Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model

Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Hongchao; Yuan, Hongping

doi:10.1016/j.ymssp.2015.04.037

Cited by 76 publications

(38 citation statements)

References 21 publications

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“…Li et al [15] utilized the hierarchical fuzzy entropy and Laplacian score to extract the fault signatures of bearings. Zhou et al [16] proposed a neighborhood component analysis based feature extraction approach. Liu et al [17] proposed a method that combines Hilbert Huang transform (HHT) and singular value decomposition (SVD) to obtain the fault features of bearings.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

et al. 2018

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Fault diagnosis of rolling bearings is important for ensuring the safe operation of industrial machinery. How to effectively extract the fault features and select a classifier with high precision is the key to realizing the fault recognition of bearings. Accordingly, a new fault diagnosis method of rolling bearings based on improved fast spectral correlation and optimized random forest (i.e., particle swarm optimization-random forest (PSO-RF)) is proposed in this paper. The main contributions of this study are made from two aspects. One is that an improved fast spectral correlation approach was developed to extract the fault features of bearings and form the feature vector more effectively. The other is that an optimized random forest classifier was developed to achieve highly accurate identification by exploiting particle swarm optimization to select the best parameters of random forest (RF). In the presented method, improved fast spectral correlation was first utilized to analyze the raw vibration signal caused by a faulty bearing to obtain the enhanced envelope spectrum. Then, the amplitudes of the four characteristic cyclic frequencies (i.e., the rotating frequency, the characteristic frequency of outer-race fault, the characteristic frequency of inner-race fault, and the characteristic frequency of rolling element fault) exhibited in the enhanced envelope spectrum were selected to form the feature vector. Finally, the PSO-RF method was introduced for identifying and classifying bearing faults. The experimental investigations demonstrate the proposed method can accurately identify bearing faults and outperform other state-of-art techniques considered.

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Section: Introductionmentioning

confidence: 99%

“…,d, it can be seen that the vibration signals of inner-fault, outer-fault and rolling element fault have obvious impacts. x FOR PEER REVIEW16 The classification results of (a) ELM; (b) SVM.…”

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confidence: 99%

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

et al. 2018

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“…He et al [14] developed a wavelet filter for early detection of faults occurring in fan bearings and to assess their fault severity. Zhou et al [15] used neighborhood component analysis and coupled HMM (CHMM) for bearing fault recognition. However, in machine running processes, the operation condition drift and/or shift often happens as a result of the following: different locations of sensors, preventive and corrective maintenance, and running condition changes of machines.…”

Section: Introductionmentioning

confidence: 99%

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

2017

Mechanical Systems and Signal Processing

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“…Many pattern recognition methods have been employed for mechanical fault diagnosis, such as artificial neural network (ANN) [30], support vector machine (SVM) [31][32][33], Bayesian classifiers [34], and hidden Markov model (HMM) [35]. SVM is a popular machine learning method based on statistical learning theory [36], which employs structural risk minimization principle, and the problems of overfitting, local minimum, and slow convergence speed can be overcome compared with neural network.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Chen

Dong

2017

Mathematical Problems in Engineering

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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.

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Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model

Cited by 76 publications

References 21 publications

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

Fault Diagnosis of Rolling Bearings Based on Improved Fast Spectral Correlation and Optimized Random Forest

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

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

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