Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions

Zheng, Zhi; Jiang, Wanlu; Wang, Zhenwei; Zhu, Yong; Yang, Kai

doi:10.1016/j.mechmachtheory.2015.04.009

Cited by 50 publications

(31 citation statements)

References 33 publications

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“…The self-similarity of signal refers to exactly or approximately similar to a part of itself and it is a typical property of fractals. In engineering, many kinds of signals are a fractal system with statistic self-similarity, either global or local self-similarity [23][24][25][26]. It is a solution based on waveform matching and makes the extension meet the trend of original signals as far as possible to maintain the inner rules or characteristic.…”

Section: The Boundary Processing Methodsmentioning

confidence: 99%

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

Shi

Song

Taheri

2016

Entropy

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Abstract:A novel bearing vibration signal fault feature extraction and recognition method based on the improved local mean decomposition (LMD), permutation entropy (PE) and the optimized K-means clustering algorithm is put forward in this paper. The improved LMD is proposed based on the self-similarity of roller bearing vibration signal extending the right and left side of the original signal to suppress its edge effect. After decomposing the extended signal into a set of product functions (PFs), the PE is utilized to display the complexity of the PF component and extract the fault feature meanwhile. Then, the optimized K-means algorithm is used to cluster analysis as a new pattern recognition approach, which uses the probability density distribution (PDD) to identify the initial centroid selection and has the priority of recognition accuracy compared with the classic one. Finally, the experiment results show the proposed method is effectively to fault extraction and recognition for roller bearing.

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Section: The Boundary Processing Methodsmentioning

confidence: 99%

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

Shi

Song

Taheri

2016

Entropy

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“…Assuming X is a not empty bounded subsets of R n , and N(X, ε) represents the minimum number of the subset that can cover X set with the maximum diameter ε. The definition of fractal box dimension is expressed as follows [16]:…”

Section: Fractal Box Dimensionmentioning

confidence: 99%

“…The connection weight β can be calculated according to Eqs. (16) to (20), and the training process of ELM is completed. The number of hidden neurons and the type of activation function have a large influence on the application of ELM, and in general, the activation function can be selected from 'sig' function, 'sin' function or 'hardlim' function.…”

Section: Fig 9 Decomposition Errors Of Eemd and Ceemdmentioning

confidence: 99%

Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM

Chen¹,

Li²,

Li³

2017

SV-JME

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The planetary gear is the most critical part of a drive transmission system, and its faults will affect the reliability of the equipment, and even cause accidents. Therefore, it is of great significance to study the fault diagnosis of the planetary gear. A method of planetary gear fault diagnosis based on the multi-scale fractal box dimension of complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM) is proposed. The original vibration signal is decomposed by CEEMD, and a series of intrinsic mode functions (IMFs) are obtained. Some effective IMFs are extracted, and their reconstructed signal associated with the feature information is obtained. The reconstructed signal is analysed with multi-scale analysis, and the fault feature information contained in the signals with different scales is quantified and extracted via a fractal box dimension. The status recognition of planetary gear is achieved by combining ELM. The experiments show that the proposed method is effective at diagnosing planetary gear faults. Highlights • A method of planetary gear fault diagnosis based on the multi-scale fractal box dimensions of CEEMD and ELM is proposed.• The effective IMFs which associate with the shock feature information are selected by the kurtosis criterion.• The multi-scale fractal box dimension of the reconstructed signal is defined as the fault feature information.• The status recognition of a planetary gear is achieved by combining ELM.

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“…The sparse MKL-ELM constrained ℓ 1 norm is given by = 1 in (14). The optimal parameter of * and * is calculated by iterative optimization methods.…”

Section: Multikernel Extreme Learning Machine (Mkl-elm)mentioning

confidence: 99%

“…The research results have provided effective theoretical support for accident prevention and pipeline maintenance and greatly reduced maintenance costs. In recent years, wavelet transform and Fourier transform, information entropy, neural network, bispectrum analysis, feature fusion, evidence theory, chaos theory, fractal theory, decision tree, and SVM have been widely applied to the fault diagnosis of reciprocating machinery, and many significant research achievements have been obtained [7][8][9][10][11][12][13][14][15][16]. Yet compared with the fault diagnosis of rotating machinery, there are still many research 2 Complexity contents to be improved: (1) the data sample size of reciprocating machinery is huge and a great deal of multisource heterogeneous information is held within them due to the influence of complex structure, multiple excitation source, multiple wearing parts, coupling of the signal, and strong nonlinearity of reciprocating machinery.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine

Wang

2017

Complexity

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Check valve is one of the most important components and most easily damaged parts in high pressure diaphragm pump, which is a typical representative of reciprocating machinery. In order to ensure the normal operation of the pump, it is necessary to monitor its running state and diagnose fault. However, in the fault diagnosis of check valve, the classification models with single kernel function can not fully interpret the classification decision function, and meanwhile unreasonable assumption of diagnostic cost equalization has a significant impact on classification results. Therefore, the multikernel function and cost-sensitive mechanism are introduced to construct the fault diagnosis model of check valve based on the multikernel cost-sensitive extreme learning machine (MKL-CS-ELM) in this paper. The comparative test results of check valve for high pressure diaphragm pump show that MKL-CS-ELM can obtain fairly or slightly better performance than ELM, CS-ELM, MKL-ELM, and multikernel cost-sensitive support vector learning machine (MKL-CS-SVM). At the same time, the presented method can obtain very high accuracy under imbalance datasets condition and effectively overcome the weakness of diagnostic cost equalization and improve the interpretability and reliability of the decision function of classification model. It, therefore, is more suitable for the practical application.

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Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions

Cited by 50 publications

References 33 publications

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings

Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM

Fault Diagnosis Method of Check Valve Based on Multikernel Cost-Sensitive Extreme Learning Machine

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