scholarly journals Underdetermined Blind Source Separation with Variational Mode Decomposition for Compound Roller Bearing Fault Signals

Sensors ◽  
2016 ◽  
Vol 16 (6) ◽  
pp. 897 ◽  
Author(s):  
Gang Tang ◽  
Ganggang Luo ◽  
Weihua Zhang ◽  
Caijin Yang ◽  
Huaqing Wang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Roller Bearing ◽  
Variational Mode Decomposition ◽  
Underdetermined Blind Source Separation ◽  
Bearing Fault ◽  
Mode Decomposition

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

2016 ◽  
Vol 39 (7) ◽  
pp. 1000-1006 ◽  
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.

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

2017 ◽  
Vol 231 (2) ◽  
pp. 200-206 ◽  
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.

scholarly journals Diagnosis of Roller Bearings Compound Fault Using Underdetermined Blind Source Separation Algorithm Based on Null-Space Pursuit

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lingli Cui ◽  
Chunguang Wu ◽  
Chunqing Ma ◽  
Huaqing Wang
Keyword(s):  
Differential Operator ◽  
Blind Source Separation ◽  
Narrow Band ◽  
Null Space ◽  
Source Separation ◽  
Roller Bearing ◽  
Vibration Sensor ◽  
Underdetermined Blind Source Separation ◽  
Roller Bearings ◽  
Band Signal

In order to solve the problem of underdetermined blind source separation (BSS) in the diagnosis of compound fault of roller bearings, an underdetermined BSS algorithm based on null-space pursuit (NSP) was proposed. In this algorithm, the signal model of faulty roller bearing is firstly used to construct an appropriate differential operator in null space. With the constructed differential operator, the mixed signals collected by the vibration sensor are decomposed into a series of stacks of narrow band signal containing the characteristics of faulty bearing. Finally, the underdetermined problem is transformed to an overdetermined problem by combining the narrow band signals and the original mixed signals into a new group of observed signals. In this way, the separation of the mixed signals can be realized. Experiments and engineering data analyses show that the problem of underdetermined BSS can be solved effectively by this approach, and then the compound fault of the roller bearing can be separated.

Sign in / Sign up

Export Citation Format

Share Document