Introducing neural-network and fuzzy-logic technologies, this volume describes the advantages and challenges of using these technologies to solve motor fault-detection problems, and discusses several design considerations and methodologies in applying these techniques to motor-incipient fault detection.
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The monitoring, diagnosis and incipient fault detection of motors are important and difficult topics in the engineering field. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
motor fault detector, motor fault detection, motor incipient fault detection, sampling window size, initial network weights, winding condition, average correct prediction, fault detection process, backpropagation training algorithm, fault detection schemes, fuzzy logic technologies, fault detection techniques, robustness measure, neural network configurations, motor faults, incipient faults, feedforward net, network accuracy, friction condition, momentum rate, relative robustness, training error, induction machines, feedforward neural network Key Phrases - Capitalized Phrases (CAPs): (learn more)
Energy Conversion, Industrial Electronics, Industry Applications, Normalized Speed Figure, International Journal of Neural Systems, New York, References Chow, Academic Press, Application of Learning Theory, Prentice Hall New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The monitoring, diagnosis and incipient fault detection of motors are important and difficult topics in the engineering field. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
motor fault detector, motor fault detection, motor incipient fault detection, sampling window size, initial network weights, winding condition, average correct prediction, fault detection process, backpropagation training algorithm, fault detection schemes, fuzzy logic technologies, fault detection techniques, robustness measure, neural network configurations, motor faults, incipient faults, feedforward net, network accuracy, friction condition, momentum rate, relative robustness, training error, induction machines, feedforward neural network Key Phrases - Capitalized Phrases (CAPs): (learn more)
Energy Conversion, Industrial Electronics, Industry Applications, Normalized Speed Figure, International Journal of Neural Systems, New York, References Chow, Academic Press, Application of Learning Theory, Prentice Hall New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 14
books:
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2
books
cite this book:
- The Industrial Electronics Handbook (The Electrical Engineering Handbook Series) by J. David Irwin on page 12, page 45, and page 111
- Fuzzy Logic with Engineering Applications by Timothy J. Ross on page 46, and page 61
- Motor Application and Maintenance Handbook by Robert W. Smeaton on page 13, and page 61
- Adaptive Pattern Recognition and Neural Networks by Yoh-Han Pao on page 27, and page 79
- Neural Networks for Control by W. Thomas Miller III on page 27, and page 111
See all 14 books this book cites
- Soft Computing in Industrial Electronics by Seppo J. Ovaska on page 41
- Knowledge-Based Intelligent Techniques in Industry (International Series on Computational Intelligence) by Lakhmi C. Jain on page 220
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