| Condition Monitoring of Turbines Using Nonlinear Mapping Method |
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| 引用本文： | LiaoGuang-lan ShiTi-lin JiangNan. Condition Monitoring of Turbines Using Nonlinear Mapping Method[J]. 武汉大学学报:自然科学英文版, 2004, 9(2): 225-228. DOI: 10.1007/BF02830607 |
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| 作者姓名： | LiaoGuang-lan ShiTi-lin JiangNan |
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| 作者单位： | SchoolofMechanicalScienceandEngineering,HuazhongUniversityofScienceandTechnology,Wuhan430074,Hubei,China |
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| 基金项目： | SupportedbytheNationalKeyBasicResearchSpecialFoundofChina (2 0 0 3CB71 62 0 7)andtheNationalNaturalScienceFoundationofChina (50 3750 4 7) |
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| 摘 要： | Aiming at the non-linear nature of the signals generated from turbines,curvilinear component analysis (CCA),a novel nonlinear projection method that favors local topology conservation is presented for turbines conditions monitoring.This is accomplished in two steps.Time domain features are extracted from raw vibration signals,and then they are projected into a two-dimensional output space by using CCA method and form regions indicative of specific conditions,which helps classify and identify turbine states visually.Therefore,the variation of turbine conditions can be observed clearly with the trajectory of image points for the feature data in the two-dimensional space,and the occurrence and development of failures can be monitored in time.
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| 关 键 词： | 涡轮机 非线性映射法 曲线组成分析 CCA 状态监控 |
| 收稿时间： | 2003-03-10 |
Condition monitoring of turbines using nonlinear mapping method |
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| Liao Guang-lan,Shi Tie-lin,Jiang Nan. Condition monitoring of turbines using nonlinear mapping method[J]. Wuhan University Journal of Natural Sciences, 2004, 9(2): 225-228. DOI: 10.1007/BF02830607 |
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| Authors: | Liao Guang-lan Shi Tie-lin Jiang Nan |
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| Affiliation: | (1) School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074 Wuhan, Hubei, China |
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| Abstract: | Aiming at the non-linear nature of the signals generated from turbines, curvilinear component analysis (CCA), a novel nonlinear projection method that favors local topology conservation is presented for turbines conditions monitoring. This is accomplished in two steps. Time domain features are extracted from raw vibration signals, and then they are projected into a two-dimensional output space by using CCA method and form regions indicative of specific conditions, which helps classify and identify turbine states visually. Therefore, the variation of turbine conditions can be observed clearly with the trajectory of image points for the feature data in the two-dimensional space, and the occurrence and development of failures can be monitored in time. |
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| Keywords: | condition monitoring turbines nonlinear mapping curvilinear component analysis |
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