Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

Yu, Xiao; Dong, Fangmin; Ding, Enjie; Wu, Shoupeng; Fan, Chun‐Yang

doi:10.1109/access.2017.2773460

Cited by 132 publications

(70 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results demonstrate that the proposed method is feasible and effective in bearing fault diagnosis. 100.00 [34] Note: local characteristic-scale decomposition (LCD); support vector machine-binary tree (SVM-BT); multiclass relevance vector machines (M-RVM); recurrence Quantification Analysis (RQA); Mahalanobis distance (MD); dual tree complex wavelet packet transform (DTCWPT); improved multiscale permutation entropy (IMPE); linear local tangent space alignment (LLTSA); extreme learning machine (ELM); support margin-local fisher discriminant analysis (SM-LFDA); grey relation pattern recognition (GRPR); multiscale root-mean-square(MSRMS); multiband spectrum entropy (MBSE).…”

Section: Resultsmentioning

confidence: 99%

“…Though the result of the proposed approach could not reach 100% like in [34] with 12 classified states as well, the proposed approach avoids the problem of feature selection and parameter optimization of SVM. Compared with the remaining researches, the proposed approach could deal with more classified states with high accuracy.…”

Section: Artificially Seeded Damagementioning

confidence: 98%

See 1 more Smart Citation

Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering‐Enhanced Multiscale Entropy Features

Zhang

Zhao

Li³

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To improve the bearings diagnosis accuracy considering multiple fault types with small samples, a new approach that combined adaptive local iterative filtering (ALIF), multiscale entropy features, and kernel sparse representation classification (KSRC) is put forward in this paper. ALIF is used to adaptively decompose the nonlinear, nonstationary vibration signals into a sum of intrinsic mode functions (IMFs). Multiple entropy features such as sample entropy, fuzzy entropy, and permutation entropy with multiscale are computed from the first three IMFs and a total of one hundred and eighty features are obtained. After normalization, the features are employed to train and test the classifier KSRC, respectively. Finally, the proposed approach is evaluated with two experimental tests. One is concerned with different types of bearing faults from the centrifugal pump; and the other is from Case Western Reserve University (CWRU) considering 12 bearing fault states. Experimental results have proved that the proposed approach is efficient for bearing fault diagnosis, and high accuracy will be obtained with high dimensional features through small samples.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Artificially Seeded Damagementioning

confidence: 98%

Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering‐Enhanced Multiscale Entropy Features

Zhang

Zhao

Li³

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Although oil analysis and acoustic emission signal analysis have unique advantages for strong background noise and early bearing failures, their application are limited due to expensive price. Therefore, vibration signal analysis plays an important role in bearing fault diagnosis, which is mainly divided into two parts, including time domain analysis [5,6] and frequency domain analysis [7]. However, the time domain analysis can only roughly reflect whether the mechanical equipment is normal or not and cannot provide detailed information (fault type, fault location, and fault severity) about the bearing.…”

Section: Introductionmentioning

confidence: 99%

Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

et al. 2020

View full text Add to dashboard Cite

Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.

show abstract

“…In the field of bearing fault diagnosis, many techniques were applied in symptom parameter selection under filterbased, wrapper-based, and hybrid methods. Yu et al [14] proposed features selection by adjusting the rand index and standard deviation ratio (FSASR) with the K-means method and standard deviation (STD) to select the sensitive statistical characteristics of bearing fault signal. Meng et al [15] applied the binary value of the gravitational search algorithm (BGSA) to find the essential features from the feature set.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Zuo

Liao

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

show abstract

Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection

Cited by 132 publications

References 53 publications

Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering‐Enhanced Multiscale Entropy Features

Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering‐Enhanced Multiscale Entropy Features

Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Contact Info

Product

Resources

About