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