Prophase Failure Prediction of the Mechanical Transmission Systems Based on the Biological Evolutionary Algorithm

Xiao Hui  Chen; Lei Xiao; Zhen Xiang Liu; Chuang Liu

doi:10.4028/www.scientific.net/KEM.572.447

Paper Titles

Analysis and Simulation of Deployable Modularized Truss Mechanisms
p.429

Mechanism Design and Modal Analysis of Vertical-Axis Tidal Turbine
p.433

An Ensemble Dynamic Time Domain Averaging Method for Gear Fault Detection without Tachometer
p.439

Diagnosis of Low-Speed Helical Gears Based on Hilbert Demodulation and Empirical Mode Decomposition
p.443

Prophase Failure Prediction of the Mechanical Transmission Systems Based on the Biological Evolutionary Algorithm
p.447

An Energy-Efficiency Wireless Sensing Method for Mechanical Failure Signal
p.451

A Time-Efficiency Multi-Channel MAC Protocol for Wireless Sensor Networks
p.455

Cold Rolling Strip Off-Tracking Online Monitoring Method via Wireless Sensor Network with Self-Powered Nodes
p.459

Experimental Research on the Feature Extraction of Underwater Transient Signal
p.463

HomeKey Engineering MaterialsKey Engineering Materials Vol. 572Prophase Failure Prediction of the Mechanical...

Prophase Failure Prediction of the Mechanical Transmission Systems Based on the Biological Evolutionary Algorithm

Abstract:

There are several types of the mechanical transmission failure, such as gear tooth broken, fatigue, pitting etc. The deterioration pattern of each failure varies according to the different environment. Furthermore, setting up the fault prediction model is quite difficult, especially at the early stage of the fault. In order to predict the prophase failure of the mechanical transmission systems depends on the condition monitoring signal, this paper researched on the biological evolutionary algorithm combined with other artificial intelligence algorithm. As the case study, the typical failure of the gearbox test-bed, for example gear tooth broken or fatigue at the test-bed was monitored by several sensors. An improved support vector machine (SVM) optimized by genetic algorithm (GA) was chosen to predict the prophase failure of the gear, due to its self-adaption and self-learning ability. The prediction results showed that it simulated the failure pattern well on the condition of a few sample data.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 572)

Pages:

447-450

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.572.447

Citation:

Cite this paper

Online since:

September 2013

Authors:

Xiao Hui Chen, Lei Xiao, Zhen Xiang Liu, Chuang Liu

Keywords:

Biological Evolutionary Algorithm, Prediction, Prophase Failure, Support Vector Machine (SVM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] LI Juan, ZHOU Dong-hua, SI Xiao-sheng, CHEN Mao-yin and XU Chun-hong: Review of incipient fault diagnosis methods. Control Theory & Applications. 29-12(2012), pp.1517-1529.

Google Scholar

[2] Randall RB. The application of fault simulation to machine diagnostics and prognostics. Int J Acoust Vib. 14-2 (2009), p.81–89.

Google Scholar

[3] Randall RB: The use of simulation models to generate data corresponding to faults inmachines. In: Proceedings of 2009 8th international conference on reliability. Maintainability and safety. ICRMS(2009), p.934–939.

DOI: 10.1109/icrms.2009.5269971

Google Scholar

[4] Zaigang Chen and Yimin Shao: Dynamic simulation of spur gear with tooth root crack propagating along tooth width and crack depth. Engineering Failure Analysis. 18(2011), p.2149–2164.

DOI: 10.1016/j.engfailanal.2011.07.006

Google Scholar