The Experts below are selected from a list of 307095 Experts worldwide ranked by ideXlab platform
Ming J. Zuo - One of the best experts on this subject based on the ideXlab platform.
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Health Condition Prediction of Gears Using a Recurrent Neural Network Approach
IEEE Transactions on Reliability, 2010Co-Authors: Zhigang Tian, Ming J. ZuoAbstract:The development of accurate Health Condition prediction approaches has been a key research topic in Condition based maintenance (CBM) in recent years. However, current Health Condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. Neural network based methods have been considered to be a very promising category of methods for equipment Health Condition prediction. In this paper, we propose a neural network prediction model called extended recurrent neural network (ERNN). An ERNN based approach is developed for Health Condition prediction of gearboxes based on the vibration data collected from a gearbox experimental system. The results demonstrate the capability of the ERNN based approach for producing satisfactory Health Condition prediction results. A comparative study based on the gearbox experiment data further establishes ERNN as an effective recurrent neural network model for equipment Health Condition prediction.
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Health Condition prognostics of gears using a recurrent neural network approach
2009 Annual Reliability and Maintainability Symposium, 2009Co-Authors: Zhigang Tian, Ming J. ZuoAbstract:The development of accurate Health Condition prediction approaches has been a key research topic in Condition Based Maintenance (CBM) in recent years. However, current Health Condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. In this work, we develop a recurrent neural network approach for equipment Health Condition prediction. The effectiveness of the approach is illustrated using data collected from a lab gearbox experimental system.
You Jua - One of the best experts on this subject based on the ideXlab platform.
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STUDY ON Health Condition OF STAFF IN CERTAIN UNIVERSITY
Modern Preventive Medicine, 2008Co-Authors: You JuaAbstract:[Objective]To investigate the Health status of the staff in Liaodong university and to explore the main reasons influencing teachers’ Health and prevention measures.[Methods]A questionnaire survey and Health check were conducted among staff in Liaodong university,who were selected by the stratified sampling.[Results]21.85% staff were under the sub-Health Condition. The occurring rate of sub-Health status in teachers was higher than others and had a statistical significance(P﹤0.05). The occurring rate of sub-Health status in sex was not significant. Risk factors were determined in the end,including social-psychological factor,unbalanced diet,deficiency of exercise and so on. The detection rate of signs was 33.66%,the signs in liver and gallbladder was 91.23% among all signs.[Conclusion]The Health Condition of staff has great problems. Society should give more love and care to the staff. Intervention measures can raise Health levels of staff.
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INVESTIGATION OF THE SUB-Health Condition IN COLLEGE STUDENTS
Modern Preventive Medicine, 2007Co-Authors: You JuaAbstract:[Objective]To investigate the Health Condition of college students,and develop the measures of eliminating the sub-Health Condition and promote their physical and mental Health.[Method]Cluster and stratified sampling survey was adopted from 310 students in medicine college.[Result]81.9%students were under sub-Health Condition with insomnia,poor appetite,tiredness,depression and anxiety.[Conclusion]The main reasons for the sub-Health Condition are under great pressure of study and job,staying up,playing computer games and having bad habits.It is important to get rid of bad habits to prevent sub-Health and improve their Health.
Zhigang Tian - One of the best experts on this subject based on the ideXlab platform.
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Health Condition Prediction of Gears Using a Recurrent Neural Network Approach
IEEE Transactions on Reliability, 2010Co-Authors: Zhigang Tian, Ming J. ZuoAbstract:The development of accurate Health Condition prediction approaches has been a key research topic in Condition based maintenance (CBM) in recent years. However, current Health Condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. Neural network based methods have been considered to be a very promising category of methods for equipment Health Condition prediction. In this paper, we propose a neural network prediction model called extended recurrent neural network (ERNN). An ERNN based approach is developed for Health Condition prediction of gearboxes based on the vibration data collected from a gearbox experimental system. The results demonstrate the capability of the ERNN based approach for producing satisfactory Health Condition prediction results. A comparative study based on the gearbox experiment data further establishes ERNN as an effective recurrent neural network model for equipment Health Condition prediction.
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Health Condition prognostics of gears using a recurrent neural network approach
2009 Annual Reliability and Maintainability Symposium, 2009Co-Authors: Zhigang Tian, Ming J. ZuoAbstract:The development of accurate Health Condition prediction approaches has been a key research topic in Condition Based Maintenance (CBM) in recent years. However, current Health Condition prediction approaches are not accurate enough, which has become the bottleneck for achieving the full power of CBM. In this work, we develop a recurrent neural network approach for equipment Health Condition prediction. The effectiveness of the approach is illustrated using data collected from a lab gearbox experimental system.
Yu Liang Dong - One of the best experts on this subject based on the ideXlab platform.
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Dynamic evaluation of wind turbine Health Condition based on Gaussian mixture model and evidential reasoning
Journal of Renewable and Sustainable Energy, 2013Co-Authors: Yu Liang Dong, Fang FangAbstract:Condition-based maintenance is an effective way to reduce operation and maintenance cost of wind turbine. Highly complex and non-stationary operational Conditions of wind turbine pose a challenge to conventional Condition monitoring technique. Thus, a systematic multi-parameter Health Condition evaluation framework that considers the dynamic operational Conditions is proposed. After characteristic parameter selection and Gaussian mixture model based multi-regime modeling, evidential reasoning is developed to evaluate the Health Condition of wind turbine. The proposed approach shows good Health Condition evaluation performance not only on the parameter level but also on the component and system level. Case studies indicate the effectiveness and potential applications of the proposed method for the wind turbine Health Condition evaluation.
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Dynamic Evaluation of Wind Turbine Health Condition Based on Multi-Source Information Fusion
Advanced Materials Research, 2012Co-Authors: Yu Liang Dong, Cheng Cheng Wang, Qi Yun PanAbstract:Due to highly complex and non-stationary operation Condition, a dynamic wind turbine Health Condition evaluation approach based on multi-source information fusion is recommended. The uncertainty and incompletion of evaluation factors are fully considered, both qualitative and quantitative evidences are integrated. A case study is provided to illustrate the implementation process of the dynamic evaluation approach for wind turbine Health Condition. It is shown that the proposed approach offers a flexible and effective way of wind turbine Health Condition evaluation and the result can be used as a support for Condition based maintenance decision.
Qi Yun Pan - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Evaluation of Wind Turbine Health Condition Based on Multi-Source Information Fusion
Advanced Materials Research, 2012Co-Authors: Yu Liang Dong, Cheng Cheng Wang, Qi Yun PanAbstract:Due to highly complex and non-stationary operation Condition, a dynamic wind turbine Health Condition evaluation approach based on multi-source information fusion is recommended. The uncertainty and incompletion of evaluation factors are fully considered, both qualitative and quantitative evidences are integrated. A case study is provided to illustrate the implementation process of the dynamic evaluation approach for wind turbine Health Condition. It is shown that the proposed approach offers a flexible and effective way of wind turbine Health Condition evaluation and the result can be used as a support for Condition based maintenance decision.
