RESEARCH PAPERS

machine design research papers




Design optimization of a permanent-magnet excited synchronous machine for electrical automobiles
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The paper deals with the automated optimal design of a new synchronous motor particularly suitable for electric vehicles. Identifying the shape of rotor pole and excitation magnets, minimizing the mass/volume and simultaneously maximizing the torque, is the main

Fundamentals of machine component design
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Enhancing the teaching of machine design by creating a basic hands-on environment with mechanical 'breadboards'
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The pendulum of engineering education is swinging from an emphasis on theoretical material to a balance between theory and hands-on activities. This transformation is motivated, in part, by the different experiential background of students presently entering

Machine Design to Achieve Manufacturing System Objectives
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ABSTRACT This paper investigates why machines are presently designed to reduce unit labor cost by increasing the speed of the machine or by eliminating direct labor altogether with automation. Machine design practices are currently shown to be operationally Energy consumption is responsible for a substantial part of the environmental impact consumption is hardly a priority for many machine designers, since they concentrate

A global design strategy for multiphase machine applied to the design of a 7-phase fractional slot concentrated winding PM machine
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Abstract This paper describes a global methodology of design for multiphase Permanent Magnet (PM) machines. Firstly a general multi machine theory for modeling multiphase machines is presented. This theory allows to define constraints and objectives for the

Analytical design of a high-torque flux-switching permanent magnet machine by a simplified lumped parameter magnetic circuit model
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FABSTRACT This paper presents how to analytically design a high-torque three-phase flux- switching permanent magnet machine with 12 stator poles and 14 rotor poles. Firstly, the machine design parameters are studied addressing on high output torque and its flux

Precision machine design
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This book is a comprehensive engineering exploration of all the aspects of precision machine design-both component and system design considerations for precision machines. It addresses both theoretical analysis and practical implementation providing many real-

Machine design considerations for sensorless control of PM motors
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ABSTRACT In this paper, considerations concerning the saliency of a PM synchronous motor, aimed at being controlled by a sensor less strategy is presented. At high speeds, the strategy is based on extracting information from the back-emf. At low speeds, however, the back-

Electric Machine Design Tailored for Powertrain Optimization
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ABSTRACT In spite of being highly efficient, battery electric vehicles suffer from limited driving range because of the limited battery capacity. This disadvantage can be encountered by optimizing the powertrain using a special electric machine design. The main idea is to

Site sensitive support structure and machine design for offshore wind farms
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Summary A design approach is investigated to counteract the effect of variable water depth and soil conditions at offshore wind farms with the aid of an offshore-specific and adaptable machine design. So far, one of the design drivers is that the fundamental eigenfrequency

A New Paradigm for Synchronous State Machine Design in Verilog
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Synchronous State Machines are one of the most common building blocks in modern digital systems. They handle everything from communications handshaking protocols to microprocessor bus wait state insertion. State machines operate at hardware speeds

A study of brushless doubly-fed (induction) machines: contributions in machine analysis, designand control
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Abstract The Brushless Doubly-Fed Machine (BDFM) shows commercial promise as a variable speed drive or generator. However, for this promise to be realised the design of the machine must be improved beyond that proposed to date. This dissertation contributes

Teaching machine design through product emulation
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It is widely accepted that in order to learn complex technical material well, some form of active experimentation or hands-on activities are required. Traditionally, in engineering education this occurs through laboratory experiments or through design projects. In

Finite element method as an aid to machine design: A computational tool
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ABSTRACT The paper provides an overview of the state of art in computational electromagnetic. There are three major areas like Design, optimization and material selection for the electric machines. The computational tool based on finite elements is very

Design of a precision electro discharge micro milling machine
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Abstract This paper presents the design of a precision electro discharge micro milling machine being designed and built in the Precision Systems Laboratory at the University of Kentucky. This machine will use micro electrodes to mill complex 3D cavities with

State machine design pattern
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ABSTRACT This paper presents a new object-oriented design pattern State Machine design pattern. This pattern extends capabilities of State design pattern. These patterns allow an object to alter its behavior when its internal state changes. Introduced event-

Design Sketch for a Million-Element NETL Machine.
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Abstract This paper describes (very briefly) a parallel hardware implementation for NETL- type semantic network memories. A million-element system can be built with about 7000 IC chips, including 4000 64K RAM chips. This compares favorably with the hardware cost of

Soft magnetic composites for advanced machine design
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ABSTRACT Soft Magnetic Composites (SMCs) suitable for electrical motor applications have been commercially available for some time. High induction and low losses are basic properties defining a useable soft magnetic material. The induction level is closely related

Generic torque-maximizing design methodology of surface permanent-magnet verniermachine
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(SPMVM) has a toothed-pole structure, and its highly nonlinear relationship between its dimensions and magnetic field makes its design cumbersome. This paper presents a generic design methodology for the machine which brings out its torque-maximizing

Transverse Flux Machine Design for Manipulating System Applications
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Abstract This paper introduces a novel drive based on transverse flux technology for use in manipulating systems. Transverse flux machines offer the combination of a high output ABSTRACT This correspondence investigates the effect of the shape and size of a standard feature (fiducial) on the positional accuracy of the centroid in machine vision applications. It is shown that a circular fiducial provides superior suhpixel registration accuracy among

Synthesizable Finite State Machine Design Techniques Using the New SystemVerilog 3.0 Enhancements
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ABSTRACT This paper details RTL coding and synthesis techniques of Finite State Machine (FSM) design using new Accellera SystemVerilog 3.0 capabilities. Efficient existing RTL coding styles are compared to new SystemVerilog 3.0 enhanced coding styles.

Machine Component Design
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This text is intended to fill one or more of the following functions: To offer the instructor a wide range for the selection of those topics that are important to the particular curriculum in the university department in which the course is being taught. To allow the same text to

Machine tool design and operation strategies for green manufacturing
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ABSTRACT Strategies to reduce energy demand in manufacturing processes are becoming necessary due to the growing concern of carbon emissions and the expected rise of electricity prices over time. To guide the development of these strategies, the results of a

Design of a novel transverse flux machine
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ABSTRACT This paper describes the design of a novel permanent magnet excited transverse flux machine. The rotor shares the same construction as hybrid stepping motors and has a permanent magnet ring, magnetised axially. The ringshaped stator winding is surrounded

Design of an Integrated 100kW Permanent Magnet Synchronous Machine in a Prototype Thruster for Ship Propulsion
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ABSTRACT -Traditional electrical motors are mechanical coupled through gear and shaft to their load. This paper presents a direct driven permanent magnet (PM) motor integrated into a ship propeller. It is proven that an integration of PM-motor and propeller is possible. This

A revised design for an understanding machine
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This paper argues that machine translation programs will be able to solve certain problems, eg, the resolution of polysemy, only by storing the meaning of natural language words in a medium and a format providing properties similar to those of human understanding. It

Hardware design and implementation of the personal sequential inference machine (PSI)
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ABSTRACT The Personal Sequential Inference ii a personal computer designed um tool fer software and hardware development in Japan's Fifth, Generation Computer Systems (FGCS) project. paper describes PSTs systems and the

Jikes Research Virtual Machine Design and Implementation of a 64-bit PowerPC Port
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Abstract This work describes the design and implementation of a 64-bit PowerPC port of the Jikes Research Virtual Machine (Jikes RVM). It explains general design decisions for 64-bit implementations of Java virtual machines, specifically Jikes RVM, along with details of the

Virtual arch type reconfigurable machine tool design: Principles and Methodology
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Abstract The report presents a new approach for designing a Reconfigurable Machine Tool (RMT). The design methodology presented in the report was developed in ERC/RMS at the University of Michigan and it generates all kinematicaly feasible RMT designs. The design

Geeter2, Tine Lefebvre* and Herman Bruyninckx z Katholieke Universiteit Leuven Division of Production Engineering, Machine Design and Automation (PMA)
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Abstract A Kalman filter is a stochastic, recursive estimator, which estimates the state of a system based on the knowledge of the system input, the measurement of the system output, and a model of the relation between input and output. The Kalman filter equations are well

Design and analysis of high speed slotless PM machine with Halbach array
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ABSTRACT Slotless machines have been applied for a very high rotational speed and/or the ripple-free torque. In this paper, slotless machines equipped with external 4-pole field Halbach and radially magnetized PM array for electro-mechanical battery. This paper

Molecular Measuring Machine design and performance
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The trend toward product miniaturization is pervasive across many fields, most notably in the microelectronics arena, but also in mechanical systems [micro-electro-mechanical systems (MEMS)], microoptics, and microfluidics to name a few. As an alternative to the top-down

Modular Fault Tolerant Switched Reluctance Machine Design and Dynamic Simulations
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ABSTRACT Electrical machines and drives used in various criti-cal fields must be of special design in order to achieve the required fault tolerance level. In the paper a modular fault tolerant switched reluctance machine is proposed and studied by means of dynamic

machine learning research papers



Genetic algorithms and machine learning
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There is no a priori reason why machine learning must borrow from nature. A field could exist, complete with well-defined algorithms, data structures, and theories of learning, without once referring to organisms, cognitive or genetic structures, and psychological or

Pattern recognition and machine learning
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Machine learning is a key technology in bioinformatics, especially in the analysis of" big data" in bioinformatics. This course gives an overview of basic concepts, algorithms, and applications in machine learning, including topics such as classification, linear regression,

Correlation-based feature selection for machine learning
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Abstract A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach.

Estimating probabilities: a crucial task in machine learning.
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Abstract The evaluation of the Bayesian formula that is used as a basis of many machine learning systems is studied in detail. It is shown that, when used naively (ie assuming the independence of attributes), its classification accuracy heavily depends on the method for

Selection of relevant features in machine learning
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Abstract In this paper, we review the problem of selecting relevant features for use in machine learning. We describe this problem in terms of heuristic search through a space of feature sets, and we identify four dimensions along which approaches to the problem can

Guest editors' introduction: On applied research in machine learning
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Common arguments for including applications papers in the Machine Learning literature are often based on the papers' value for advertising success stories and for boosting morale. For example, high-profile applications can help to secure funding for future research and can

Memo functions and machine learning
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If computers could learn from experience their usefulness would be increased. When I write a clumsy program for a contemporary computer a thousand runs on the machine do not re- educate my handiwork. On every execution, each time-wasting blemish and crudity, each
Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4. 5, are probably the most popular in the machine learning community.
Machine learning is a scientific discipline and, like the fields of AI and computer science, has both theoretical and empirical aspects. Although recent progress has occurred on the theoretical front (see Machine Learning, volume 2, number 4), most learning algorithms

Machine learning from imbalanced data sets 101
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For research to progress most effectively, we first should establish common ground regarding just what is the problem that imbalanced data sets present to machine learning systems. Why and when should imbalanced data sets be problematic When is the

Information extraction from HTML: Application of a general machine learning approach
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Abstract Because the World Wide Web consists primarily of text, information extraction is central to any e ort that would use the Web as a resource for knowledge discovery. We show how information extraction can be cast as a standard machine learning problem, and

Torch: a modular machine learning software library
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Abstract. Many scientific communities have expressed a growing interest in machine learning algorithms recently, mainly due to the generally good results they provide, compared to traditional statistical or AI approaches. However, these machine learning

Computer Systems That Learn: Classification And Prediction Methods From Statistics, Neural Nets, Machine Learning And Exp
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Title: Computer Systems That Learn: Classification And Prediction Methods From Statistics, Neural Nets, Machine Learning And Expert Systems (Machine Learning Series) Author: Sholom M. Weiss,Casimir A. Kulikowski, Publisher: Morgan Kaufmann Pages: 223 Published:

An introduction to machine learning
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Page 1. An introduction to machine learning Pierre Lison, Language Technology Group (LTG) Department of Informatics HiOA, October 3 2012 Page 2. @ 2012, Pierre Lison INF5820 course Outline Motivation Machine learning approaches My own research Conclusion 2

The discipline of machine learning
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Abstract Over the past 50 years the study of Machine Learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of Statistics that largely ignored computational considerations, to a broad

Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context.
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Abstract A small subset of machine learning algorithms, mostly inductive learning based, applied to the KDD 1999 Cup intrusion detection dataset resulted in dismal performance for user-to-root and remote-to-local attack categories as reported in the recent literature. The

The geometry of ROC space: understanding machine learning metrics through ROC isometrics
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Abstract Many different metrics are used in machine learning and data mining to build and evaluate models. However, there is no general theory of machine learning metrics, that could answer questions such as: When we simultaneously want to optimise two criteria,

Data mining: machine learning, statistics, and databases
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Abstract Knowledge discovery in databases and data mining aim at semiautomatic tools for the analysis of large data sets. We give an overview of the area and present some of the

Combining Data Mining and Machine Learning for Effective User Profiling.
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This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the de-, ic nrrnm,-,li~h~rl,,an .am.L~ nf mn.-h;na lm..~:~~ e-. .- . ..--..*.*yYYA'.. UY.b Y UISLUY I III-Yllr IxuIY11~ methods. In particular, we combine data mining and

A machine learning architecture for optimizing web search engines
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Abstract Indexing systems for the World Wide Web, such as Lycos and Alta Vista, play an essential role in making the Web useful and usable. These systems are based on Information Retrieval methods for indexing plain text documents, but also include

protein interaction in machine learning



Bayesian machine learning methods for predicting protein-peptide interactions and detecting mosaic structures in DNA sequences alignments
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Abstract Short well-defined domains known as peptide recognition modules (PRMs) regulate many important protein-protein interactions involved in the formation of macromolecular complexes and biochemical pathways. High-throughput experiments like

Protein Classification in a Machine Learning Framework
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Bioinformatics is a marriage of biology, informatics and mathematics and it employs computational tools and methods for managing, analysing and manipulating sets of biological data [1; 2]. The main tasks of bioinformatics are the gathering, organization and

Seminar: Machine Learning in Bioinformatics–Protein Function Prediction
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The definition of protein function is not clearly defined and can vary between people and situations [ROS01, GER04, FRI01]. The Gene Ontology classification of protein function includes a Cellular Component, Biological Process and Molecular Function. This is useful

Work in Progress: Identification of Protein Functional Sites using Machine Learning with Support Vector Machines
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Detection of functional sites in proteins is an important problem in computational biology and has wide implications in computational drug discovery. FEATURE by Stanford Helix Group (http://feature. stanford. edu/webfeature/is a system for identifying active sites in proteins

A machine-learning approach to hydrogenosomal protein identification in Trichomonas vaginalis
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Abstract. The protozoan parasite Trichomonas vaginalis is the causative agent of the most prevalent non-viral sexually transmitted disease in humans. It possesses mitochondrion- related organelles, known as hydrogenosomes, that produce ATP under anaerobic

Machine Learning Prediction of Amino Acid Sequence Characterization in Protein N-Myristoylation
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Protein N-myristoylation is the lipid modification in which the saturated fatty acid of 14 carbon number binds covalently to N-terminal of virus-based and eukaryotic protein. N- myristoylated proteins have specific seqence at N-terminal called N-myristoylation signal

Improving the State of the Art in Machine Learning Methods for Protein-RNA Interface Prediction
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Page 1. Iowa State University Bioinformatics and Computational Biology Graduate Program Improving the State of the Art in Machine Learning Methods for Protein-RNA Interface Prediction

Protein Classification in a Machine Learning Framework
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At present in most sophisticated pattern recognition tasks humans still outperform computers; however, in certain specific tasks computer models can do better than humans. My scientific interests include building mathematical and algorithmic models in the fields

Supervised Machine Learning in Bioinformat-ics: Protein Classification
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Abstract Since the introduction of next generation sequencing (NGS) there has been demand for sophisticated methods to classify proteins based on sequence data. Protein sequences have traditionally been performed in the lab by biologists, however open

A Modified Machine Learning Method Used in Protein Prediction in Bioinformatics
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Abstract: Biological information resources have been growing rapidly with the development of biological science and technology, and the development of computer technology and the Internet has made large scale data storage, processing and transmission possible.

bearing fault finding and solution



Bearing fault detection using artificial neural networks and genetic algorithm
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A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs), namely, multilayer perceptron (MLP), radial basis function (RBF) network, and probabilistic neural network (PNN). The time domain vibration

Fault diagnosis for a rolling bearing used in a reciprocating machine by adaptive filtering technique and fuzzy neural network
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Abstract:-This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal

Bearing fault diagnosis based on neural network classification and wavelet transform
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Abstract:-Automated fault classification has been an important pattern recognition problem for decades. In the performance of all motor driven systems, bearings play an important role. The purpose of condition monitoring and fault diagnostics are to detect and distinguish

Automatic bearing fault classification combining statistical classification and fuzzy logic
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Abstract-In this paper, a new automatic analysis method for the detection of cyclic bearing faults is introduced. The method uses a multivariate statistical fault classification and fuzzy logic. Features are extracted from an envelope spectrum of the frame acceleration of a

Enhanced unsupervised noise cancellation using angular resampling for planetary bearing faultdiagnosis
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The bearings of planetary gears provide one of the most difficult scenarios for detection and diagnostics of bearing faults, since the fault signals must pass through a tortuous and time- varying path to arrive at external measurement points where they can be detected. In the

814. Fault diagnosis of main engine journal bearing based on vibration analysis using Fisher linear discriminant, K-nearest neighbor and support vector machine
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Abstract. Vibration technique in a machine condition monitoring provides useful reliable information, bringing significant cost benefits to industry. By comparing the signals of a machine running in normal and faulty conditions, detection of defected journal bearings is Abstract. This paper focuses on the diagnostics of ball bearings in direct-drive motors. These specific AC brushless motors are increasing their importance in automation machineries because they can work with a built-in flexibility. In particular the angular displacement of

A Study of Rolling-Element Bearing Fault Diagnosis Using Motor's Vibration and Current Signatures
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Abstract: This paper investigates the fault detection and diagnosis for a class of rolling- element bearings using signal-based methods based on the motor's vibration and phase current measurements, respectively. The envelope detection method is employed to

Bearing fault diagnosis of a mine stone crasher by vibration condition monitoring technique
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Abstract: To maintain an efficient operating unit and avoid failure of mineral critical equipment, it is necessary to maintain the critical parts of that equipment. Recently, rotary machines with rolling bearings have become highly advanced and the environments in

Incipient bearing fault diagnosis using DWT for feature extraction
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Abstract:An efficient predictive plan is needed for any industry because it can optimize the resources management and improve the economy plant, by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in the productive

Optimization of time domain features for rolling bearing fault diagnostics
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Abstract The main objective of this paper is the determination of an optimal parameter set for the generation of features from an acceleration time signal of a bearing vibration with different fault sizes. The derived features based on an advanced analysis of statistical

Unscented Kalman filter with gaussian process degradation model for bearing faultprognosis
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ABSTRACT The degradation of rolling-element bearings is mainly stochastic due to unforeseeable influences like short term overstraining, which hampers the prediction of the remaining useful lifetime. This stochastic behaviour is hardly describable with parametric

Bearing fault detection for on-line quality control of electric motors
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Abstract- This paper is concerned with analysis and classification of vibration signals from universal electric motors. The goal is to reveal manufacturing defects caused by assembly machines composing the motors on a production line. Such machines may under certain

Wavelet Analysis And Envelope Detection For Rolling Element Bearing Fault Diagnosis ś A Comparative Study
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ABSTRACT Envelope Detection (ED) is traditionally always used with Fast Fourier Transform (FFT) to identify the rolling element bearing faults. The inability of FFT to detect non-stationary signals makes Wavelet Analysis (WA) an alternative for machinery fault

Improved wavelet neural network based on hybrid genetic algorithm applicationin on faultdiagnosis of railway rolling bearing
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Abstract The method of improved wavelet transform neural network based on hybrid GA (genetic algorithm) is presented to diagnose rolling bearings faults in this paper. Genetic Artificial Neural Networks (GA-ANN) overcomes BP neural network's fault of slow

PMSM bearing fault detection by means of fourier and wavelet transform
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Abstract-This paper presents a study of permanent magnet synchronous machines (PMSM) with bearing fault using a two-dimensional (2-D) finite element analysis (FEA). Fourier Fast and Wavelet Transform were used to fault detection of bearing damage under stationary

Point processes for bearing fault detection under non-stationary operating conditions
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ABSTRACT Bearing faults represent the most frequent mechanical faults in rotational machines. They are characterized by repetitive impacts between the rolling elements and the damaged surface. The time intervals between two impacts are directly related with the

Bearing Fault Analysis Using Frequency Analysis and Wavelet Analysis
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Abstract:Bearing fault diagnosis is important in condition monitoring of any rotating machine. Early fault detection in machineries can save millions of dollars in emergency maintenance cost. Different techniques are used for fault analysis such as short time

Spectral Methods Assessment in Journal Bearing Fault Detection Applications
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ABSTRACT The scope of this work is to assess three spectral methods employed in journal bearing fault detection. To this end, two popular methods namely the Short Time Fourier Transform (STFT) and the Wavelet Transform and an innovative and very promising

Support Vector Machine based bearing fault detection of induction motor
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Abstract: In this paper a simple Least Square Support Vector Machine (LS-SVM) based technique is given to detect the bearing lubrication related fault of an induction motor. Vibration is one of the most preferred parameter to detect the bearing related faults.

Using a dynamic roller bearing model under varying fault parameters
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Abstract In condition monitoring of rotating machinery vibration analysis is commonly used for fault detection. Often bearing faults appear as wear, indentation and spalling, which result from foreign particles, overload, inadequate lubrication or vibration while metal to

Wavelet Transform Based Feature Extraction for Fault Diagnosis of Rolling-element Bearing
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Abstract Since rolling-element bearings are widely used in various mechanical and electrical systems, reliable bearing fault diagnosis technique is critically needed to prevent performance malfunction of the systems. In this study, the vibration signals of bearings

Journal-Bearing Fault Detection Based on Vibration Analysis Using Feature Selection and Classification Techniques
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ABSTRACT Vibration technique in a machine condition monitoring provides useful reliable information, bringing significant cost benefits to industry. By comparing the signals of a machine running in normal and faulty conditions, detection of faults like journal-bearing

Wavelet based signal demodulation technique for bearing fault detection
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The method is based on the wavelet transform as a method of signal demodulation. Properties of time frequency representation of the signal enables extraction of typical damage signatures from the signal. First step of this method is a wavelet filtration, which

A new blind-source-separation method and its application to fault diagnosis of rolling bearing
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Abstract In this paper some existing methods of Blind Source Separation (BSS) are analyzed and the general framework of BSS based on Joint Diagonalization (JD) is presented. Fractional Fourier Transform (FrFT) is reviewed, and a new property of FrFT is established

Statistical approach for tapered bearing fault detection using different methods
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Abstract:The study investigated the sensitivity of using a contact and a non contact method in tapered roller bearing damage detection. Accelerometers mounted on the bearing housing and connected to Sound and Vibration Analyzer (SVAN) 958 was used to

A new method of bearing fault diagnostics in complex rotating machines using multi-sensor mixtured hidden Markov models
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ABSTRACT Vibration signals from complex rotating machines are often non-Gaussian and non-stationary, so it is difficult to accurately detect faults of a bearing inside using a single sensor. This paper introduces a new bearing fault diagnostics scheme in complex rotating

Rolling bearing fault detection and isolation-A didactic study
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Abstract:-The present paper aims to demonstrate why usually when theoretical mathematical models are used to compute the frequencies corresponding to a faulty rolling bearing a deviation is obtained between the computed values and the real frequencies emitted by

On Finding Better Wavelet Basis for Bearing Fault Detection
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Abstract: This paper considers the comparision of the Meyer and Morlet wavelet for bearing fault diagnosis. We created a wavelet based upon a transient vibration signal model established for signals generated in deep-groove ball bearings with pitting (spalling)

The fault diagnosis of the rolling bearing based on the LMD and time-frequency analysis
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Abstract The rolling bearing vibration is a complex, non-stationary and dynamic process. The vibration signal can not be described through a fixed time or frequency function. This increases the difficulty of fault diagnosis. Therefore, this paper proposed a rolling bearing

Application of Wavelet Packets in bearing fault diagnosis
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Abstract:-In this paper the wavelet packet transform is used for processing of rolling element bearing fault signals. The effectiveness of the envelope analysis technique is combined with the flexibility of the wavelet packet transform, helping in the minimization of interventions

Rolling element bearing fault detection based on orthogonal hilbert-huang transform
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Abstract A new approach to fault diagnosis of bearings, based on Orthogonal Hilbert-Huang Transform (OHHT), is presented. This method is based on Empirical Mode Decomposition (EMD) and Intrinsic Mode Function Orthogonalization technique. Empirical mode


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