A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification

Shi, Yousong; Zhou, Jing; Huang, Jie; Xu, Yanhe; Liu, Baonan

doi:10.3390/s22114266

Cited by 7 publications

(5 citation statements)

References 37 publications

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“…The method proposed in this experiment has a high AUC value, with AUC values of 0.7365, 0.7335, 0.9232, and 0.9141 for fan, water pump, slide rail, and valve sound detection, respectively, with an average AUC value of 0.8268. This paper's method increased the AUC value by 11.8722% compared to the method in [18], 35.6518% compared to the method in [19], and 38.4322% compared to the method in [20]. At the same time, the accuracy rate of the model proposed in this experiment is 90.1%, and the loss function value is 0.27, both higher than those of the methods in the literature.…”

Section: Discussionmentioning

confidence: 49%

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Intelligent Analysis of Vibration Faults in Hydroelectric Generating Units Based on Empirical Mode Decomposition

2023

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Implementing intelligent identification of faults in hydroelectric units helps in the timely detection of faults and taking measures to minimize economic losses. Therefore, improving the accuracy of fault signal recognition has always been a research focus. This study is based on the improved empirical mode decomposition (EMD) theory to study the denoising and feature extraction of vibration signals of hydroelectric units and uses the backpropagation neural network (BPNN) to establish corresponding connections between signal features and vibration fault states. The improved EMD in this study can improve the performance of noise reduction processing and contribute to the accurate identification of vibration faults. The vibration fault identification criteria can adopt three dimensionless feature parameters: peak skewness coefficient, valley skewness coefficient, and kurtosis coefficient of the second- and third-order components of the signal, with recognition rates and accuracy reaching 90.6% and 96.2%, respectively. This paper’s area under the curve (AUC) values were 0.7365, 0.7335, 0.9232, and 0.9141 for abnormal sound detection of the fan, water pump, slide, and valve, respectively, with an average AUC value of 0.8268. This paper’s accuracy is 90.1%, and the loss function value is 0.27. The validation results demonstrate that this paper’s method has high intelligent fault analysis capabilities. The experimental results confirm that this method can effectively detect vibration signals in hydroelectric units and perform effective noise reduction processing, thereby improving the diagnostic accuracy of fault signals. Therefore, this method can be effectively applied to the detection of vibration faults in hydroelectric units.

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Section: Discussionmentioning

confidence: 49%

“…Most of the features extracted from fault signals are time-frequency features, and the vibration signals of hydroelectric units are essentially nonlinear and nonstationary [18]. Wavelet analysis has had certain advantages in processing such signals, but it has two shortcomings in application.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Analysis of Vibration Faults in Hydroelectric Generating Units Based on Empirical Mode Decomposition

2023

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“…The potential energy equation of hydropower units' shafting is [27]: In order to facilitate the analysis, this paper adopts appropriate simplified assumptions: (1) In the modeling stage, the torsional motion and axial vibration of shafting are ignored, and only the radial displacement and inclination of the spindle system are analyzed.…”

Section: Dynamics Modeling Of Shafting Of Hydropower Unitsmentioning

confidence: 99%

Analysis of Shafting System Vibration Characteristics for Mixed-Flow Hydropower Units Considering Sand Wear on Turbine Blades

Chen,

Wang,

Chen

et al. 2024

Applied Sciences

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Addressing the issue of increased shaft-system vibration in high-altitude mixed-flow hydropower generating units due to sand wear on turbine blades, a three-dimensional model of a specific mixed-flow water turbine was constructed. CFD numerical simulations were employed to analyze the fluid exciting force acting on the turbine runner under varying degrees of blade wear. An approximate analytical model was then established for the variation of fluid exciting force in the turbine runner system using the Fourier harmonic analysis method. A multi-degree-of-freedom mathematical model of flexural and inclined coupling vibration of a hydropower unit’s shafting, considering blade wear, was constructed. The nonlinear dynamic model was numerically calculated by the Runge–Kutta method. The vibration responses of the shafting of hydropower units under different wear degrees were obtained by means of a time-domain diagram, frequency-domain diagram, axis-locus diagram, phase-locus diagram, and Poincare mapping. Based on the formula for calculating the wear amount of the blade material, the runner amplitude degradation trajectory model was established, and the pseudo-failure time of turbine blades was determined according to the allowable value of amplitude.

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“…Huang et al [12] improved the transfer function method and built a nonlinear flexible model using Simulink for the simulation of hydraulic-mechanical-electrical coupling dynamics containing fault characteristics. Although Shi et al [13] have not established the system coupling model, they have proposed that shafting is the core component of coupling problem in hydropower unit in their research on fault diagnosis. Yang et al [14] studied the coupling mechanism in the hydraulic system of hydropower station, and the research results show that the coupling relationship between the pump and the motor in the system cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

A multi-field coupling simulation model for the centrifugal pump system

Dong

Shuai

et al. 2023

Mechanics & Industry

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In this paper, a multi-field coupling simulation model, including the motor, shaft, and pump, is established to analyze the motor's influence and the shaft on the pump's operating status. First of all, different forms of simulation calculation models are established according to each device's characteristics. Then, the Predictor-Corrector Newmark-β method (PC Newmark-β method) is used to calculate the operating status of the motor, shaft, and pump under mutual influence. Finally, the validity of the simulation model is verified by comparing the experimental data. The results show that the centrifugal pump, shaft, and motor should be viewed as a complete system to analyze. The torque and speed of different equipment are interrelated and influenced. In addition, the dynamic characteristics of the shaft will also cause the fluctuation of the pump's operating speed. The fluctuation of the centrifugal pump impeller's rotational speed further affects the characteristics of the pressure pulsation on the wall of the centrifugal pump, which is finally reflected in the vibration characteristics of the pump casing.

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A Vibration Fault Identification Framework for Shafting Systems of Hydropower Units: Nonlinear Modeling, Signal Processing, and Holographic Identification

Cited by 7 publications

References 37 publications

Intelligent Analysis of Vibration Faults in Hydroelectric Generating Units Based on Empirical Mode Decomposition

Intelligent Analysis of Vibration Faults in Hydroelectric Generating Units Based on Empirical Mode Decomposition

Analysis of Shafting System Vibration Characteristics for Mixed-Flow Hydropower Units Considering Sand Wear on Turbine Blades

A multi-field coupling simulation model for the centrifugal pump system

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