Fault classification of rolling bearing based on LMD-sample entropy and LS-SVM

Aiming at the shortcomings of difficult classification of rolling bearing compound faults and low recognition accuracy, a composite fault diagnosis method of rolling bearing combined with ALIF and KELM is proposed. First, the basic concepts of ALIF and KELM are introduced, and then ALIF is used to decompose the sample data of vibration signals of different bearing states so that each sample can get several IMFs, select the top K IMFs containing the main fault information from each sample, calculate the energy feature and sample entropy of each IMF, and construct a fault feature vector with a dimension of 2K. Finally, the feature vectors of the training set and the test set are input into the KELM model for fault classification. Experimental results show that, compared with EMD-KELM model, ALIF-ELM model, ALIF-BP model, and IFD-KELM model, the rolling bearing composite fault diagnosis method based on the ALIF-KELM model has higher classification accuracy.

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A Novel Multimode Fault Classification Method Based on Deep Learning

Journal of Control Science and Engineering ◽

10.1155/2017/3583610 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 15

Author(s):

Funa Zhou ◽

Yulin Gao ◽

Chenglin Wen

Keyword(s):

Deep Learning ◽

Rolling Bearing ◽

Classification Method ◽

Fault Classification ◽

Classification Models ◽

Classification Result ◽

The Third ◽

Fundamental Step ◽

Fault Severity

Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition. In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result. For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity. The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method.

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Fine-Grained Fault Diagnosis Method of Rolling Bearing Combining Multisynchrosqueezing Transform and Sparse Feature Coding Based on Dictionary Learning

Shock and Vibration ◽

10.1155/2019/1531079 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13

Author(s):

Guodong Sun ◽

Yuan Gao ◽

Kai Lin ◽

Ye Hu

Keyword(s):

Fault Diagnosis ◽

Dictionary Learning ◽

Nonnegative Matrix ◽

Rolling Bearing ◽

Fault Classification ◽

Support Vector ◽

Time Frequency ◽

Fine Grained ◽

Diagnosis Method

To accurately diagnose fine-grained fault of rolling bearing, this paper proposed a new fault diagnosis method combining multisynchrosqueezing transform (MSST) and sparse feature coding based on dictionary learning (SFC-DL). Firstly, the high-resolution time-frequency images of raw vibration signals, including different kinds of fine-grained faults of rolling bearing, were constructed by MSST. Then, the basis dictionary was trained through nonnegative matrix factorization with sparseness constraints (NMFSC), and the trained basis dictionary was employed to extract features from time-frequency matrixes by using nonnegative linear equations. Finally, a linear support vector machine (LSVM) was trained with features of training samples, and the trained LSVM was employed to diagnosis the fault classification of test samples. Compared with state-of-the-art fault diagnosis methods, the proposed method, which was tested on the bearing dataset from Case Western Reserve University (CWRU), achieved the fine-grained classification of 10 mixed fault states. Meanwhile, the proposed method was applied on the dataset from the Machinery Failure Prevention Technology (MFPT) Society and realized the classification of 3 fault states under different working conditions. These results indicate that the proposed method has great robustness and could better meet the needs of practical engineering.

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Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

The Open Fuels & Energy Science Journal ◽

10.2174/1876973x01407010078 ◽

2014 ◽

Vol 7 (1) ◽

pp. 78-83 ◽

Cited By ~ 1

Author(s):

Jiatang Cheng ◽

Li Ai ◽

Zhimei Duan ◽

Yan Xiong

Keyword(s):

Fault Diagnosis ◽

Information Fusion ◽

Evidence Theory ◽

Fault Classification ◽

Fusion Method ◽

Inconsistent Result ◽

High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

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Deep convolutional neural network with new training method and transfer learning for structural fault classification of vehicle instrument panel structure

Journal of Mechanical Science and Technology ◽

10.1007/s12206-020-1009-3 ◽

2020 ◽

Vol 34 (11) ◽

pp. 4489-4498

Author(s):

Sang-Yun Lee ◽

Sang-Kwon Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Deep Convolutional Neural Network ◽

Fault Classification ◽

Instrument Panel ◽

Training Method

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Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽

10.3390/en13051094 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1094 ◽

Cited By ~ 3

Author(s):

Lanjun Wan ◽

Hongyang Li ◽

Yiwei Chen ◽

Changyun Li

Keyword(s):

Neural Network ◽

Working Conditions ◽

Bp Neural Network ◽

Evidence Theory ◽

Prediction Method ◽

Rolling Bearing ◽

Fault Prediction ◽

Fault Classification ◽

Bearing Fault ◽

Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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