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Proceedings Paper

Self-adaptive selection and decision optimizing method of failure prediction based on equipment vibration signal
Author(s): Guoxin Wu; Xiaoli Xu; Hongjun Wang
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Paper Abstract

The status monitoring and failure detection for equipment operation have always been important means to protect equipment for its safe and reliable operation. Therefore, establishing of a self-adaptive selection and decision optimizing model based on trend prediction method can self-adaptively select trend prediction method according to actual operating status so as to improve failure prediction accuracy and expand application range of failure prediction. The failure prediction experimental device was established to verify the practical application of optimal objective function in the fault prediction. The self-adaptive selection and decision optimizing method, which realizes the failure prediction for large size rotating equipments base on vibration signal, not only can adapt failure predictions of different rotating equipments, but also can realize the real-time online prediction for rotating equipment status; moreover, it has self-adaptive judgment method for multiple vibrating trend prediction models so that the optimal prediction results has high judgment success rate. Meanwhile, it provides trend prediction method adopting multiple prediction models and provides prediction results conducted by multiple prediction models. Compared with historical actual value, it has higher judgment value of failure early warning.

Paper Details

Date Published: 27 May 2011
PDF: 7 pages
Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973T (27 May 2011); doi: 10.1117/12.891999
Show Author Affiliations
Guoxin Wu, Beijing Information Science & Technology Univ. (China)
Xiaoli Xu, Beijing Information Science & Technology Univ. (China)
Hongjun Wang, Beijing Information Science & Technology Univ. (China)


Published in SPIE Proceedings Vol. 7997:
Fourth International Seminar on Modern Cutting and Measurement Engineering
Jiezhi Xin; Lianqing Zhu; Zhongyu Wang, Editor(s)

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