The condition monitoring system of the hydropower generator unit (HGU) contains a vast amount of monitoring data related to equipment performance. It is challenging to reveal the HGU performance change law from these data. This paper constructed a correlation transmissibility damage indicator (CTDI) to analyze HGU degradation performance based on the transmissibility function (TF). Unlike traditional TF damage indicators, CTDI can use different types of monitoring data to obtain system TF to increase TF combination and improve detection sensitivity. The analysis is performed using actual monitoring data from a hydropower plant in Guangxi, China. Firstly, a new frequency concept is defined based on the characteristics of HGU obtaining data every 10 minutes. Then, the HGU is considered a system and divided into three subsystems according to the sensor installation locations of the monitoring system. The damage indicator is used to analyze the system and its subsystems at the time scales of day, week, and month to locate and quantify the damage of HGU. The final results of this analysis show that the method can accurately detect and locate faults, applies to different operating conditions, and reveals the HGU performance deterioration pattern, which is of great significance for HGU maintenance.
AnXPanLZhangF (2017) Analysis of hydropower unit vibration signals based on variational mode decomposition. Journal of Vibration and Control23(12): 1938–1953. DOI: 10.1177/1077546315605240.
2.
ChengJZZhuCFuWL, et al. (2019) An imitation medical diagnosis method of hydro-turbine generating unit based on Bayesian network. Transactions of the Institute of Measurement and Control41(12): 3406–3420. DOI: 10.1177/0142331219826665.
3.
ChesneSDeraemaekerA (2013) Damage localization using transmissibility functions: a critical review. Mechanical Systems and Signal Processing38(2): 569–584. DOI: 10.1016/j.ymssp.2013.01.020.
4.
FengZKNiuWJWangS, et al. (2018) Developing a successive linear programming model for head-sensitive hydropower system operation considering power shortage aspect. Energy155: 252–261. DOI: 10.1016/j.energy.2018.04.173.
FuWLWangKZhangC, et al. (2019a) A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine. Transactions of the Institute of Measurement and Control41(15): 4436–4449. DOI: 10.1177/0142331219860279.
7.
FuWLWangKLiCS, et al. (2019b) Vibration trend measurement for a hydropower generator based on optimal variational mode decomposition and an LSSVM improved with chaotic sine cosine algorithm optimization. Measurement Science and Technology30(1): 1–26. DOI: 10.1088/1361-6501/aaf377.
8.
FuWLWangKTanJW, et al. (2020) Vibration tendency prediction approach for Hydropower Generator fused with multiscale dominant ingredient chaotic analysis, adaptive mutation grey wolf optimizer, and KELM. Complexity2020: 1–20. DOI: 10.1155/2020/4516132.
9.
HuXLiCSTangG (2019) A hybrid model for predicting the degradation trend of hydropower units based on deep learning. In: Prognostics and System Health Management Conference (PHM-Qingdao), Qingdao, China, 2019. DOI: 10.1109/PHM-Qingdao46334.2019.8942913.
10.
HuangHDQinASMaoHL, et al. (2021) The prediction method on the early failure of hydropower units based on Gaussian Process Regression driven by monitoring data. Applied Sciences-Basel11(1): 153. DOI: 10.3390/app11010153.
11.
HughesDRGhoshalARoweE, et al. (2004) Health monitoring of thick materials using piezoceramic patches, time signals, and wavelet transmittance functions. Shock and Vibration11(1): 47–66. DOI: 10.1155/2004/940976.
12.
JohnsonTJAdamsDE (2002) Transmissibility as a differential indicator of structural damage. Journal of Vibration and Acoustics-Transactions of the ASME124(4): 634–641. DOI: 10.1115/1.1500744.
13.
JohnsonTJBrownRLAdamsDE, et al. (2004) Distributed structural health monitoring with a smart sensor array. Mechanical Systems and Signal Processing18(3): 555–572. DOI: 10.1016/S0888-3270(03)00002-5.
14.
KhalilAAl JanaidehMAljanaidehKF, et al. (2021) Health monitoring of mixed autonomous and human-driven vehicle platoon using transmissibility identification. In: 2021 American Control Conference (ACC), New Orleans, LA, 2021. IEEE 3302-7 DOI: 10.23919/ACC50511.2021.9482794.
15.
LiQKLiaoMFJingXJ (2021) Transmissibility function-based fault diagnosis methods for beam-like engineering structures: a review of theory and properties. Nonlinear Dynamics106(3): 2131–2163. DOI: 10.1007/s11071-021-06883-5.
16.
MaiaNMMAlmeidaRABUrgueiraAPV, et al. (2011) Damage detection and quantification using transmissibility. Mechanical Systems and Signal Processing25(7): 2475–2483. DOI: 10.1016/j.ymssp.2011.04.002.
17.
PanLQLiJZLuoJ (2010) A temporal and spatial correlation based missing values imputation algorithm in wireless sensor networks. Chinese Journal of Computers33(1): 3–13.
18.
RaiAUpadhyaySH (2017) Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering. Mechanical Systems and Signal Processing93: 16–29. DOI: 10.1016/j.ymssp.2017.02.003.
19.
SchulzMJPaiPFInmanDJ (1999) Health monitoring and active control of composite structures using piezoceramic patches. Composites Part B-Engineering30(7): 713–725. DOI: 10.1016/S1359-8368(99)00034-7.
20.
SoofiYJBitarafM (2022) Output-only entropy-based damage detection using transmissibility function. Journal of Civil Structural Health Monitoring12(1): 191–205. DOI: 10.1007/s13349-021-00535-3.
21.
TongMYShaoKLLuoXL, et al. (2020) Application of a fractional grey prediction model based on a filtering algorithm in image processing. Mathematical Problems in Engineering2020(11): 1–18. DOI: 10.1155/2020/4170804.
22.
WelchP (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics15(2): 70–73. DOI: 10.1109/TAU.1967.1161901.
23.
XiongLLiuJSongB, et al. (2021) Deep learning compound trend prediction model for hydraulic turbine time series. International Journal of Low-Carbon Technologies16(3): 725–731. DOI: 10.1093/ijlct/ctaa106.
24.
XuBBChenDYZhangH, et al. (2018) Shaft mis-alignment induced vibration of a hydraulic turbine generating system considering parametric uncertainties. Journal of Sound and Vibration435: 74–90. DOI: 10.1016/j.jsv.2018.08.008.
25.
XuLPMaHYRenDZ (2019a) Reliability analysis of tractor multi-way valves based on the improved weighted grey relational method. Journal of Engineering-Joe13: 86–92. DOI: 10.1049/joe.2018.8985.
26.
XuBLiHPangW, et al. (2019b) Bayesian network approach to fault diagnosis of a hydroelectric generation system. Energy Science & Engineering7(5): 1669–1677. DOI: 10.1002/ese3.383.
27.
ZhangHSchulzMJFergusonF, et al. (1999) Structural health monitoring using transmittance functions. Mechanical Systems and Signal Processing13(5): 765–787. DOI: 10.1006/mssp.1999.1228.
28.
ZhangLLangZQPapaeliasM (2016) Generalized transmissibility damage indicator with application to wind turbine component condition monitoring. IEEE Transactions on Industrial Electronics63(10): 6347–6359. DOI: 10.1109/Tie.2016.2580519.
29.
ZhouKBZhangJYShanYH, et al. (2019) A hybrid multi-objective optimization model for vibration tendency prediction of Hydropower Generators. Sensors19(9): 2055–2076. DOI: 10.3390/s19092055.
30.
ZhouJZShanYHLiuJ, et al. (2020) Degradation tendency prediction for pumped storage unit based on integrated degradation index construction and hybrid CNN-LSTM model. Sensors20(15): 1–22. DOI: 10.3390/s20154277.
