A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Signal Spectrum Analysis of Sediment Water Impact of Hydraulic Turbine Based on ICEEMDAN-Wavelet Threshold Denoising Strategy
Studies show that sediment erosion is one of the main factors attributing to hydraulic turbine failure. The present paper represents an investigation into acoustic vibration signals generated by the water flow impacting the hydraulic turbine runner under three different operating conditions. Collected signals were denoised using the ICEEMDAN-wavelet threshold method, and then the spectral characteristics and sample entropy characteristics of the signals for the three operating conditions were analyzed. The results show that when clean water flows through the hydraulic turbine, the sample entropy reaches its smallest values and the dominant frequency component in the spectrogram is 59.39 Hz. When transitioning from clean water to the flood flow containing 2–4 mm sediment particles, the sample entropy is increasing and a high-frequency component higher than 59.39 Hz becomes the prominent frequency of the spectrogram. Meanwhile, the formation of high-frequency components increases with the sand-containing particle size. Based on the spectral characteristics and sample entropy characteristics of the acoustic vibration signals under different operating conditions, it can provide a reference for the sand avoidance operation of the hydraulic turbine during flood season. In addition, it provides a supplement to the existing hydraulic turbine condition’s monitoring systems and a new avenue for subsequent research on early warning of hydraulic turbine failure.
Signal Spectrum Analysis of Sediment Water Impact of Hydraulic Turbine Based on ICEEMDAN-Wavelet Threshold Denoising Strategy
Studies show that sediment erosion is one of the main factors attributing to hydraulic turbine failure. The present paper represents an investigation into acoustic vibration signals generated by the water flow impacting the hydraulic turbine runner under three different operating conditions. Collected signals were denoised using the ICEEMDAN-wavelet threshold method, and then the spectral characteristics and sample entropy characteristics of the signals for the three operating conditions were analyzed. The results show that when clean water flows through the hydraulic turbine, the sample entropy reaches its smallest values and the dominant frequency component in the spectrogram is 59.39 Hz. When transitioning from clean water to the flood flow containing 2–4 mm sediment particles, the sample entropy is increasing and a high-frequency component higher than 59.39 Hz becomes the prominent frequency of the spectrogram. Meanwhile, the formation of high-frequency components increases with the sand-containing particle size. Based on the spectral characteristics and sample entropy characteristics of the acoustic vibration signals under different operating conditions, it can provide a reference for the sand avoidance operation of the hydraulic turbine during flood season. In addition, it provides a supplement to the existing hydraulic turbine condition’s monitoring systems and a new avenue for subsequent research on early warning of hydraulic turbine failure.
Signal Spectrum Analysis of Sediment Water Impact of Hydraulic Turbine Based on ICEEMDAN-Wavelet Threshold Denoising Strategy
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Hydraulic pressure signal denoising using threshold self-learning wavelet algorithm
| British Library Online Contents | 2008
Analysis of Wavelet Algorithm of Redundancy Denoising for Hydraulic System Signal
| British Library Online Contents | 2009
Signal Denoising Based on the Wavelet Transform
| British Library Online Contents | 2000
Optimal Wavelet Basis Selection for Wavelet Denoising of Structural Vibration Signal
| British Library Conference Proceedings | 2014