scholarly journals A New Improved Kurtogram and Its Application to Bearing Fault Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Lei Xiao ◽  
Jianmin Zhao ◽  
Hongzhi Teng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
High Reliability ◽  
Wavelet Packet ◽  
Optimal Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Incipient Faults ◽  
Low Load ◽  
Envelope Signal

A new improved Kurtogram was proposed in this paper. Instead of Kurtosis, correlated Kurtosis of envelope signal extracted from the wavelet packet node was used as an indicator to determine the optimal frequency band. Correlated Kurtosis helps to determine the fault related impulse signals not affected by other unrelated signal components. Finally, two simulated and three experimental bearing fault cases are used to validate the effectiveness of proposed method and to compare with other similar methods. The results demonstrate it can locate resonant frequency band with a high reliability than two previous developed methods by Lei et al. and Wang et al. especially for the incipient faults under low load.

scholarly journals Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Haodong Yuan ◽  
Jin Chen ◽  
Guangming Dong
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Fixed Number ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learned Dictionary ◽  
Adaptive Pso ◽  
Diagnosis Method ◽  
Linear Svm

A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet and Improved BP Neural Network for Wind Turbines

2013 ◽  
Vol 347-350 ◽  
pp. 117-120
Author(s):  
Zhao Ran Hou
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Domain ◽  
Wind Turbines ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Rolling Bearing ◽  
Transmission System ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Vibration signal was a carrier of fault features of the wind turbine transmission system, it can reflect most of the fault information of the wind turbine transmission system. According to the frequency domain features of the roller bearing fault, wavelet packet transform for feature extraction was proposed as the characteristics of wind turbines in the presence of a large number of transient and non-stationary signals. The characteristics of wavelet packet was analyzed, combined with the wind turbines in the rolling bearing fault characteristic vibration extraction methods, the rolling bearing fault diagnosis was realized through the wavelet packet decomposition and reconstruction, the procedure was given. The simulation result shows that this application can reflect relationship of the failure characteristics and frequency domain feature vectors, also the nonlinear mapping ability of neural networks was played and the fault diagnosis capability enhanced.

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