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| Fault Diagnosis and Life Prediction of Genetic Neural Network-based Dehumidifier |
| Received:June 16, 2016 Revised:January 15, 2017 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7643/ issn.1672-9242.2017.01.018 |
| KeyWord:genetic algorithm neural network dehumidifier dew point temperature |
| Author | Institution |
| ZHANG Qi |
Department of Environment Simulation, Huayin Ordinance Test Centre, Huayin , China |
| WU Ya-feng |
School of Power and Energy, Northwestern Polytechnical University, Xi′an , China |
| XU Jian |
Department of Environment Simulation, Huayin Ordinance Test Centre, Huayin , China |
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| Abstract: |
| Objective To identify performance state of dehumidifier and predict residual life of adsorbent. Methods A data driving-based genetic neural network model was proposed in view of the slow variable failure process of dehumidifier. Firstly, to solve the issue fuzzy division of equipment failure degree, 5 failure patterns of the dehumidifier were obtained with correlation analysis according to characteristic vectors formed by 5 thermodynamic parameters which reflect adsorbent degradation. Secondly, the mapping relationship between state parameters and failure patterns was established with genetic neural network. Finally, principal parameters reflecting adsorption capacity of equipment were predicted by extrapolation. Results The diagnosis network could determine deterioration degree and evolution process of equipment accurately. It possessed very high accuracy in network prediction. Conclusion This method can effectively finish fault diagnosis and prediction of dehumidifier. |
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