Intelligent Fault Classification of Rolling Bearing at Variable Speed Based on Reconstructed Phase Space

In this paper, the authors propose a new technique for the classification of seizures, non-seizures, and seizure-free EEG signals based on non-linear trajectories of EEG signals. The EEG signals are decomposed using the EMD technique to obtain intrinsic mode functions (IMFs). The phase space of these IMFs is then reconstructed using a novel technique of higher-order dimensions (3D, 4D, 5D, 6D, 7D, 8D, 9D, and 10D). The existing techniques of seizure detection have deployed 2D & 3D phase–space reconstruction only. The Euclidean distance of all higher-order PSR is used as a feature to classify seizures, non-seizures, and seizure-free EEG signals. The performance of the proposed method is analyzed on the Bonn University database in which 7D reconstructed phase space classification accuracy of 99.9% has been achieved both using Random Forest classifier and J48 decision tree.

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Fault Classification of Rolling Bearing Based on Time-Frequency Generalized Dimension of Vibration Signal and ANFIS

The Open Mechanical Engineering Journal ◽

10.2174/1874155x01408010861 ◽

2014 ◽

Vol 8 (1) ◽

pp. 861-864 ◽

Cited By ~ 1

Author(s):

Fang Li

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Fault Classification ◽

Time Frequency ◽

Generalized Dimension

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Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM

Mathematical Problems in Engineering ◽

10.1155/2021/2636302 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Jie Ma ◽

Shitong Liang ◽

Zhengyu Du ◽

Ming Chen

Keyword(s):

Fault Diagnosis ◽

Rolling Bearing ◽

Fault Classification ◽

Training Set ◽

Sample Data ◽

Diagnosis Method ◽

Main Fault ◽

Bp Model ◽

Fault Information

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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