan, B., Andrew, K.S.J.: Intelligent condition-based prediction of machinery reliability. Mechanical Systems and Signal Processing 5, 1600–1614 (2009)
Li, Y.G., Nilkitsaranont, P.: Gas turbine performance prognostic for condition-based maintenance. Applied Energy 10, 2152–2161 (2009)
Wahyu, C., Achmad, W., Bo-Suk, Y.: Combination of probability approach and support vector machine towards machine health prognostics. Probabilistic Engineering Mechanics 2, 165–173 (2011)
Sikorska, J.Z., Hodkiewicz, M., Ma, L.: Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing 5, 1803–1836 (2011)
Wenbin, W.: A two-stage prognosis model in condition based maintenance. European Journal of Operational Research 182, 1177–1187 (2007)
Fei, L., Zongjun, X., Bin, D.: Energy characteristics of machining systems and its application. China Machine Press, Beijing (1995)
Fei, L., Jun, L., Yan, H.: Automatic Collection Method of Machining Progress Information for Large-size Workpieces Based on Reference Power Curve. J. of Mechanical Engineering 10, 111–118 (2009)
Hua, F., Shushing, S., Zhenliang, X., Aiwei, F., Lan, B.: Application of the information fusion method in mine air supplying system based on fuzzy neural network. J. of China Coal Socerty 2, 264–267 (2006)
Kaili, Z., Yaohong, K.: Study on Performance for Pattern Recognition Systems Based on Neural Network Data Fusion. Computer Engineering 17, 103–105 (2006)
Bin, L., Weiguo, Z., Dongfang, N., Wei, Y.: Fault Prediction System of Airplane Steer surface Based on Neural Network Model. Journal of System Simulation 21, 5840–5843 (2008)
Zhenyu, G., Yan, H., Jun, L.: Condition-based maintenance system for large equipment based on running information fusion. Computer Integrated Manufacturing Systems 10, 2094–2100 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, Z., Zheng, J., He, Y., Liu, J. (2011). Large Equipment Condition Monitoring Based on Reference Power Curve Fitting by Multi-sensors Fusion. In: Zheng, D. (eds) Advances in Electrical Engineering and Electrical Machines. Lecture Notes in Electrical Engineering, vol 134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25905-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-25905-0_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25904-3
Online ISBN: 978-3-642-25905-0
eBook Packages: EngineeringEngineering (R0)