Complex Equipment Failure Prediction Technology Based on Virtual Environment

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Abstract:

Complex equipment used in the process, due to changes in running load, vibration, shock and other incidental factors and other confounding factors can lead to changes in equipment and technology status, fault diagnosis of the current state of change brought about by these technologies use is not enough information, relying solely on traditional sensor technology can not test some special devices for signal extraction and data acquisition features. Therefore, the establishment of complex equipment and more operating conditions of knowledge acquisition simulation environment, considering the failure of complex equipment failure caused by the internal laws and mechanisms, to the essence of the phenomenon of failure analysis, combined with the physical prototype test results and raw statistical data, research the actual process of using complex equipment, considering the state of properties, condition monitoring parameters and the integration of traditional knowledge, expertise and reasoning techniques to be applied to physical prototype of a certain conclusion, not only for the early prediction of potential failures, laid the theoretical foundation for forecasting , and fault prediction system for the complex equipment, the development provides important data and technical support to solve the complex equipment maintenance and support tasks needed problem.

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621-624

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December 2011

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Huang Jingde, Key-Technology research of potential fault diagnosis on complex electronic equipment, Computer measurement & control. 2(2010)243-245.

Google Scholar

[2] Cai Jinding, Yan Renwu, Fault diagnosis of power electronic circuit applying ARMA Bispectrum and discrete hidden Markov model, Proceedings of the CSEE, 24 (2010) 54-60.

Google Scholar

[3] Shen Jie, Wang Zhengqun, Zou Jun, Hou Yanping, Approach of recognition based on continuous hidden Markov model, Computer Engraining and Design. 3(2008)707-709.

Google Scholar