Data-Driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications

Si, Xiao-Sheng; Zhang, Zheng-Xin; Hu, Chang-Hua

ISBN 10: 3662540282 / ISBN 13: 9783662540282

Edité par Springer, 2017

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N° de réf. du vendeur 26376467523

Détails bibliographiques

• Titre: Data-Driven Remaining Useful Life Prognosis Techniques : Stochastic Models, Methods and Applications
• Éditeur: Springer
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 3662540282
• ISBN à 13 chiffres: 9783662540282
• Reliure: Couverture rigide
• État de l'article: New

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Synopsis

Part I Introduction, Basic Concepts and Preliminaries.- Overview.- Advances in Data-Driven Remaining Useful Life Prognosis.- Model Determination for Lifetime Prognosis.- Part II Remaining Useful Life Prognosis for Linear Stochastic Degrading Systems.- Adaptive Remaining Useful Life Prediction Method.- Exact and Closed-Form Solution to Remaining Useful Life Prediction.- Remaining Useful Life Prediction With Three-Level Variability.- Part III Remaining Useful Life Prognosis for Nonlinear Stochastic Degrading Systems.- Nonlinear Degradation Modeling and Remaining Useful Life Prediction Method.- Hidden and Nonlinear Degradation Modeling and Online Remaining Useful Life Prediction Method.- Remaining Useful Life Prediction Method With State Switches.- Remaining Useful Life Prediction for Degrading Systems with Imperfect Maintenance.- Part IV Applications of Prognostics in Decision Making.- Variable Cost-based Maintenance Model from Prognostic Information.- Forecasting Spare Parts Demand under Prognostic Information.- Variable Cost-Based Maintenance and Inventory Model from Prognostic Information.- Prognostic-information-Based Order-Replacement Policy for Degrading Systems.