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Modeling of the safe region based on support vector data description for health assessment of wheelset bearings

APPLIED MATHEMATICAL MODELLING 2019年 73卷 19-39页

作者： Liu, Zhiliang Kang, Jinlong Zhao, Xuejun Zuo, Ming J. Qin, Yong Jia, Limin Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Alberta Dept Mech Engn Edmonton AB T6G 1H9 Canada

This paper presents a modeling method, an optimization method, and two applications for the safe region concept that has been developed for the health assessment of wheelset bearings of high-speed trains. The proposed safe region model uses support vector data description to handle cases in high-speed trains where only normal data are available. The optimization process is designed with a strong physical interpretation of the relationship between the kernel parameter and the model performance. A new indicator derived from the safe region model is proposed for degradation evaluation throughout the whole life cycle. Experimental data on wheelset bearings with naturally generated faults are used to demonstrate that the proposed approach has competitive advantages in several performance aspects for anomaly detection and provides monotonic labels in assessment of increasing degradation levels. (C) 2019 Published by Elsevier Inc.

关键词： Safe region support vector data description Anomaly detection Degradation evaluation Wheelset bearing

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A fast gene selection method for multi-cancer classification using multiple support vector data description

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JOURNAL OF BIOMEDICAL INFORMATICS 2015年 53卷 381-389页

作者： Cao, Jin Zhang, Li Wang, Bangjun Li, Fanzhang Yang, Jiwen Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210000 Jiangsu Peoples R China

For cancer classification problems based on gene expression, the data usually has only a few dozen sizes but has thousands to tens of thousands of genes which could contain a large number of irrelevant genes. A robust feature selection algorithm is required to remove irrelevant genes and choose the informative ones. support vector data description (SVDD) has been applied to gene selection for many years. However, SVDD cannot address the problems with multiple classes since it only considers the target class. In addition, it is time-consuming when applying SVDD to gene selection. This paper proposes a novel fast feature selection method based on multiple SVDD and applies it to multi-class microarray data. A recursive feature elimination (RFE) scheme is introduced to iteratively remove irrelevant features, so the proposed method is called multiple SVDD-RFE (MSVDD-RFE). To make full use of all classes for a given task, MSVDD-RFE independently selects a relevant gene subset for each class. The final selected gene subset is the union of these relevant gene subsets. The effectiveness and accuracy of MSVDD-RFE are validated by experiments on five publicly available microarray datasets. Our proposed method is faster and more effective than other methods. (C) 2014 Elsevier Inc. All rights reserved.

关键词： support vector data description Gene selection Multi-class classification Gene expression data support vector machine

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A K-Farthest-Neighbor-based approach for support vector data description

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APPLIED INTELLIGENCE 2014年第1期41卷 196-211页

作者： Xiao, Yanshan Liu, Bo Hao, Zhifeng Cao, Longbing Guangdong Univ Technol Sch Comp Guangzhou Guangdong Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Guangdong Peoples R China Univ Technol Sydney Adv Analyt Inst Sydney NSW 2007 Australia

support vector data description (SVDD) is a well-known technique for one-class classification problems. However, it incurs high time complexity in handling large-scale datasets. In this paper, we propose a novel approach, named K-Farthest-Neighbor-based Concept Boundary Detection (KFN-CBD), to improve the training efficiency of SVDD. KFN-CBD aims at identifying the examples lying close to the boundary of the target class, and these examples, instead of the entire dataset, are then used to learn the classifier. Extensive experiments have shown that KFN-CBD obtains substantial speedup compared to standard SVDD, and meanwhile maintains comparable accuracy as the entire dataset used.

关键词： support vector data description K-Farthest Neighbors

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Sound Damage Detection of Bridge Expansion Joints Using a support vector data description

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SENSORS 2023年第7期23卷 3564页

作者： Li, Junshi Yang, Caiqian Chen, Jun Xiangtan Univ Sch Civil Engn Xiangtan 411105 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China

A novel method is proposed for the damage identification of modal bridge expansion joints (MBEJs) based on sound signals. Two modal bridge expansion joint specimens were fabricated to simulate healthy and damaged states. A microphone was used to collect the impact signals from different specimens. The wavelet packet energy ratio of the sound signal was used to identify the difference in specimen state. Firstly, the wavelet packet energy ratio was used to establish the feature vectors, which were reduced dimensionality using principal component analysis. Subsequently, a support vector data description model was established to detect the difference in the signals. The identification effects of three parameter optimization methods (particle swarm optimization, genetic algorithm optimization, and Bayesian optimization) were compared. The results showed that the wavelet packet energy ratio of sound signals could effectively distinguish the state of the support bar. The support vector data description of Bayesian optimization worked best, and the proposed method could successfully detect damage to the support bar of MBEJs with an accuracy of 99%.

关键词： modal bridge expansion joint damage detection sound signal wavelet packet energy ratio support vector data description

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Adjustment mode decision based on support vector data description and evidence theory for assembly lines

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INDUSTRIAL MANAGEMENT & data SYSTEMS 2018年第8期118卷 1711-1726页

作者： Lv, Youlong Qin, Wei Yang, Jungang Zhang, Jie Donghua Univ Coll Mech Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

Purpose - Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data. Design/methodology/approach - The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster-Shafer (D-S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory. Findings - The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line. Practical implications - This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope. Originality/value - The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can sol

关键词： Decision making Adjustment modes Dempster-Shafer theory Mixed-model assembly lines support vector data description

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Fault detection based on squirrel search algorithm and support vector data description for industrial processes

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SOFT COMPUTING 2022年第24期26卷 13639-13650页

作者： Alejandro Navarro-Acosta, Jesus Garcia-Calvillo, Irma D. Resendiz-Flores, Edgar O. Univ Autonoma Coahuila Ctr Invest Matemat Aplicadas Unidad Saltillo Saltillo 25280 Coahuila Mexico Tecnol Nacl Mexico IT Saltillo Div Estudios Posgrad & Invest Blvd V Carranza 2400 Col Tecnol Saltillo 25280 Coahuila Mexico

This paper proposes a novel fault detection system by the combination of support vector data description and Squirrel Search Algorithm. This approach is capable to deal with processes or machines where the number of fault observations is small or not even available for training phase. In this work the use of classic support vector data description as well as its fast version with two kernel functions is proposed. The experimental results showed that the proposed system exhibits suitable capabilities for fault detection in complex industrial processes such as the one presented in this research. Moreover, a nonparametric statistical analysis is also included in order to compare the considered strategies and enhance the efficiency of the presented approach. Finally, a comparison with genetic algorithm approach and the one-class classifier based on support vectors is carried out which shows that the proposed algorithm outperforms traditional techniques.

关键词： support vector data description Squirrel Search Algorithm Fault detection One-class classification

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Gompertz binary particle swarm optimization and support vector data description system for fault detection and feature selection applied in automotive pedals components

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第5-8期92卷 2311-2324页

作者： Alejandro Navarro-Acosta, Jesus Resendiz-Flores, Edgar O. Corp Mexicana Invest Mat SA CV COMIMSA Ciencia & Tecnol 790 Col Saltillo 400 Saltillo 25290 Coahuila Mexico Technol Inst Saltillo Div Postgrad Studies & Res Dept Met Mech Engn Blvd V Carranza 2400 Col Tecnol Saltillo 25280 Coahuila Mexico

This work presents an improved fault detection by reference space optimization and simultaneous feature selection applied in a manufacturing complex process of automotive pedals components. support vector data description (SVDD) one-class classification method uses a hypersphere with the minimum volume to find an enclosed boundary containing almost all target objects. Gompertz binary particle swarm optimization algorithm (GBPSO) is applied to optimize kernel hyperparameters for SVDD and simultaneously solve the feature selection problem. In order to justify and validate the results, also the genetic algorithm (GA) and binary particle swarm optimization algorithm (BPSO) are presented to compare the performances of the three approaches in terms of the misclassification function. The experimental results showed that the proposed approach can correctly select the influencing input variables in order to achieve an efficient fault detection.

关键词： Feature selection Fault detection Gompertz binary particle swarm optimization support vector data description

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Batch process monitoring based on functional data analysis and support vector data description

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JOURNAL OF PROCESS CONTROL 2014年第7期24卷 1085-1097页

作者： Yao, Ma Wang, Huangang Xu, Wenli Tsinghua Univ Inst Control Theory & Technol Dept Automat Being 100084 Peoples R China

Since the batch dataset is often organized as a special three-way array, i.e. (batches x variables x time), the research of data-based batch process monitoring has attracted much attention. This paper introduces a novel batch process monitoring framework based on functional data description (FDD), which treats each variable's trajectory varying with time in a batch process as a typical functional datum. Thus, the original batch process data can be transformed into a two-way manner (batches x functions of the variable trajectories) by describing each variable trajectory as a function. The FDD-based batch process monitoring method not only is effective to distinguish the subtle differences in the shape of variables' trajectories between normal batches and faulty batches, but also can easily handle some off-line data preprocessing steps, such as recovery of the missing data and trajectory alignment. Based on FDD, this paper extends a one-class classification method called support vector data description (SVDD) to its functional counterpart, which is called functional SVDD (FSVDD), and further proposes the algorithm of FSVDD-based batch process monitoring. Finally, two case studies, including a simulation example and an industrial semiconductor etch process, are used to illustrate the validity of the proposed FSVDD-based batch process monitoring method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Batch processes data-based process monitoring Functional data analysis support vector data description

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

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2009年第3期23卷 669-681页

作者： Pan, Yuna Chen, Jin Guo, Lei Shanghai Jiao Tong Univ State Key Lab Mechan Syst & Vibrat Shanghai 200240 Peoples R China

Bearing performance degradation assessment is one of the most important techniques in proactive maintenance aiming to realize equipment's near-zero downtime and maximum productivity. In this paper, we propose a new robust method for it based on improved wavelet packet decomposition (IWPD) and support vector data description (SVDD). A health index is designed based on general distance. Node energies of IWPD are used to compose feature vectors. Based on feature vectors extracted from normal signals, a SVDD model fitting a tight hypersphere around them is trained, the general distance of test data to this hypersphere is used as the health index. Research results of its application in a bearing accelerated life test show that this health index can reflect effectively bearing performance degradation comparing with many other parameters. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Performance degradation assessment Improved wavelet packet decomposition support vector data description Bearing accelerated life test

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Belief-based chaotic algorithm for support vector data description

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SOFT COMPUTING 2019年第12期23卷 4289-4314页

作者： Hamidzadeh, Javad Namaei, Neda Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

One of the efficient tools to handle segregation of imbalanced data is support vector data description (SVDD). In contrast to support vector machine (SVM), enclosing target data in a hyper-sphere by SVDD leads to avoid biasing toward major data. SVDD can gain the best description of data when its free parameters are set to proper values. In this paper, we propose belief-based chaotic krill herd algorithm for SVDD (BCKH-SVDD) with the aim of designing effective description of data. First, we introduce a new SVDD based on belief function theory, and then, we tune the free parameters by chaotic krill herd algorithm. Belief function theory is one of the best methods to enhance decision making for uncertain data. By adding a new belief-based weight, we can decide better about the data around the SVDD boundary and the classification will be more precise. Chaotic krill herd optimization algorithm introduces chaotic maps in the krill herd algorithm. With the help of chaotic maps, the two issues, namely local optima avoidance and convergence speed, can be overcome. Thus, chaotic krill herd algorithm is constructed based on chaotic functions and automatic switching between global and local searches of krill herd. To present the power of BCKH-SVDD, several experiments have been conducted based on tenfold cross-validation over real-world data sets from UCI repository. Experimental results show the superiority of the proposed algorithm to state-of-the-art methods in terms of classification accuracy, precision and recall measures.

关键词： One-class classification support vector data description Belief function theory Outlier detection Belief-based chaotic algorithm for SVDD

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