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Journal of Engineering Studies and Research , 2011,
Abstract: The present study is an assessment to assign reference 2309CK bearings installed on a centrifugal fan from States expressing a (probability) level of degradation from "Good" to "Degraded". We seek to model the dynamics of change of state with a homogeneous Markov chain. In addition, it is possible to use the transition matrix associated to Markov chain to an operation analysis for the determination of the rotating machine reliability. The proposed model (transition matrix or graphic form) allows to know the turnover probability throughout life degradation.
Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine  [PDF]
Shuen-De Wu,Po-Hung Wu,Chiu-Wen Wu,Jian-Jiun Ding,Chun-Chieh Wang
Entropy , 2012, DOI: 10.3390/e14081343
Abstract: Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).
Prediction of protein binding sites in protein structures using hidden Markov support vector machine
Bin Liu, Xiaolong Wang, Lei Lin, Buzhou Tang, Qiwen Dong, Xuan Wang
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-381
Abstract: In this study, we introduce a novel machine learning model hidden Markov support vector machine for protein binding site prediction. The model treats the protein binding site prediction as a sequential labelling task based on the maximum margin criterion. Common features derived from protein sequences and structures, including protein sequence profile and residue accessible surface area, are used to train hidden Markov support vector machine. When tested on six data sets, the method based on hidden Markov support vector machine shows better performance than some state-of-the-art methods, including artificial neural networks, support vector machines and conditional random field. Furthermore, its running time is several orders of magnitude shorter than that of the compared methods.The improved prediction performance and computational efficiency of the method based on hidden Markov support vector machine can be attributed to the following three factors. Firstly, the relation between labels of neighbouring residues is useful for protein binding site prediction. Secondly, the kernel trick is very advantageous to this field. Thirdly, the complexity of the training step for hidden Markov support vector machine is linear with the number of training samples by using the cutting-plane algorithm.Identification of protein binding site has significant impact on understanding protein function. Development of fast and accurate computational methods for protein binding site prediction is helpful in identifying functionally important amino acid residues and facilitating experimental efforts to catalogue protein interactions. It also plays a key role in enhancing computational docking studies, drug design and functional annotation for the structurally determined proteins with unknown function [1].Protein binding site prediction has been studied for decades [2-4]. Several machine learning methods have been applied in this field. These methods can be split into two groups: classificati
Multi-Scale Analysis Based Ball Bearing Defect Diagnostics Using Mahalanobis Distance and Support Vector Machine  [PDF]
Shuen-De Wu,Chiu-Wen Wu,Tian-Yau Wu,Chun-Chieh Wang
Entropy , 2013, DOI: 10.3390/e15020416
Abstract: The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE), multi-scale permutation entropy (MPE), multi-scale root-mean-square (MSRMS) and multi-band spectrum entropy (MBSE). Some of the features are then selected as the inputs of the support vector machine (SVM) classifier through the Fisher score (FS) as well as the Mahalanobis distance (MD) evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU) are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.
A New and Effective Method of Bearing Fault Diagnosis Using Wavelet Packet Transform Combined with Support Vector Machine  [cached]
Yun-jie Xu,Shu-dong Xiu
Journal of Computers , 2011, DOI: 10.4304/jcp.6.11.2502-2509
Abstract: After briefly analyzing past research, by wavelet packet transform with support vector machine (SVM), a new method of bearing fault diagnosis is presented. Wavelet packets have greater decor relation properties than standard wavelets in that they induce a finer partitioning of the frequency domain of the process generating the data. we analyze the vibration features of testing signals of a bearing system in different running conditions by wavelet de-noising with thresholds; we decompose the feature signals into different frequency bands with the wavelet packet transform (WPT) and then calculate the energy percentage of every frequency band component to obtain its fault detection index used for fault diagnosis by the support vector machine (SVM). We analyze the vibration features of testing signals of a bearing system in different running conditions by wavelet de-noising with thresholds; and to decompose the feature signals into different frequency bands with the wavelet packet transform (WPT), through wavelet packet transform to obtain wavelet coefficients and then Energy eigenvector of frequency domain are extracted by using Shannon entropy principle. Subsequently, the extracted Energy eigenvector of frequency domain are applied as inputs to support vector machine(SVM)for bearing from internal fault. Fault state of bearing is identified by using radial basis function genetic-support vector machine. What is worth mentioning in particular is that our method can also effectively diagnose compound faults.
Pattern Classification for Rolling Bearing Based on α-Stable Distribution and Support Vector Machine

- , 2015, DOI: 10.16450/j.cnki.issn.1004-6801.2015.06.011
Abstract: 针对滚动轴承发生故障时振动信号表现出来的脉冲特性,提出了一种基于〖WTBX〗α〖WTBZ〗稳定分布和支持向量机的模式分类方法。介绍了〖WTBX〗α〖WTBZ〗稳定分布的定义和概率密度函数,并与故障轴承振动信号的概率密度函数曲线进行比较,证明了具有脉冲特性的轴承振动信号符合〖WTBX〗α〖WTBZ〗稳定分布。用小波包分解技术对不同类型的轴承实测数据进行分解,并提取相应特征参数作为特征向量,建立支持向量机诊断模型,进行特征模式分类。通过与传统的基于峭度和方差的模式分类方法进行比较,表明该方法具有较高的诊断准确性。
A pattern classification method based on α-stable distribution and support vector machine (SVM) is proposed, aiming at the extraction of the pulse characteristics of vibration signals from rolling bearings with faults. First, the α-stable distribution is defined, and its probability distribution function (PDF) is introduced and compared with the PDF of the vibration signals from the faulted bearing. The comparison results confirm that the vibration signals with pulse characteristics agree with the α-stable distribution. Then, the measured signals from different bearings are decomposed by wavelet packet decomposition, and the relevant characteristics parameters are computed and selected as eigenvectors that can be used to classify feature patterns based on SVM. Comparing the presented method with the traditional method illustrates its better classification performance.
Protein Structure Prediction Using Support Vector Machine  [PDF]
Anil Kumar Mandle,Pranita Jain,Shailendra Kumar Shrivastava
International Journal on Soft Computing , 2012,
Abstract: Support Vector Machine (SVM) is used for predict the protein structural. Bioinformatics method use to protein structure prediction mostly depends on the amino acid sequence. In this paper, work predicted of 1-D, 2-D, and 3-D protein structure prediction. Protein structure prediction is one of the most important problems in modern computation biology. Support Vector Machine haves shown strong generalization ability protein structure prediction. Binary classification techniques of Support Vector Machine are implemented and RBF kernel function is used in SVM. This Radial Basic Function (RBF) of SVM produces better accuracy in terms of classification and the learning results.
Design of A Center Deviation Adaptive Bearing Pressure Assembly Machine
Guangguo Zhang,Lei Zhang,Zhibin Chang
TELKOMNIKA : Indonesian Journal of Electrical Engineering , 2012, DOI: 10.11591/telkomnika.v10i4.859
Abstract: A car transmission in order to solve the production of eight gear and nine file two models of the transmission of tapered roller bearing outer ring manual assembly accuracy, low noise, the efficiency is low, the labor intensity and difficult to realize automatic assembly line requirements so assembly problem, put forward the center deviation adaptive bearing pressure assembly machine development. Through the research, design, test and making, eventually developed used in automobile gearbox bearing pressure assembly machine. Therefore the automatic production is achieved,which improves the production efficiency , ensures the assembly quality, and brings higher profit to the enterprise.
E. Suresh Kumar,Dhiren kumar Behera,Asis Sarkar
International Journal of Computer Science and Management Studies , 2012,
Abstract: Modern probability theory studies chance processes for which theknowledge of previous outcomes influence predictions for future experiments. In principle, when a sequence of chance experiments, all of the past outcomes could influence the predictions for the next experiment. In Markov chain type of chance, the outcome of a given experiment can affect the outcome of the next experiment. The system state changes with time and the state X and time t are two random variables. Each of these variables can be either continuous or discrete. Various degradation on photovoltaic (PV) systems can be viewed as different Markov states and further degradation can be treated as the outcome of the present state. The PV system is treated as a discrete state continuous time system with four possible outcomes, namely, s1 : Good condition, s2 : System with partial degradation failures and fully operational, s3 : System with major faults and partially working and hence partial output power, s4 : System completely fails. The calculation of the reliability of the photovoltaic system is complicated since the system have elements or subsystems exhibiting dependent failures and involving repair and standby operations. Markov model is a better technique that has much appeal and works well when failure hazards and repair hazards are constant. The usual practice of reliability analysis techniques include FMEA((failure mode and effect analysis), Parts count analysis, RBD ( reliability block diagram ), FTA( fault tree analysis ) etc. These are logical, boolean and block diagram approaches and never accounts the environmental degradation on the performance of the system. This is too relevant in the case of PV systems which are operated under harsh environmental conditions. This paper is an insight into the degradation of performance of PV systems and presenting a Markov model of the system by means of the different states and transitions between these states.
Structural health monitoring of a cantilever beam using support vector machine
Satish B Satpal, Yogesh Khandare, Anirban Guha and Sauvik Banerjee
International Journal of Advanced Structural Engineering , 2013, DOI: 10.1186/2008-6695-5-2
Abstract: In this article, the effectiveness of support vector machine (SVM) is examined for health monitoring of beam-like structures using vibration-induced modal displacement data. The SVM is used to predict the intensity or location of damage in a simulated cantilever beam from displacements of the first mode shape. Twelve levels of damage intensities have been simulated at 12 locations, and six levels of white Gaussian noise have been added, thereby obtaining 1,008 simulations. About 90% of these are used for training the SVM, and the remaining are used for testing. The trained SVM is able to predict damage intensity and location of all the training set data with nearly 100% accuracy. The test set data reveal that SVM is able to predict damage intensity and damage location with errors varying from 0.28% to 4.57% and 0% to 20.3%, respectively, when there is no noise in the data. Addition of noise degrades the performance of SVM, the degradation being significant for intensity prediction and less for damage location prediction. The results demonstrate the use of SVM as a powerful tool for structural health monitoring without using the data of healthy state.
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