Intelligent Predictive Maintenance Frameworks for Fault Classification: Hybridising Machine Learning Techniques - Softcover
Synopsis
With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these systems since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results.
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Intelligent Predictive Maintenance Frameworks for Fault Classification
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
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Intelligent Predictive Maintenance Frameworks for Fault Classification
Published by
LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these systems since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results. 244 pp. Englisch. Seller Inventory # 9786206752738
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Intelligent Predictive Maintenance Frameworks for Fault Classification
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LAP Lambert Academic Publishing, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
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Intelligent Predictive Maintenance Frameworks for Fault Classification
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
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Intelligent Predictive Maintenance Frameworks for Fault Classification
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
New
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Intelligent Predictive Maintenance Frameworks for Fault Classification
Published by
LAP LAMBERT Academic Publishing Jul 2023, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
New
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these systems since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 244 pp. Englisch. Seller Inventory # 9786206752738
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Intelligent Predictive Maintenance Frameworks for Fault Classification : Hybridising Machine Learning Techniques
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206752739
ISBN 13: 9786206752738
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - With the rising demand for complex, integrated and autonomous systems in the field of engineering, efficient and versatile Predictive Maintenance (PdM) frameworks have become a requirement for monitoring the health status of these systems since safety, reliability and optimum asset utilisation, are key issues. However, due to the continuously changing dynamics of industrial operations, the data recorded for developing PdM frameworks are often high-dimensional and characterised by undesirable features such as high level of uncertainty, class imbalance and multiclass among others. These undesirables limit the efficiency of existing PdM frameworks in producing desirable results. For these reasons, this book has proposed three hybrid and novel PdM frameworks capable of handling such undesirable features through the hybridisation of machine learning techniques. The proposed hybrid frameworks advance the field of PdM by improving the accuracy of fault diagnosis as the issue of undesirable features impedes the ability of machine learning algorithms to produce desired results. Seller Inventory # 9786206752738