Classification of fault location and performance degradation of a roller bearing

Mathematical Problems in Engineering

2017

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 -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.

Section: Comparison With Different Preliminarymentioning

confidence: 99%

Bearing Fault Diagnosis Based on Improved Locality‐Constrained Linear Coding and Adaptive PSO‐Optimized SVM

Yuan

Chen

Mathematical Problems in Engineering

2017

“…The scatter matrix for zero mean data is given by C = FF T . Then, a kernel matrix can be constructed as K = F T F. Using the kernel trick, the centered kernel matrix can be expressed as follows 14,15 …”

Section: Feature Extractionmentioning

confidence: 99%

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Sheng

Advances in Mechanical Engineering

et al. 2016

To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time-frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

“…Ozgonenel et al [9] investigated performance of EEMD and made a comparison between it and classical EMD for feature vector extraction. Zhang et al [10] extracted two types of features referred to as singular values and AR model parameters based on EEMD and then inputted these features to particle swarm optimization support vector machine to diagnose faults for rolling element bearings. An et al use EEMD and Hilbert transform to extract the fault features of bearing pedestal looseness of wind turbine effectively [11].…”

Section: Introductionmentioning

confidence: 99%

The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest

Qin

et al. 2017

Shock and Vibration

Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method.