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| Dynamic parametric identification for a hydropower house based on data fusion and LMD |
| WANG Haijun1,2 LI Kang1,2 LIAN Jijian1,2 |
1. State Key Lab of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, China;
2. School of Civil Engineering, Tianjin University, Tianjin 300350, China |
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Abstract The vibration monitoring accuracy of hydropower station structures is easy to be affected by environmental background noise and measured points’ locations. In order to improve the signal-to-noise ratio of these vibration signals and their information integrity, the combined method based on data fusion and the local mean decomposition (LMD) was proposed. Firstly, vibration signals of different observation points were fused to improve the integrity of information. Then, the fused signals were decomposed with LMD into several product function (PF) components. Through the spectral analysis, the de-noised signals were reconstructed. Finally, the de-noised signals were identified to obtain effective dynamic parameters of the house structure. Through simulated signal analysis, it was shown that for dynamic parametric identification, the new method is superior to the digital filtering method, the wavelet threshold method and the ensemble empirical mode decomposition (EEMD). The proposed method was applied to analyze the actual measured vibration data of hydropower houses and the better results were achieved.
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Received: 12 October 2016
Published: 15 January 2018
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