Fault Prediction and Health Management for Mechanical Equipment Groups Based on the Internet of Things

Xiao Li Xu; Tao Chen; Yun Bo Zuo; Shao Hong Wang

doi:10.4028/www.scientific.net/AMM.121-126.3925

Paper Titles

Edge Detection Based on Wavelet Transform and Fusion
p.3904

Establishment of Aircraft Ammunition Fault Diagnosis Expert System Knowledge Base Based on FTA
p.3909

A Trustable Approach Based on Fuzzy Inference for P2P Systems
p.3914

Experimental Investigation on Cavitation Noise of Water Jet and its Chaotic Behaviour
p.3919

Fault Prediction and Health Management for Mechanical Equipment Groups Based on the Internet of Things
p.3925

Effects of Temperature and Humidity on the Optical Property of Aluminum Film
p.3930

Freeze Drying Technology Forming Drug Release Bionic Scaffolds with Gradient Structure
p.3935

GE PAC and Image Processing Based on the Corn Automatically Grading System Design
p.3940

Using the Simplified 3D DIC Method to Measure the Deformation of 3D Surface
p.3945

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 121-126Fault Prediction and Health Management for...

Fault Prediction and Health Management for Mechanical Equipment Groups Based on the Internet of Things

Abstract:

Safety operation of mechanical equipment groups is of great importance to production and human resources as well as environment. In order to ensure safe operation and improve intelligent level of early fault prediction for key mechanical equipment groups, this paper proposes fault prediction and health management based on the internet of things with structure of three layers to realize comprehensive perception, reliable transmission and intelligent processing of the fault information from the mechanical equipment groups. Two key technologies like condition monitoring and fault prediction are analyzed and application prospects of fault prediction and health management for mechanical equipment groups based on the internet of things are presented in this paper.

You might also be interested in these eBooks

Frontiers of Manufacturing and Design Science II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 121-126)

Pages:

3925-3929

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.121-126.3925

Citation:

Cite this paper

Online since:

October 2011

Authors:

Xiao Li Xu, Tao Chen, Yun Bo Zuo, Shao Hong Wang

Keywords:

Fault Prediction, Health Management, Internet of Things (IOT), Mechanical Equipment Groups, Prediction Management

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhong Binglin, Huangren. Mechanical Fault Diagnostic Theory (3rd Edition), Beijing: China Machine Press (2007).

Google Scholar

[2] Hess A, Fila L, The joint strike fighter (JSF) PHM concept: potential impact on aging aircraft problems, in Aerospace Conference Proceeding, Piscataway, (2002), pp.3021-3026.

DOI: 10.1109/aero.2002.1036144

Google Scholar

[3] Information on http: / www. gjs2010. org/docs/S4-6_E. Kovacs. pdf 2010-9-24.

Google Scholar

[4] S. Haller, S. Karnouskos, and C. Schroth, The Internet of Things in an enterprise context[C], Future Internet Systems (FIS), LCNS, vol. 5468. Springer, (2008), pp.8-14.

DOI: 10.1007/978-3-642-00985-3_2

Google Scholar

[5] C.Y. Mao and Y.H. Han. Discussion on the Application of Internet of Things in Logistics Production Management[C], 2010 International Conference on E-Business and E-Government, (2010), pp.3901-3903.

Google Scholar

[6] A.K.S. Jardine,D. Lin and D. Banjevic. A Review On Machinery Diagnostics and Prognostics Implementing Condition-based Maintenance. Mechanical Systems and Signal Processing, Vol. 20(2006), pp.1483-1510.

DOI: 10.1016/j.ymssp.2005.09.012

Google Scholar

[7] X. Liang, X.S. Li, L. Zhang and J. S Yu. Survey of Fault Prediction Supporting Condition Based Maintenance, Measurement & Control Technology, Vol. 26(6) (2007), pp.5-8, 14.

Google Scholar

[8] J. Lee, J. Ni, D. Djurdjanovic, H. Qiu and H.T. Liao. Intelligent prognostics tools and e-maintenance. Computers in Industry, 57 (2006), pp.476-489.

DOI: 10.1016/j.compind.2006.02.014

Google Scholar