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Deep Learning-Based Predictive Maintenanace: AND ANOMALY DETECTION FOR SPACECRAFT ENGINES - Tapa blanda
Sinopsis
"Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry.
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DEEP LEARNING-BASED PREDICTIVE MAINTENANACE
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
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DEEP LEARNING-BASED PREDICTIVE MAINTENANACE
Publicado por
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
Nuevo
PAP
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Deep Learning-Based Predictive Maintenanace (Paperback)
Publicado por
LAP Lambert Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
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Paperback. Condición: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9786630259094
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Deep Learning-Based Predictive Maintenanace
Publicado por
LAP Lambert Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
Nuevo
Tapa blanda
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DEEP LEARNING-BASED PREDICTIVE MAINTENANACE
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LAP LAMBERT Academic Publishing Aug 2026, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch. Nº de ref. del artículo: 9786630259094
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DEEP LEARNING-BASED PREDICTIVE MAINTENANACE : AND ANOMALY DETECTION FOR SPACECRAFT ENGINES
Publicado por
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines' presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. Nº de ref. del artículo: 9786630259094
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Deep Learning-Based Predictive Maintenanace (Paperback)
Publicado por
LAP Lambert Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
Nuevo
Paperback
Librería: AussieBookSeller, Truganina, VIC, Australia
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Paperback. Condición: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9786630259094
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Deep Learning-Based Predictive Maintenanace (Paperback)
Publicado por
LAP Lambert Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
Nuevo
Paperback
Librería: CitiRetail, Stevenage, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9786630259094
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DEEP LEARNING-BASED PREDICTIVE MAINTENANACE
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LAP LAMBERT Academic Publishing Aug 2026, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -'Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines' presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. 60 pp. Englisch. Nº de ref. del artículo: 9786630259094
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DEEP LEARNING-BASED PREDICTIVE MAINTENANACE | AND ANOMALY DETECTION FOR SPACECRAFT ENGINES
Publicado por
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6630259092
ISBN 13: 9786630259094
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Librería: preigu, Osnabrück, Alemania
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Taschenbuch. Condición: Neu. DEEP LEARNING-BASED PREDICTIVE MAINTENANACE | AND ANOMALY DETECTION FOR SPACECRAFT ENGINES | Ms. D. Banu Kranthi (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630259094 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 136790970
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