Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine

Li, Yongbo; Yang, Yuantao; Wang, Xianzhi; Liu, Binbin; Liang, Xun

doi:10.1016/j.jsv.2018.04.036

Cited by 146 publications

(90 citation statements)

References 26 publications

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“…(8) After the mutant fruit flies were added to the population, formula (10) was used to select Swarmsize fruit flies to form a new population. K fruit flies were inoculated with the immune vaccine generated in step (5), and the immune selection was conducted according to the odor concentration value of individual fruit flies before and after vaccination. If the odor concentration value decreases, the corresponding individual will be retained; otherwise, the vaccine will be cancelled.…”

Section: Immune Fruit Fly Optimization Algorithmmentioning

confidence: 99%

“…It is useful to diagnose the type of fault and the fault location before gearbox out of action. So, composite fault extraction of the gearbox is critical [4][5][6]. At present, the methods used in gearbox fault extraction include Wavelet transform (WT), Empirical mode decomposition (EMD), Local mean decomposition (LMD), and Ensemble empirical mode decomposition (EEMD), these methods successfully extract the fault information, but they will show their own weaknesses when extracting composite faults [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

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In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

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Section: Immune Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

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show abstract

“…According to the definition of conditions, MMSE values of scale factor τ between 3 and 10 should be selected as a sensitive feature vector subset. Further evaluation is needed to assess the effectiveness of the proposed method based on the MMSE and GDE method, and this paper introduces the binary tree support vector machine (BTSVM) method [26] to [28] to evaluate four valve fault eigenvectors. To test this, 150 eigenvector samples were selected from each valve fault, and 100 were taken as training samples, other 50 as test samples.…”

Section: Fault Diagnosis For Reciprocating Compressor Valve Based Onmentioning

confidence: 99%

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Li¹,

Wang²,

Zhao³

et al. 2019

SV-JME

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According to the nonlinearity, non-stationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of a reciprocating compressor valve based on modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. First, the variational mode decomposition (VMD) method with superior anti-interference performance was utilized to analyse the strong non-stationarity vibration signals for all fault states. The modified multiscale entropy (MMSE) method provided for movingaverage procedures by replacing mean-average coarse-grained procedures was developed for the vibration signals after de-noising, and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and to select the optimal sensitivity scale feature. Finally, a binary tree of support vector machine (BTSVM) was selected as the classifier to identify the reciprocating compressor valve fault type. By analysing the experimental data, it can be shown that the method can effectively identify the fault type of the reciprocating compressor valve. Keywords: fault diagnosis, reciprocating compressor valve, modified multiscale entropy, global distance evaluation, binary tree of support vector machine Highlights • An integrated fault feature extraction method of a reciprocating compressor valve based on the VMD-MMSE and GDE is proposed. • A consistent number K of band-limited intrinsic mode functions (BLIMFs) were selected based on a novel criterion for all fault states • The MMSE method provided for the moving-average procedure by replacing mean-average coarse-grained procedure was developed for the vibration signals, GDE was introduced to refine the eigenvectors for higher recognition efficiency and accuracy. • The effectiveness of this method is verified by the recognition results of BTSVM in comparison to other feature extraction methods.

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“…Studies on bearing-fault classification differ in two ways. The first type of studies covers different signal-processing techniques for the classification of bearing faults [7] and [13] or for feature extraction and selection from vibrational data, which are then used to enhance the results of an applied classification method [14]. Other studies mostly utilize the different classification methods to obtain better classification results [6] and [15].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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Early fault diagnosis of rolling bearings based on hierarchical symbol dynamic entropy and binary tree support vector machine

Cited by 146 publications

References 26 publications

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

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