Characteristic parameter degradation prediction of hydropower unit based on radial basis function surface and empirical mode decomposition

An, Xueli; Pan, Luoping

doi:10.1177/1077546313508578

Cited by 17 publications

(13 citation statements)

References 14 publications

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“…For instance, Guo et al constructed one-dimensional PDI utilizing the recurrent neural network (RNN) to predict remaining useful life of bearing [ 16 ]. An et al adopted a PDI with the upper bracket horizontal vibration data to analyze the degradation trend of hydropower unit [ 17 ]. However, these PDIs are constructed mainly with single sensor data or a single object, which cannot comprehensively assess the degradation of the entire equipment.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Fu et al used the least squares support vector regression (LS-SVR) model to predict the vibration tendency of hydropower unit [ 18 ]. An et al solved the DTP of hydropower unit with radial basis function neural network (RBFNN) and grey theory [ 17 ]. In the above methods, the parameters of LS-SVR affect its performance in dealing with constrained optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou

Shan

Liu

et al. 2020

Sensors

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Accurate degradation tendency prediction (DTP) is vital for the secure operation of a pumped storage unit (PSU). However, the existing techniques and methodologies for DTP still face challenges, such as a lack of appropriate degradation indicators, insufficient accuracy, and poor capability to track the data fluctuation. In this paper, a hybrid model is proposed for the degradation tendency prediction of a PSU, which combines the integrated degradation index (IDI) construction and convolutional neural network-long short-term memory (CNN-LSTM). Firstly, the health model of a PSU is constructed with Gaussian process regression (GPR) and the condition parameters of active power, working head, and guide vane opening. Subsequently, for comprehensively quantifying the degradation level of PSU, an IDI is developed using entropy weight (EW) theory. Finally, combining the local feature extraction of the CNN with the time series representation of LSTM, the CNN-LSTM model is constructed to realize DTP. To validate the effectiveness of the proposed model, the monitoring data collected from a PSU in China is taken as case studies. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) obtained by the proposed model are 1.1588, 0.8994, 0.0918, and 0.9713, which can meet the engineering application requirements. The experimental results show that the proposed model outperforms other comparison models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Zhou

Shan

Liu

et al. 2020

Sensors

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“…To accurately diagnose the early faults of bearings, deconvolution methods, 1 spectral kurtosis, 2,3 order tracking, 4 and Teager–Kaiser energy methods 5,6 have been widely used to extract the bearing fault feature from the raw vibration signals. Recently, a large number of time–frequency analysis methods, including wavelet transform, 7,8 variational mode decompoisition, 9,10 time–frequency distribution, 11,12 empirical mode decomposition (EMD), 13,14 and matching pursuit, 15,16 have been developed for rotating machinery health condition monitoring and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

Cheng

Zou

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Local means decomposition is an adaptive and nonparametric time–frequency decomposition method for nonstationary and nonlinear signals. However, in practice, local means decomposition is susceptible to mode mixing phenomena and produces different scale oscillations in one mode or similar scale oscillations in different modes, rendering the decomposition results difficult to interpret in terms of physical meansing. The noise-assisted ensemble local means decomposition method not only effectively resolved mode mixing but also generated a new problem, which tolerates residual noise in signal reconstruction. Targeting these shortcomings, this article proposes complementary ensemble local means decomposition, a novel noise-assisted time–frequency analysis method. First, an ensemble of white noise is added to the original signal via complementary positive and negative pairs. Second, local means decomposition is applied to decompose the noisy signals into a series of product functions, and the final results are obtained by averaging. The simulation results confirm that complementary ensemble local means decomposition offers an innovative improvement over ensemble local means decomposition in terms of eliminating residual noise. The superiority of the proposed method was further validated on fault signals obtained from faulty railway bearings (rolling element and outer race fault signals).

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“…Huang proposed the use of an empirical mode decomposition method 8 : it can decompose a complex signal into a limited sum of intrinsic mode functions (IMFs). Each IMF component represents a group of characteristic scale signals.…”

Section: Introductionmentioning

confidence: 99%

“…Approximate entropy 8,11 is a measure of the complexity of a time series: it is based on marginal probability distribution statistics. The required data length of this method is short.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

Pan

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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For the unsteady characteristics of a fault vibration signal from a wind turbine rolling bearing, a bearing fault diagnosis method based on adaptive local iterative filtering and approximate entropy is proposed. The adaptive local iterative filtering method is used to decompose original vibration signals into a finite number of stationary components. The components which comprise major fault information are selected for further analysis. The approximate entropy of the selected components is calculated as a fault feature value and input to a fault classifier. The classifier is based on the nearest neighbor algorithm. The vibration signals from a spherical roller bearing on a wind turbine in its normal state, with an outer race fault, an inner race fault and a roller fault are analyzed. The results show that the proposed method can accurately and efficiently identify the fault modes present in the rolling bearings of a wind turbine.

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Characteristic parameter degradation prediction of hydropower unit based on radial basis function surface and empirical mode decomposition

Cited by 17 publications

References 14 publications

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Degradation Tendency Prediction for Pumped Storage Unit Based on Integrated Degradation Index Construction and Hybrid CNN-LSTM Model

Complementary ensemble local means decomposition method and its application to rolling element bearings fault diagnosis

Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropy

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