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FAULT DIAGNOSIS OF BALL BEARINGS USING soft computing

FAULT DIAGNOSIS OF BALL BEARINGS USING SOFT COMPUTING

引用

ASME international Design Engineering Technical conferences/Computers and Information in Engineering conference

作者： Bhavaraju, Kalyan M. Kankar, P. K. Sharma, Satish C. Harsha, S. P. Indian Inst Technol Dept Mech & Ind Engn Noise & Vibrat Control Lab Roorkee 247667 Uttarkhand India

ISBN: (纸本)9780791848982

This paper presents the condition monitoring and fault diagnosis of rolling element bearings using Support Vector Machines (SVM). The vibration response of healthy bearings and bearings with various component defects such as outer race, inner race, balls and their combination have been analyzed. From the obtained vibration spectrum, it is clearly seen that a discrete peak of excitation appeared for the specific defect of bearings. In this paper, various faults of the bearings has been simulated and classified. The process includes, data acquisition, feature extraction from time response and a knowledge based system to classify faults. Features defining feature vectors are formed using statistical techniques and are fed as input to the support vector machine (SVM) classifiers. Knowledge based system developed for classification can be used for automatic recognition of machinery faults based on feature vector.

关键词： Machinery Rolling bearings Condition monitoring Feature extraction Data acquisition Excitation Ball bearings Vibration Fault diagnosis Support vector machines Bearings Vibrational spectra

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Ubiquitous Intelligence and computing - 7th international conference, UIC 2010, Proceedings

Ubiquitous Intelligence and Computing - 7th International Co...

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7th international conference on Ubiquitous Intelligence and computing, UIC 2010

ISBN: (纸本)3642163548

The proceedings contain 48 papers. The topics discussed include: programming pervasive spaces;the operating system for the computer of the 21st century;extracting social and community intelligence from digital footprints: an emerging research area;smart itinerary recommendation based on user-generated GPS trajectories;inferring user search intention based on situation analysis of the physical world;ontology-enabled activity learning and model evolution in smart homes;support vector machines for inhabitant identification in smart houses;towards non-intrusive sleep pattern recognition in elder assistive environment;the making of a dataset for smart spaces;introduction to the business processes with ambient media - challenges for ubiquitous and pervasive systems;alerting accidents with ambiguity: a tangible tabletop application for safe and independent chemistry experiments;and dependency relation based detection of lexicalized user goals.

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Approximated maximum likelihood bearing estimation based on ant colony algorithm

Approximated maximum likelihood bearing estimation based on ...

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2010 international conference on Web Information Systems and Mining, WISM 2010

作者： Zhai, Hongcun Hou, Yunshan Jin, Yong College of Mathematical Science Luoyang Normal University Luoyang 471022 China College of Marine Northwestern Polytechnical University Xi'an 710072 China Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

ISBN: (纸本)9780769542249

It is well known that Approximated Maximum Likelihood(AML) estimator has the best performance for short time sampling wideband source bearing estimation. But for a long time, the heavy computational load of maximizing the multivariate, highly non-linear likelihood function prevented it from popular use. In this paper, we introduced Ant Colony Algorithm (ACA) to work with AML for computing the exact solutions to the likelihood function with a guarantee of global convergence. The resulted estimator is called Approximated Maximum Likelihood bearing estimator based on Ant Colony Algorithm (ACA-AML). Simulations show that ACA-AML not only reduces the computational complexity greatly but also maintains the excellent performance of the original AML estimator. © 2010 IEEE.

关键词： Maximum likelihood estimation

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Attribute value selection considering the minimum description length approach and feature granularity

Attribute value selection considering the minimum descriptio...

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13th international conference on Information Processing and Management of Uncertainty, IPMU 2010

作者： Ince, Kemal Klawonn, Frank Volkswagen AG Komponenten-Werkzeugbau Gifhornerstr. 180 Braunschweig 38037 Germany Data Analysis and Pattern Recognition Lab Ostfalia University of Applied Sciences Salzdahlumer Str. 46/48 Wolfenbüttel 38302 Germany

ISBN: (纸本)3642140483

In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum description length principle (MDL) and aspects of granular computing. The approach is verified by giving an example using a data set which is extracted and preprocessed from an operational information system of the Components Toolshop of Volkswagen AG. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Granular computing

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Learning and Adaptation of Robot Skills using Fuzzy Models

Learning and Adaptation of Robot Skills using Fuzzy Models

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2010 IEEE World Congress on Computational Intelligence

作者： Palm, Rainer Iliev, Boyko Univ Orebro Dept Technol AASS SE-70182 Orebro Sweden

ISBN: (纸本)9781424469208

Robot skills can be taught and recognized by a Programming-by-Demonstration technique where first a human operator demonstrates a set of reference skills. The operator's motions are then recorded by a data-capturing system and modeled via fuzzy clustering and a Takagi-Sugeno modeling technique. The resulting skill models use the time as input and the operator's actions as outputs. During the recognition phase, the robot recognizes which skill has been used by the operator in a novel demonstration. This is done by comparison between the time clusters of the test skill and those of the reference skills. Finally, the robot executes the recognized skill by using the corresponding reference skill model. Drastic differences between learned and real world conditions which occur during the execution of skills by the robot are eliminated by using the Broyden update formula for Jacobians. This method was extended for fuzzy models especially for time cluster models. After the online training of a skill model the updated model is used for further executions of the same skill by the robot.

关键词： Takagi-Sugeno modeling technique control engineering computing data capturing system fuzzy clustering fuzzy control fuzzy models learning (artificial intelligence) pattern clustering programming-by-demonstration technique robot skills adaptation robot skills learning robots

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Fourier transform of untransformable signals using pattern recognition technique

Fourier transform of untransformable signals using pattern r...

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international conference on Advances in computing, Control, and Telecommunication Technologies

作者： Gupta, Varun Mittal, Manish Singh, Gavendra Pahuja, Sharvan Kumar Dept. of Instrumentation and Control Engg. National Institute of Technology Jalandhar Jalandhar India Dept. of Electronics and Communication Engg. National Institute of Technology Jalandhar Jalandhar India

ISBN: (纸本)9780769542690

In this paper we are highlighting the signals that are not Fourier transformable and give its Fourier transform using PCA (Principle Component Analysis), LDA (Linear Discriminant Analysis).Such signals are step signal, signum, etc. Basically Fourier transform transforms time domain signal into frequency domain and after transformation describes what frequencies original signal have. Principle Component Analysis is a way of identifying patterns (recognition) in the data and the differences of the data is highlighted. With the help of PCA & LDA we do the dimension reduction of the signal. LDA is used in statistics and machine learning to find a linear combination of features which characterize or separate two or more classes of objects or events. The resulting combination may be used as a linear classifier or, more commonly, for dimensionality reduction before later classification. LDA is closely related to anova (analysis of variance). PCA is used for analyzing. Main advantage of PCA is that once patterns are found and data is compressed that is by reducing the number of dimension without much loss of information. Dimension reduction is the process of reducing the number of random variables under consideration, and can be divided into feature selection and feature extraction. Feature selection approaches try to find a subset of the original variables and feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. © 2010 IEEE.

关键词： Principal component analysis

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A Fast Implementation of the CT EXT Algorithm for the Testor Property Identification

A Fast Implementation of the CT EXT Algorithm for the Testor...

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9th Mexican international conference on Artificial Intelligence (MICAI)

作者： Sanchez-Diaz, Guillermo Piza-Davila, Ivan Lazo-Cortes, Manuel Mora-Gonzalez, Miguel Salinas-Luna, Javier Univ Guadalajara Ctr Univ Valles Ameca 46600 Jal Mexico Mexico

ISBN: (纸本)9783642167720

Typical testors are a useful tool for both feature selection and for determining feature relevance in supervised classication problems. Nowadays, generating all typical testors of a training matrix is computationally expensive;all reported algorithms have exponential complexity, depending mainly on the number of columns in the training matrix. For this reason, different approaches such as sequential and parallel algorithms, genetic algorithms and hardware implementations techniques have been developed. In this paper, we introduce a fast implementation of the algorithm CT_EXT (which is one of the fastest algorithms reported) based on an accumulative binary tuple, developed for generating all typical testors of a training matrix. The accumulative binary tuple implemented in the CT_EXT algorithm, is a useful way to simplifies the search of feature combinations which fulfill the testor property, because its implementation decreases the number of operations involved in the process of generating all typical testors. In addition, experimental results using the proposed fast implementation of the CT_EXT algorithm and the comparison with other state of the art algorithms that generated typical testors are presented.

关键词： feature selection typical testors pattern recognition

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Event matching technique for multidimensional range predicate

Event matching technique for multidimensional range predicat...

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40th international conference on Computers and Industrial Engineering, CIE40 2010

作者： Kim, Gihong Hong, Bonghee Dept. of Computer Science and Engineering Pusan National University Jangjeon-dong Geumjeong-gu Busan Korea Republic of

ISBN: (纸本)9781424472956

Event matching technique against lots of multidimensional range predicates is important in the many applications. Existing techniques are restricted to the domain that all the predicates are specified by the user. However, generally some predicates can be omitted by the application requirement. If some predicates are omitted, it is impossible to insert predicates to existing indexing scheme. One of alternatives can be naïve sequential matching that all the events are compared with all the predicates one by one and more advanced alternative is intersection of multi 1-d index. To solve this problem, we propose a novel index scheme for multidimensional range predicates that can be omitted by the application requirement.

关键词： pattern matching

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Function and service pattern analysis for reconfiguring collaboration systems

Function and service pattern analysis for reconfiguring coll...

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40th international conference on Computers and Industrial Engineering, CIE40 2010

作者： Lee, Sangil Ryu, Kwangyeol Lee, Sunhwa Nam, Sungho Cho, Yongju Department of Industrial Engineering Pusan National University Busan Korea Republic of Korea Digital Collaboration Center Korea Institute of Industrial Technology Ansan Korea Republic of

ISBN: (纸本)9781424472956

Recently, manufacturing companies tried to increase competitiveness in their business collaboration with cooperative companies rather than within their own company. In Korea, more than 600 manufacturing companies are using web-based collaboration systems developed by the government-led project, referred to as i-Manufacturing. The project is being conducted to facilitate on-line collaboration among manufacturing companies since 2004. There are lots of functions and services supported by each collaboration system. In order to re-apply a developed system to other companies, however, we have to modify, upgrade, or newly develop some parts of the system, which is called as customization. During customization processes, we reconfigure functions or services of the system to satisfy user requirements. To facilitate reconfiguration of collaboration systems, therefore, we first define user patterns. Then we introduce an analysis technique to investigate and analyze patterns by using data mining. The analysis technique verifies normal or abnormal patterns(e.g., drastic increase in using a specific function or service), and automatically make the system recognize an abnormal pattern as a new normal pattern when the abnormal pattern continue for a long time. Including the analysis technique, we also suggest a reconfiguration guideline for reorganizing function modules into services in a specific collaboration system.

关键词： Data mining

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Proceedings of the 4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

Proceedings of the 4th ACM International Conference on Distr...

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4th ACM international conference on Distributed Event-Based Systems, DEBS 2010

ISBN: (纸本)9781605589275

The proceedings contain 38 papers. The topics discussed include: context aware computing and its utilization in event-based systems;logic-based representation, reasoning and machine learning for event recognition;an event view model and DSL for engineering an event-based SOA monitoring infrastructure;an RFID architecture based on an event-oriented component model;content-based rendezvous with upgraph combination;event semantics in event dissemination architectures for massive multiuser virtual environments;complex event processing synergies with predictive analytics;event processing for large-scale distributed games;reliable fault-tolerant sensors for distributed systems;business-oriented development methodology for complex event processing: demonstration of an integrated approach for process monitoring;placement of replicated tasks for distributed stream processing systems;and an approach for iterative event pattern recommendation.

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