Fault Diagnosis Method of Wind Turbine Bearing Based on Improved Intrinsic Time-scale Decomposition and Spectral Kurtosis

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

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Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

Frontiers of Information Technology & Electronic Engineering ◽

10.1631/fitee.1500337 ◽

2017 ◽

Vol 18 (2) ◽

pp. 272-286 ◽

Cited By ~ 7

Author(s):

Jun-hong Zhang ◽

Yu Liu

Keyword(s):

Fault Diagnosis ◽

Diesel Engines ◽

Time Scale ◽

Least Square ◽

Intrinsic Time ◽

Time Scale Decomposition

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A novel wind turbine bearing fault diagnosis method based on Integral Extension LMD

Measurement ◽

10.1016/j.measurement.2015.06.005 ◽

2015 ◽

Vol 74 ◽

pp. 70-77 ◽

Cited By ~ 26

Author(s):

W.Y. Liu ◽

Q.W. Gao ◽

G. Ye ◽

R. Ma ◽

X.N. Lu ◽

...

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Integral Extension ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Diagnosis Method ◽

Turbine Bearing

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Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406217691554 ◽

2017 ◽

Vol 232 (5) ◽

pp. 881-894 ◽

Cited By ~ 5

Author(s):

Yu Liu ◽

Junhong Zhang ◽

Kongjian Qin ◽

Yueyun Xu

Keyword(s):

Pattern Recognition ◽

Fault Diagnosis ◽

Diesel Engine ◽

Time Scale ◽

Relevance Vector Machine ◽

Economic Losses ◽

Intrinsic Time ◽

Fault Features ◽

Time Scale Decomposition ◽

Diagnosis Approach

Diesel engine is the most widely used power source of machines. However, faults occur frequently and often cause terrible accidents and serious economic losses. Therefore, diesel engine fault diagnosis is very important. Commonly, a single unitary pattern recognition method is used to diagnose the faults of diesel engine, but its performance decreases sharply when there are many fault types. Targeting this problem, a novel diesel engine fault diagnosis approach is proposed in this study. The approach is composed of four stages. Firstly, the nonstationary and nonlinear vibration signal of diesel engine is decomposed into a series of proper rotation components (PRCs) and a residual signal by the intrinsic time-scale decomposition (ITD) method. Secondly, six typical time-domain and four typical frequency-domain characteristics of the first several PRCs are extracted as fault features. Then, the modular and ensemble concepts are introduced to construct the multistage Adaboost relevance vector machine (RVM) model, in which the kernel fuzzy c-means clustering (KFCM) algorithm is used to decompose a complex classification task into several simple modules, and the Adaboost algorithm is used to improve the performance of each RVM based module. Finally, the fault diagnosis results can be obtained by inputting the fault features into the multistage Adaboost RVM model. The analysis results show that the fault diagnosis approach based on ITD and multistage Adaboost RVM performs effectively for the fault diagnosis of diesel engine, and it is better than the traditional pattern recognition methods.

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