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Data mining

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Articles 91 - 94 of 94

Full-Text Articles in Computer Engineering

Diagnostics Of Eccentricities And Bar/End-Ring Connector Breakages In Polyphase Induction Motors Through A Combination Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Techniques, John F. Bangura, Richard J. Povinelli, Nabeel Demerdash, Ronald H. Brown Jul 2003

Diagnostics Of Eccentricities And Bar/End-Ring Connector Breakages In Polyphase Induction Motors Through A Combination Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Techniques, John F. Bangura, Richard J. Povinelli, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the foundations of a technique for detection and categorization of dynamic/static eccentricities and bar/end-ring connector breakages in squirrel-cage induction motors that is not based on the traditional Fourier transform frequency-domain spectral analysis concepts. Hence, this approach can distinguish between the "fault signatures" of each of the following faults: eccentricities, broken bars, and broken end-ring connectors in such induction motors. Furthermore, the techniques presented here can extensively and economically predict and characterize faults from the induction machine adjustable-speed drive design data without the need to have had actual fault data from field experience. This is done through the …

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A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events, Richard J. Povinelli, Xin Feng Mar 2003

A New Temporal Pattern Identification Method For Characterization And Prediction Of Complex Time Series Events, Richard J. Povinelli, Xin Feng

Electrical and Computer Engineering Faculty Research and Publications

A new method for analyzing time series data is introduced in this paper. Inspired by data mining, the new method employs time-delayed embedding and identifies temporal patterns in the resulting phase spaces. An optimization method is applied to search the phase spaces for optimal heterogeneous temporal pattern clusters that reveal hidden temporal patterns, which are characteristic and predictive of time series events. The fundamental concepts and framework of the method are explained in detail. The method is then applied to the characterization and prediction, with a high degree of accuracy, of the release of metal droplets from a welder. The …

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Diagnostics Of Bar And End-Ring Connector Breakage Faults In Polyphase Induction Motors Through A Novel Dual Track Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Modeling, Richard J. Povinelli, John F. Bangura, Nabeel Demerdash, Ronald H. Brown Mar 2002

Diagnostics Of Bar And End-Ring Connector Breakage Faults In Polyphase Induction Motors Through A Novel Dual Track Of Time-Series Data Mining And Time-Stepping Coupled Fe-State Space Modeling, Richard J. Povinelli, John F. Bangura, Nabeel Demerdash, Ronald H. Brown

Electrical and Computer Engineering Faculty Research and Publications

This paper develops the fundamental foundations of a technique for detection of faults in induction motors that is not based on the traditional Fourier transform frequency domain approach. The technique can extensively and economically characterize and predict faults from the induction machine adjustable speed drive design data. This is done through the development of dual-track proof-of-principle studies of fault simulation and identification. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case data. Then, the fault cases are classified by their inherent characteristics, so-called “signatures” or “fingerprints.” These fault signatures are extracted …

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Intelligent Mining In Image Databases, With Applications To Satellite Imaging And To Web Search, Stephen Gibson, Vladik Kreinovich, Luc Longpre, Brian Penn, Scott A. Starks Jan 2000

Intelligent Mining In Image Databases, With Applications To Satellite Imaging And To Web Search, Stephen Gibson, Vladik Kreinovich, Luc Longpre, Brian Penn, Scott A. Starks

Departmental Technical Reports (CS)

An important part of our knowledge is in the form of images. For example, a large amount of geophysical and environmental data comes from satellite photos, a large amount of the information stored on the Web is in the form of images, etc. It is therefore desirable to use this image information in data mining. Unfortunately, most existing data mining techniques have been designed for mining numerical data and are thus not well suited for image databases. Hence, new methods are needed for image mining. In this paper, we show how data mining can be used to find common patterns …

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