This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the…mehr
This volume contains selected papers from the Fifth Conference on Maintenance, Condition Monitoring and Diagnostics, MCMD 2021, in Oulu, Finland, collected by editors with years of experiences in condition monitoring, signal processing, advanced reasoning and diagnostics, maintenance, risk assessment, and asset management. This work maximizes reader insights into the current trends in novel technologies and maintenance trends in industrial domains, energy production and energy conservation, mechatronics and robot technologies. These proceedings discuss key issues and challenges in the operation, maintenance and risk management of complex engineering systems and will serve as a valuable resource for condition monitoring and risk management professionals from industry and science exchange knowledge, experiences and strengthen multidisciplinary network those in the field. This book will be of benefit to academia, and industry alike.
Senior Research Fellow Esko Juuso has D.Sc. (Tech.) on Control and Systems Engineering in Department of Process and Environmental Engineering at the University of Oulu. He is Adjunct Professor on Computational Intelligence and ISCM Fellow. He has a M.Sc. (Tech.) degree in Technical Physics (Material Physics) from the University of Oulu, Finland. He is the developer of the linguistic equation (LE) approach and the nonlinear scaling methodology used in various applications. His research interests are in modelling and control of industrial processes with a special emphasis on combining intelligent control, fault diagnosis and performance monitoring into smart adaptive systems (SAS) and Cyber Physical Systems (CPS). He has done research visits to the UK (University of Manchester Institute of Science and Technology - UMIST), Spain (Plataforma Solar de Almeriá - PSA), Germany (University of Dortmund) and France (Institut Français de Méchanique Avancé - IFMA). He has been a member of Esprit Working Groups, SiE - Simulation in Europe and FALCON - Fuzzy Algorithms for Control and has been active in European Networks of Excellence (ERUDIT and EUNITE), including the vice-chairman of Production Industries Committee of EUNITE NoE. Dr. Diego Galar is Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology where he is coordinating several H2020 projects related to different aspects of cyber physical systems, Industry 4.0, IoT or Industrial AI and Big Data. He was also involved in the SKF UTC centre located in Lulea focused on SMART bearings and also actively involved in national projects with the Swedish industry or funded by Swedish national agencies like Vinnova. He has authored more than five hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences and actively participating in national and international committees for standardization and R&D in the topics of reliability and maintenance.
Inhaltsangabe
Data Sharing Concept for Electric Car Services: Fleet Level Optimization and Emission Reduction based on Monitored Data.- Unsupervised Deep Neural Network Considering the Uncertainties Effect in Pipeline Condition Monitoring using Guided Ultrasonic Waves.- Detecting Gas Injection Problems in Vacuum Tank Degassing using Measurements of Multiple Variables.- Feature Assessment for a Hybrid Model.- Operability Forecasting Combining Neural Network and Survival Analysis with an Application to Hot Corrosion in Turbofan.- Active Monitoring of RCF in Toroidal Bearings using Acoustic Emission.- Fatigue Risk Analysis with Intelligent Digital Twins based on Condition Monitoring.- Low-end Hardware in Stress Monitoring of CNC Machines.- Real Order Derivatives and Spectral Norms in Fault Detection.- Systematic Methodology for Generating Natural Spall Faults on Rolling Element Bearings.- Physics-Inspired Feature Engineering for Condition Monitoring of Alternating Current Powered Solenoid-Operated Valves.- Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump using a Hybrid Model.
Data Sharing Concept for Electric Car Services: Fleet Level Optimization and Emission Reduction based on Monitored Data.- Unsupervised Deep Neural Network Considering the Uncertainties Effect in Pipeline Condition Monitoring using Guided Ultrasonic Waves.- Detecting Gas Injection Problems in Vacuum Tank Degassing using Measurements of Multiple Variables.- Feature Assessment for a Hybrid Model.- Operability Forecasting Combining Neural Network and Survival Analysis with an Application to Hot Corrosion in Turbofan.- Active Monitoring of RCF in Toroidal Bearings using Acoustic Emission.- Fatigue Risk Analysis with Intelligent Digital Twins based on Condition Monitoring.- Low-end Hardware in Stress Monitoring of CNC Machines.- Real Order Derivatives and Spectral Norms in Fault Detection.- Systematic Methodology for Generating Natural Spall Faults on Rolling Element Bearings.- Physics-Inspired Feature Engineering for Condition Monitoring of Alternating Current Powered Solenoid-Operated Valves.- Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump using a Hybrid Model.
Data Sharing Concept for Electric Car Services: Fleet Level Optimization and Emission Reduction based on Monitored Data.- Unsupervised Deep Neural Network Considering the Uncertainties Effect in Pipeline Condition Monitoring using Guided Ultrasonic Waves.- Detecting Gas Injection Problems in Vacuum Tank Degassing using Measurements of Multiple Variables.- Feature Assessment for a Hybrid Model.- Operability Forecasting Combining Neural Network and Survival Analysis with an Application to Hot Corrosion in Turbofan.- Active Monitoring of RCF in Toroidal Bearings using Acoustic Emission.- Fatigue Risk Analysis with Intelligent Digital Twins based on Condition Monitoring.- Low-end Hardware in Stress Monitoring of CNC Machines.- Real Order Derivatives and Spectral Norms in Fault Detection.- Systematic Methodology for Generating Natural Spall Faults on Rolling Element Bearings.- Physics-Inspired Feature Engineering for Condition Monitoring of Alternating Current Powered Solenoid-Operated Valves.- Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump using a Hybrid Model.
Data Sharing Concept for Electric Car Services: Fleet Level Optimization and Emission Reduction based on Monitored Data.- Unsupervised Deep Neural Network Considering the Uncertainties Effect in Pipeline Condition Monitoring using Guided Ultrasonic Waves.- Detecting Gas Injection Problems in Vacuum Tank Degassing using Measurements of Multiple Variables.- Feature Assessment for a Hybrid Model.- Operability Forecasting Combining Neural Network and Survival Analysis with an Application to Hot Corrosion in Turbofan.- Active Monitoring of RCF in Toroidal Bearings using Acoustic Emission.- Fatigue Risk Analysis with Intelligent Digital Twins based on Condition Monitoring.- Low-end Hardware in Stress Monitoring of CNC Machines.- Real Order Derivatives and Spectral Norms in Fault Detection.- Systematic Methodology for Generating Natural Spall Faults on Rolling Element Bearings.- Physics-Inspired Feature Engineering for Condition Monitoring of Alternating Current Powered Solenoid-Operated Valves.- Advanced Prognostics for a Centrifugal Fan and Multistage Centrifugal Pump using a Hybrid Model.
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