@inproceedings{55336,
  abstract     = {{Predicting the remaining useful life of technical 
systems has gained significant attention in recent years due to 
increasing demands for extending the lifespan of degrading system 
components. Therefore, already used systems are retrofitted by 
integrating sensors to monitor their performance and 
functionality, enabling accurate diagnosis of their condition and 
prediction of their remaining useful life. One of the main 
challenges in this field is identified in the missing data from the 
time where the retrofitted system has already run but without 
being monitored by sensors. In this paper, a novel approach for 
the combined diagnostics and prognostics of retrofitted systems is 
proposed. The methodology aims to provide an accurate diagnosis 
of the system’s health state and estimation of the remaining useful 
life by a combination of a machine learning and expert knowledge. 
To evaluate the effectiveness of the proposed methodology, a case 
study involving a retrofitted system in an industrial setting is 
selected and applied. It is demonstrated that the approach 
effectively diagnose the current system’s health state and 
accurately predict its remaining useful life, thereby enabling 
predictive maintenance and decision-making. Overall, our 
research contributes to advancing the field of condition 
monitoring for retrofitted systems by providing a comprehensive 
methodology that addresses the challenge of missing data.}},
  author       = {{Bender, Amelie and Aimiyekagbon, Osarenren Kennedy and Sextro, Walter}},
  booktitle    = {{Proceedings of the 2024 Prognostics and System Health Management Conference (PHM)}},
  isbn         = {{979-8-3503-6058-5}},
  keywords     = {{retrofit, diagnosis, prognostics, RUL prediction, missing data, ball bearings}},
  location     = {{Stockholm, Schweden}},
  publisher    = {{IEEE Computer Society}},
  title        = {{{Diagnostics and Prognostics for Retrofitted Systems: A Comprehensive Approach for Enhanced System Health Assessment}}},
  doi          = {{10.1109/PHM61473.2024.00038}},
  year         = {{2024}},
}