An improved Hilbert–Huang transform and its application in vibration signal analysis

Peng, Z.K.; Tse, Peter W.; Chu, Fu Lei

doi:10.1016/j.jsv.2004.10.005

Cited by 409 publications

(225 citation statements)

References 13 publications

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“…One of the limitations of the EMD algorithm is that it produces unwanted (nonexistent) components at low frequencies [25]. To circumvent this, the cross-correlation µ i of c i with the initial time series signal f (t) is checked [25].…”

Section: Hilbert-huang Transform (Hht)mentioning

confidence: 99%

See 1 more Smart Citation

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

et al. 2017

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Abstract:The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs) and distribution system operators (DSOs) in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT), genetic algorithm (GA) and artificial neural network (ANN) is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP) meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day's wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method.

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Section: Hilbert-huang Transform (Hht)mentioning

confidence: 99%

“…To circumvent this, the cross-correlation µ i of c i with the initial time series signal f (t) is checked [25]. Only the IMFs with cross-correlation µ i greater than a prespecified minimum value λ are taken as real IMFs.…”

Section: Hilbert-huang Transform (Hht)mentioning

confidence: 99%

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

et al. 2017

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“…Wu and Huang [33] proposed the ensemble empirical mode decomposition that deals with the white noise that causes mode mixing. Peng et al [34] proposed the application of the wavelet packet transform to split the signal into a set of various narrow band signals before the use of the empirical mode decomposition.…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Bakker

Gibson

Wilson

et al. 2015

Mechanical Systems and Signal Processing

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Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the denoised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

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“…Peng et al suggested an improved Hilbert-Huang transform using wavelet packet transform and applied an IMF selection based on correlation coefficients [12] and Yu et al proposed the concept of EMD energy entropy and utilized its value to identify different bearing fault types [13]. Junsheng et al exploited singular values of IMFs as fault feature vectors of support vector machines [14] and Ricci et al presented an automatic IMF selection method using a merit index [15].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

et al. 2018

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Abstract:In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes. An effective solution to these problems in the decomposition process can help to determine significant IMFs and to improve the performance of the fault diagnosis system. This paper describes a novel power-based IMF selection algorithm and evaluates the performance of the proposed fault diagnosis system using improved complete ensemble EMD with adaptive noise and a multi-layer perceptron neural network.

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An improved Hilbert–Huang transform and its application in vibration signal analysis

Cited by 409 publications

References 13 publications

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

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