"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.
"synopsis" may belong to another edition of this title.
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9786630259094
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9786630259094
Quantity: Over 20 available
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condition: 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. Seller Inventory # 9786630259094
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Seller Inventory # I-9786630259094
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch. Seller Inventory # 9786630259094
Quantity: 2 available
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: 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. Seller Inventory # 9786630259094
Quantity: 2 available
Seller: AussieBookSeller, Truganina, VIC, Australia
Paperback. Condition: 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. Seller Inventory # 9786630259094
Quantity: 1 available
Seller: CitiRetail, Stevenage, United Kingdom
Paperback. Condition: 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. Seller Inventory # 9786630259094
Quantity: 1 available
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: 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. Seller Inventory # 9786630259094
Quantity: 1 available
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: 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. Seller Inventory # 136790970
Quantity: 5 available