Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning

Yao, Beibei; Peng, Zhen; Wu, Lifeng; Guan, Yong

doi:10.1109/access.2017.2693379

Cited by 39 publications

(19 citation statements)

References 12 publications

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“…, c}; otherwise, f i,j = 0 [21], [22]. Then, projection matrix P will be calculated in the low-dimensional manifold [36]- [38], while the other parameters are fixed. The objective function about P is…”

Section: Modified Label Propagation Methodsmentioning

confidence: 99%

Modified Label Propagation on Manifold With Applications to Fault Classification

Xie

2020

IEEE Access

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In process monitoring, fault classification performance heavily relies on the labels of training data. However, the labeled data are inadequate and difficult to obtain because they require experienced human annotators. In this paper, a modified label propagation (MLP) method is proposed to propagate labels from labeled data to unlabeled data. The proposed label propagation algorithm has the following advantages: (1) It constructs a global and local consistency framework with the aid of a data graph, manifold learning, and data labels. This framework follows the assumption that data on the manifold will have similar structures, and nearby data will have similar labels. (2) Considering the inner relationship between the unlabeled data and historical data, a new definition for the initial label matrix is offered, which is significant for label propagation. (3) The new method propagates labels in a low-dimensional manifold space, which is different from most existing label propagation methods that propagate them in the original space. The results reveal that under the global and local consistency framework, soft labels of unlabeled data are given more effective predictions. With additional soft labels of unlabeled data, the MLP-based fault classification method is introduced. The simulation results obtained using a toy example demonstrate the label propagation performance of the MLP, and those obtained for the penicillin fermentation process verify the effectiveness of the MLP-based fault classification method. INDEX TERMS Modified label propagation, manifold learning, fault classification, semi-supervised learning, fisher discriminant analysis.

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Section: Modified Label Propagation Methodsmentioning

confidence: 99%

Modified Label Propagation on Manifold With Applications to Fault Classification

Xie

2020

IEEE Access

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“…The bearing data are taken from the experimental simulation platform of The Case Western Reserve University [25] which it is shown in Fig.4, it consists of a 2 hp motor on the left, a torque transducer in the middle, a load motor on the right, and several control electronics which are not shown. The designation of the experimental bearing is SKF-6205 which is a deep groove ball bearing.…”

Section: A Experimentsmentioning

confidence: 99%

The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

2020

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The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

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“…Manifold learning is a classical nonlinear dimension reduction analysis method for the feature extraction of vibration signals, which can extract the low-dimensional submanifold representing the feature component of the signal from high-dimensional space. The typical dimensionality reduction algorithms include isometric feature mapping (ISOMAP), locally linear embedding (LLE) and local tangent space alignment algorithm (LTSA) [13]. Su et al [14] proposed a multi-fault diagnosis method based on orthogonal supervised LTSA and least square support vector machine for the bearing.…”

Section: Introductionmentioning

confidence: 99%

“…Based on TSVD, the proposed tensor robust principal component analysis (TRPCA) [32] method can provide a favorable noise reduction performance for tensor. According to the manifold learning theory [13], TRPCA believes that the feature component in the attractor tensor is distributed in a low-dimensional submanifold region of the high-dimensional phase space, and this region usually has a low-tubal rank structure. Besides, the noise component in the attractor tensor is generally considered to be sparse.…”

Section: Introductionmentioning

confidence: 99%

Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

2020

IEEE Access

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Rolling Element Bearing Fault Diagnosis Using Improved Manifold Learning

Cited by 39 publications

References 12 publications

Modified Label Propagation on Manifold With Applications to Fault Classification

Modified Label Propagation on Manifold With Applications to Fault Classification

The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

Research on Multichannel Signals Fault Diagnosis for Bearing via Generalized Non-Convex Tensor Robust Principal Component Analysis and Tensor Singular Value Kurtosis

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