Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

Zhang, Jianmin; Guo, Huijuan; Zhang, Long; Xu, Qingyao; Li, Hui

doi:10.1155/2016/3086454

Cited by 19 publications

(13 citation statements)

References 29 publications

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“…The updating strategy for r 1 is defined as follows and changes of r 1 for individuals of different subgroups is shown in Figure 3b: (1) Elite individuals: Are the best individuals among all ones, and close to the optimal value. A smaller r 1 is given with a cubic function to enhance local searching: (27) where r max and r min are the maximum and minimum value of r 1 respectively, while T and t are the maximum and current number of iterations respectively.…”

Section: Adaptive Sine Cosine Algorithmmentioning

confidence: 99%

“…Correspondingly, SVDD is a kernel method inspired by the idea of SVM theory [25], which possesses a prominent recognition ability through establishing a minimum hypersphere that contains as many samples as possible [26]. Currently, combined with relative distance decision strategy, SVDD has been successfully employed in the field of pattern recognition [27,28]. However, traditional relative distance decision strategy is difficult to accurately classify unknown samples locating in overlap regions of the hyperspheres and regions outside all the hyperspheres.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Tan

Wang

et al. 2019

Applied Sciences

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Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability.

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Section: Adaptive Sine Cosine Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Tan

Wang

et al. 2019

Applied Sciences

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show abstract

“…The IMF component selected in this paper is greater than 0.5, which is IMF1. The Hilbert transform is performed on the component, and then the transformed signal is Fourier transformed to obtain an envelope spectrum [45].…”

Section: Envelope Spectrum Analysismentioning

confidence: 99%

“…The experimental data which was collected by using the bearing fatigue life test bench is obtained from the Intelligent Maintenance System Center of the University of Cincinnati, USA [37]. The test bench is shown in Figure 9: The experimental data which was collected by using the bearing fatigue life test bench is obtained from the Intelligent Maintenance System Center of the University of Cincinnati, USA [37]. The test bench is shown in Figure 9: The test bench spindle is equipped with four double row roller Rexnord ZA-2115 bearings.…”

Section: Test Bench Introductionmentioning

confidence: 99%

Evaluation of Rolling Bearing Performance Degradation Using Wavelet Packet Energy Entropy and RBF Neural Network

et al. 2019

Self Cite

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Rolling bearings are the most important parts in rotating machinery, and one of the most vulnerable parts to failure. The rolling bearing is a cyclic symmetrical structure that is stable under normal operating conditions. However, when the rolling bearing fails, its symmetry is destroyed, resulting in unstable performance and causing major accidents. If the performance of rolling bearings can be monitored and evaluated in real time, maintenance strategies can be implemented promptly. In this paper, by using wavelet packet energy entropy (WPEE), the early fault-free features of bearing and the failure samples of similar bearings are decomposed firstly, and the energy value is extracted as the original feature, simultaneously. Secondly, a radial basis function (RBF) neural network model is established by using early fault-free features and similar bearing failure characteristics. The bearing full-life data characteristics of the extracted features are added into the RBF model in an iterative manner to obtain performance degradation Indicator. Boxplot was introduced as an adaptive threshold method to determine the failure threshold. Finally, the results are verified by empirical mode decomposition and Hilbert envelope demodulation. A bearing accelerated life experiment is performed to validate the feasibility and validity of the proposed method. The experimental results show that the method can diagnose early fault points in time and evaluate the degree of bearing degradation, which is of great significance for industrial practical applications.

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“…Finally, the degradation degree could be reflected by degradation indicator. Zhou et al [13] proposed a degradation assessment method based on wavelet entropy and support vector data description (SVDD) to establish the degradation model of bearings. Firstly, the SVDD model was trained by the energy entropy of vibration signal decomposed by wavelet packet in normal state, and then the relative distance between the energy entropy of test sample to the hypersphere was taken as the quantitative index of bearing degradation assessment.…”

Section: Introductionmentioning

confidence: 99%

A Bearing Performance Degradation Modeling Method Based on EMD‐SVD and Fuzzy Neural Network

Gai

Shen

2019

Shock and Vibration

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Bearing performance degradation assessment has great significance to condition-based maintenance (CBM). A novel degradation modeling method based on EMD-SVD and fuzzy neural network (FNN) was proposed to identify and evaluate the degradation process of bearings in the whole life cycle accurately. Firstly, the vibration signals of bearings in known states were decomposed by empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs) containing feature information. Then, the selected key IMFs which contain the main features were decomposed by singular value decomposition (SVD). And the decomposed results were used as the training samples of FNN. At last, the output results of the tested data were normalized to the health index (HI) through learning and training of FNN, and then the performance degradation degree could be described by the distance between the test sample and the normal one. According to the case study, this modeling method could evaluate the performance degradation of bearings effectively and identify the early fault features accurately. This method also provided an important maintenance strategy for the CBM of bearings.

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Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

Cited by 19 publications

References 29 publications

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Evaluation of Rolling Bearing Performance Degradation Using Wavelet Packet Energy Entropy and RBF Neural Network

A Bearing Performance Degradation Modeling Method Based on EMD‐SVD and Fuzzy Neural Network

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