Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2012.6360931
Title: A two-stage equipment predictive maintenance framework for high-performance manufacturing systems
Authors: Hu, B.
Pang, C.K. 
Luo, M.
Li, X.
Chan, H.L.
Keywords: Genetic algorithm
manufacturing systems
neural networks
predictive maintenance
Issue Date: 2012
Source: Hu, B.,Pang, C.K.,Luo, M.,Li, X.,Chan, H.L. (2012). A two-stage equipment predictive maintenance framework for high-performance manufacturing systems. Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 : 1343-1348. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIEA.2012.6360931
Abstract: It has been a long interest from researchers to have an effective approach optimizing maintenance scheduling due to the large budgetary item factories spent on equipment maintenance. Since nowadays large scale of machinery log data is already collected and maintained in most manufacturing plants, it is feasible to extract useful information from this database and predict equipment failure utilizing intelligent and statistical techniques. In order to cope with the high complexity raised in predicting equipment failure, a two-stage equipment predictive maintenance framework based on a systematic integration of biological inspired algorithms and statistical analysis considering each advantages and disadvantages has been proposed and developed. Evaluation and development of the genetic algorithm, neural network, and multiple regression forecasting components in this framework for predicting equipment failure is presented. Through the case study on a wafer fabrication plant in a semiconductor company, the feasibility and effectiveness of the proposed system is demonstrated. © 2012 IEEE.
Source Title: Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
URI: http://scholarbank.nus.edu.sg/handle/10635/69114
ISBN: 9781457721175
DOI: 10.1109/ICIEA.2012.6360931
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