T33-01 | Interpretable Survival Models for Predictive Maintenance
Paul Castle, Janet Ham, Melinda Hodkiewicz and Adriano Polpo |
T33-02 | Low-Cost Solutions for Maintenance with a Raspberry Pi
Martin Larrañaga, Riku Salokangas, Olli Saarela and Petri Kaarmila |
T33-03 | Machine Learning-Enabled Modeling Approach for Predictive Mainte-nance Decision-Making Support
Chunsheng Yang, Yubin Yang, Xiaohui Yang and Qiangqiang Cheng |
T33-04 | The SUPREEMO Experiment for Smart Monitoring for Energy Efficiency and Predictive Maintenance of Electric Motor Systems
S. Kotsilitis, K. Chairetakis, A. Katsari and E. Marcoulaki |
T33-05 | Degradation Modelling of Centrifugal Pumps as Input to Predictive Maintenance
Tom Ivar Pedersen, Jørn Vatn and Kim A. Jørgensen |
T33-06 | Modeling Turbocharger Failures using Markov Process for Predictive Maintenance
Mahmoud Rahat, Sepideh Pashami, Slawomir Nowaczyk and Zahra Kharazian |
T33-07 | Data Analysis to Facilitate Offshore Seawater Ultrafiltration Membrane Replacement Decision and Scheduling of Chemical Wash
Abu MD Ariful Islam and Jørn Vatn |
T33-08 | Remaining Useful Life Estimation Using Vibration-based Degradation Signals
Bahareh Tajiani, Jørn Vatn and Viggo Gabriel Borg Pedersen |
T33-09 | Condition Monitoring and Reliability of a Resistance Spot Welding Process
Matteo Strozzi, Marco Cocconcelli, Riccardo Rubini, Gianmarco Genchi and Alessandro Zanella |
T33-10 | Avenues For Future Research on Predictive Maintenance Purposes in Terms of Risk Minimization
Rim Louhichi, Mohamed Sallak and Jacques Pelletan |
