Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description

2009 ◽  
Vol 23 (3) ◽  
pp. 669-681 ◽  
Author(s):  
Yuna Pan ◽  
Jin Chen ◽  
Lei Guo
Keyword(s):  
Wavelet Packet ◽  
Assessment Method ◽  
Performance Degradation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description

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

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianmin Zhou ◽  
Huijuan Guo ◽  
Long Zhang ◽  
Qingyao Xu ◽  
Hui Li
Keyword(s):  
Wavelet Packet ◽  
Assessment Method ◽  
Performance Degradation ◽  
Data Repository ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Mode Decomposition ◽  
Lifting Wavelet ◽  
Envelope Spectrum

Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

Structure Health Monitoring Using Support Vector Data Description and Wavelet Packet Energy Distributions

2011 ◽  
Vol 135-136 ◽  
pp. 930-937
Author(s):  
Chen Dong Duan ◽  
Yi Yan Liu ◽  
Qiang Gao
Keyword(s):  
Health Monitoring ◽  
Wavelet Packet ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Distributions ◽  
Data Description ◽  
Structural Health ◽  
Ideal Candidate ◽  
Monitoring And Diagnostics

A new monitoring and diagnostics method using support vector data description (SVDD) is proposed which only needs samples under healthy condition. The method is an ideal candidate for coping with the problem of a shortage of the unhealthy condition samples. We firstly select several nodes of the monitored structure, and decompose the signals from these nodes with wavelet packet transform (WPT). To monitoring structural health efficiently, we assemble a combine feature by using wavelet packet energy distributions of these nodes. The feature is then applied as the input of a developed SVDD classifier. Experiment shows that the SVDD classifier was able to distinguish the normal and abnormal condition ideally, and can be used as an automation approach for structural health monitoring.

A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means

2009 ◽  
Vol 223 (11) ◽  
pp. 2687-2695 ◽  
Author(s):  
Y N Pan ◽  
J Chen ◽  
G M Dong
Keyword(s):  
Hybrid Model ◽  
Normal State ◽  
Performance Degradation ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Fuzzy C Means ◽  
Kernel Parameter

Bearing performance degradation assessment is more effective than fault diagnosis to realize condition-based maintenance. In this article, a hybrid model is proposed for it based on a support vector data description (SVDD) and fuzzy c-means (FCM). SVDD, which holds excellent robustness to outliers, is used to obtain the clustering centre of normal state. The subjection of tested data to normal state is defined as a degradation indicator, which is computed by a FCM algorithm with final failure data. The results of applying this hybrid model to an accelerated bearing life test show that it can effectively assess bearing performance degradation. Furthermore, it is robust to the outliers in the training set and is not influenced by the Gaussian kernel parameter.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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