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51	Domain-based Collaborative Learning for Enhanced Health Management of Distributed Industrial Assets Pandhare, Vibhor January 2021 (has links) No description available. Mechanical Engineering Collaborative Learning Federated Learning Domain Adaptation Expectation Maximization Data Privacy Prognostics
52	Reproducible Prognostic and Health Management for Complex Industrial System using Human-AI Collaboration Li, Fei January 2021 (has links) No description available. Mechanical Engineering Prognostics and Health Management Human-AI collaboration Machine Learning Industrial AI
53	Data Suitability Assessment and Enhancement for Machine Prognostics and Health Management Using Maximum Mean Discrepancy Jia, Xiaodong January 2018 (has links) No description available. Mechanical Engineering Data Suitability Machine Learning Data Quality Prognostics and Health Management Aero-Engine
54	Decentralized Federated Autonomous Organizations for Prognostics and Health Management Bagheri, Behrad 15 June 2020 (has links) No description available. Computer Science Federated Learning Prognostics and Health Management PHM Homomorphic Encryption Blockchain Machine Learning
55	A Transfer Learning Methodology of Domain Generalization for Prognostics and Health Management Yang, Qibo January 2020 (has links) No description available. Mechanical Engineering Prognostics and health management Transfer learning Domain generalization Maximum mean discrepancy Neural networks
56	A Data Augmentation Methodology for Class-imbalanced Image Processing in Prognostic and Health Management Yang, Shaojie January 2020 (has links) No description available. Mechanical Engineering Data Augmentation Image Processing Machine Vision Deep Learning Prognostics and Health Management
57	Driving Behaviour Modelling Framework for Intelligent Powertrain Health Management Doikin, Aleksandr, Campean, Felician, Priest, Martin, Angiolini, E., Lin, C., Agostinelli, E. 26 July 2022 (has links) Yes / Implementation of an intelligent powertrain health management relies on robust prognostics modelling. However, prognostics capability is often limited due to unknown future operating conditions, which varies with duty cycles and individual driver behaviours. On the other hand, the growing availability of data pertaining to vehicle usage allows advanced modelling of usage patterns and driver behavious, bringing optimisation opportunities for powertrain operation and health management. This paper introduces a methodology for driving behaviour modelling, underpinned by Machine Learning classification algorithms, generating model-based predictive insight for intelligent powertrain health management strategies. Specifically, the aim is to learn the patterns of driving behaviour and predict characteristics for the short-term future operating conditions as a basis for enhanced control strategies to optimise energy efficiency and system reliability. A case study of an automotive emissions aftertreatment system is used to comprehensively demonstrate the proposed framework. The case study illustrates the approach for integrating predictive insight from machine learning deployed on real world trip behaviour data, in conjunction with a reliability-based model of the operational behaviour of a particulate filter, to propose an intelligent active regeneration control strategy for improved efficiency and reliability performance. The effectiveness of the proposed strategy was demonstrated on an industry standard model-in-the-loop set up with a representative sample of real-world vehicle driving data. / The authors acknowledge funding for the research presented in this article from Jaguar Land Rover under a research collaboration with the University of Bradford on “Intelligent Personalised Powertrain Healthcare”, and the Institute of Digital Engineering who have provided funding for proof of concept – the aiR-FORCE project. Intelligent health management Prognostics Driving behaviour Machine learning Diesel particulate filter
58	PHM Methodology for Location-based Health Evaluation and Fault Classification of Linear Motion Systems Gore, Prayag January 2022 (has links) No description available. Mechanical Engineering Prognostics and Health Management Linear Motion Systems Ball Screw Health Monitoring Fault Classification Transfer Learning
59	Automotive IVHM: a framework for intelligent health management of powertrain systems. Development of a framework and methodology based on the fusion of knowledge-based and data-driven modelling approaches for diagnostics and prognostics of complex systems with application to automotive powertrain systems Doikin, Aleksandr January 2020 (has links) The full text will be available at the the end of the embargo period: 29th Jul 2026 Automotive IVHM Intelligent powertrain health management Automotive Powertrain Diagnostics Prognostics Maintenance
60	An Evaluation of Classification Algorithms for Machinery Fault Diagnosis Buzza, Matthew 15 June 2017 (has links) No description available. Mechanical Engineering Fault Diagnosis Classification Algorithms Prognostics and Health Management Complex Machinery

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