Prediction of service life of large centrifugal compressor remanufactured impeller based on clustering rough set and fuzzy Bandelet neural network |
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| Affiliation: | 1. SIME Laboratory, Higher National Engineering School of Tunis, University of Tunis, Tunisia;2. Digital Research Centre of Sfax, Tunisia;1. National Safety Engineering Technology Research Center for Pressure Vessels and Pipelines, Anhui Province Safety Technology Laboratory for Pressure Vessels and Pipelines, Hefei General Machinery Research Institute, Hefei 230031, China;2. National Key Laboratory of Compressor Technology, Hefei General Machinery Research Institute, Hefei 230031, China;1. School of Economics and Management, Xidian University, Xi’an, Shaanxi 710071 China;2. Center of Network, Guangdong AIB Polytechnic, Guangzhou, Guangdong 510507 China;3. Department of Rheumatology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 China;4. Viterbi School of Engineering, University of Southern California, Los Angeles 90007 USA;5. Center of Faculty Dvpt. and Tech., Guangdong Univ. of Finance and Economics, Guangzhou, 510320 China |
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| Abstract: | In order to predict the service life of large centrifugal compressor impeller correctly, the rough set and fuzzy Bandelet neural network are combined to construct the novel prediction model which can give full play to theirs advantages. The attribute reduction algorithm based rough set and clustering method is firstly designed to optimize the inputting variables of fuzzy Bandelet neural network. And then the prediction model based on fuzzy Bandelet neural network is proposed, the Bandelet function is used as the excitation function of hidden layer and is combined with fuzzy theory to improve the prediction effectiveness of the prediction model. The training algorithm of fuzzy Bandelet neural network is designed based on improved genetic algorithm, the improved genetic algorithm introduces the adaptive differential evolution method into the traditional genetic algorithm, which can effectively optimize the parameters of fuzzy Bandelet neural network. Finally, the original 30 input variables of fuzzy Bandelet neural network are reduced to 9 input nodes based on rough set using 500 remanufacturing impellers as research objects. The service life of remanufacturing impeller is predicted based on three prediction models, and simulation results show that the fuzzy Bandelet neural network optimized by improved genetic algorithm has highest prediction precision and efficiency, which can correctly predict the service life of remanufacturing impeller. |
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| Keywords: | Fuzzy bandelet neural network Centrifugal compressor Remanufacturing impeller Service life Rough set |
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