Fault diagnosis method for spherical roller bearing of wind turbine based on variational mode decomposition and singular value decomposition

Adaptive local iterative filtering (ALIF) is a new signal decomposition method that uses the iterative filters strategy together with an adaptive and data-driven filter length selection to achieve the decomposition. The complexity of wind power generation systems means that the randomness and kinetic mutation behaviour of their vibration signals are demonstrated at different scales. Thus it is necessary to analyse the vibration signal across multiple scales. A method based on ALIF and singular value decomposition (SVD) was used for the fault diagnosis of a wind turbine roller bearing. The ALIF method is used to decompose the bearing vibration signal into several stable components. The components, which contain major fault information, are selected to build an initial feature vector matrix. The singular value of the matrix is computed as the feature vectors of each bearing fault. The feature vectors embody the characteristics of the vibration signal. The nearest neighbour algorithm is used as a classifier to identify faults in a roller bearing. Experimental data show that the proposed method can be used to identify roller bearing faults of a wind turbine.

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A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD

Shock and Vibration ◽

10.1155/2018/1382726 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Chenguang Huang ◽

Jianhui Lin ◽

Jianming Ding ◽

Yan Huang

Keyword(s):

Fault Diagnosis ◽

Singular Values ◽

Ensemble Empirical Mode Decomposition ◽

Singular Value ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Diagnosis Method ◽

Adaptive Noise ◽

Periodic Impulse ◽

Value Decomposition

A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.

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Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331215626247 ◽

2016 ◽

Vol 39 (7) ◽

pp. 1000-1006 ◽

Cited By ~ 8

Author(s):

Xueli An ◽

Yongjun Tang

Keyword(s):

Fault Diagnosis ◽

Energy Distribution ◽

Wind Turbine ◽

Roller Bearing ◽

Vibration Signal ◽

Rolling Bearing ◽

Variational Mode Decomposition ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition

For the unsteady characteristics of a fault vibration signal of a wind turbine’s rolling bearing, a bearing fault diagnosis method based on variational mode decomposition of the energy distribution is proposed. Firstly, variational mode decomposition is used to decompose the original vibration signal into a finite number of stationary components. Then, some components which comprise the major fault information are selected for further analysis. When a rolling bearing fault occurs, the energy in different frequency bands of the vibration acceleration signals will change. Energy characteristic parameters can then be extracted from each component as the input parameters of the classifier, based on the K nearest neighbour algorithm. This can identify the type of fault in the rolling bearing. The vibration signals from a spherical roller bearing in its normal state, with an outer race fault, with an inner race fault and with a roller fault were analyzed. The results showed that the proposed method (variational mode decomposition is used as a pre-processor to extract the energy of each frequency band as the characteristic parameter) can identify the working state and fault type of rolling bearings in a wind turbine.

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Method of Multi-resolution and Effective Singular Value Decomposition in Under-determined Blind Source Separation and Its Application to the Fault Diagnosis of Roller Bearing

2015 11th International Conference on Computational Intelligence and Security (CIS) ◽

10.1109/cis.2015.117 ◽

2015 ◽

Cited By ~ 1

Author(s):

Baitong Zhou ◽

Zengli Liu

Keyword(s):

Fault Diagnosis ◽

Singular Value Decomposition ◽

Blind Source Separation ◽

Source Separation ◽

Roller Bearing ◽

Singular Value ◽

Value Decomposition

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Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

Sensors ◽

10.3390/s18041210 ◽

2018 ◽

Vol 18 (4) ◽

pp. 1210 ◽

Cited By ~ 17

Author(s):

Rui Yuan ◽

Yong Lv ◽

Gangbing Song

Keyword(s):

Fault Diagnosis ◽

Singular Value Decomposition ◽

Empirical Mode Decomposition ◽

Singular Value ◽

High Order ◽

Rolling Bearings ◽

Multivariate Empirical Mode Decomposition ◽

Mode Decomposition ◽

Value Decomposition

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Bearing fault diagnosis of a wind turbine based on variational mode decomposition and permutation entropy

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x17693492 ◽

2017 ◽

Vol 231 (2) ◽

pp. 200-206 ◽

Cited By ~ 11

Author(s):

Xueli An ◽

Luoping Pan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Nearest Neighbor ◽

Roller Bearing ◽

Permutation Entropy ◽

Variational Mode Decomposition ◽

Vibration Signals ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Mode Decomposition

Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis.

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