Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing

An, Xueli; Jiang, Dongxiang; Chen, Jie; Liu, Chao

doi:10.1177/1077546311403185

Cited by 59 publications

(36 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…25 It has been widely used in biomedical signal processing and bearing defect diagnosis. 26 Comparing with other adaptive time-frequency analysis methods such as EMD and local mean decomposition (LMD), ITD shows obvious advantages in computational efficiency and frequency resolution for complex and nonstationary signals. First of all, ITD is originally proposed for nonstationary signals that are time varying.…”

Section: Itdmentioning

confidence: 99%

Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Duan

Yao

Wang

et al. 2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Gearbox diagnosis under stationary operating conditions has been extensively investigated; however, variable operating conditions such as load and speed changes play important roles in affecting the accuracy of gearbox diagnosis. This article presents an integrative approach of intrinsic time-scale decomposition and hierarchical temporal memory for gearbox diagnosis under variable operating conditions. A total of two modules are emphasized including a feature extraction method and an integrative feature fusion and classification model. Intrinsic time-scale decomposition method is investigated to extract the gearbox features which are insensitive to variable operating conditions, and its performance overcomes the commonly used empirical mode decomposition in terms of decomposition result and computational efficiency. Hierarchical temporal memory integrates feature fusion and pattern classification in one model to autonomously diagnose gearbox defect. Performance comparison among the presented method, back-propagation neural network, support vector machine, and fuzzy c-means clustering using experimental data demonstrate the effectiveness of the presented method.

show abstract

Section: Itdmentioning

confidence: 99%

Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Duan

Yao

Wang

et al. 2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…ITD is a relatively new non-stationary signal process tool which decomposes a complex multi-component signal into several proper rotation components (PRCs) with the lowest varying PRC as the trend. An et al [15] put forward a fault diagnosis method of wind turbine bearing based on intrinsic time-scale decomposition (ITD) and regarded the frequency centers of the main proper rotation components as fault feature vectors to identify the fault types of the wind turbine bearing. Lin [16] used the improved intrinsic timescale decomposition (IITD) method to process the intermittent signals and resisted the mode mixing of ITD.…”

Section: Introductionmentioning

confidence: 99%

A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension

Yan

Xiang

2015

Renewable Energy

View full text Add to dashboard Cite

a b s t r a c tIn this paper an ensemble intrinsic time-scale decomposition (EITD) method based on the cubic spline interpolation and linear transformation of intrinsic time-scale decomposition (ITD) was proposed, which can restrain the end effect and avoid the signal distortion. Combining ensemble intrinsic time-scale decomposition (EITD) with wavelet packet transform (WPT) and correlation dimension (CD), a novel method for decomposing nonstationary vibration signal and diagnosing wind turbine faults is presented. In implementation of the method, wavelet packet transform is employed to denoise raw vibration signals. Some important influencing factors relating directly to the computational precision of correlation dimension are discussed. The advantage of combining EITD and fractal dimension is that it does recognize the wind turbine gearbox fault types, and can solve the difficulty of recognizing fault conditions when two or more fractal dimensions are close to each other. To verify the effectiveness of the EITD-WPT-CD in detecting the faults, their induced vibrations are collected from high speed shaft gear under normal and faulty conditions through acceleration measurement. The results show that this method is capable of extracting the signal features and identifying the working conditions. The fault diagnosis application in a wind turbine gearbox indicates that the proposed method improved the accuracy of fault diagnosis.

show abstract

“…Intrinsic time-scale decomposition (ITD) is a novel signal processing method developed in recent years, which could decompose a complex signal into several proper rotation components (PRCs) based on the local time-scale of signal characteristics [23,24]. The ITD algorithm can decompose complex signals into a number of proper rotations (PR) based on signals' local characteristics adaptively.…”

Section: Introductionmentioning

confidence: 99%

Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum

Bi¹,

Ma²,

Liu³

et al. 2018

J. vibroeng.

View full text Add to dashboard Cite

Engine knock limits the thermal efficiency improvement of spark-ignition (SI) engines. Thus, the extract research of the knock characteristics has a great significance for the development of gasoline engines. The research proposes a novel knock detection and diagnosis method in SI engines using the CEIITD (Complementary Ensemble Improved Intrinsic timescale decomposition) and Bi-spectrum algorithm. The CEIITD algorithm is used to extract the knock characteristics. The results show that the CEIITD algorithm can effectively and clearly extract the knock shock characteristics (including light knock) through the vibration signals. A Bi-spectrum analysis can further distinguish between the light knock signal and normal combustion signal. The Bi-spectrum results also show that knock characteristic has a strong non-Gaussian property. At last, the Band pass filter and Improved ITD method were employed to identify the knock characteristics from these cylinder block vibration signals. The comparison result shows that the CEIITD method proposed in this paper is more suitable to detect the knock characteristic.

show abstract

Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing

Cited by 59 publications

References 8 publications

Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension

Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum

Contact Info

Product

Resources

About