@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}},
}

@inproceedings{55429,
  abstract     = {{A detailed understanding of the cognitive process underlying diagnostic reasoning in medical experts is currently lacking. While high-level theories like hypothetico-deductive reasoning were proposed long ago, the inner workings of the step-by-step dynamics within the mind remain unknown. We present a fully automated approach to elicit, monitor, and record diagnostic reasoning processes at a fine-grained level. A web-based user interface enables physicians to carry out a full diagnosis process on a simulated patient, given as a pre-defined clinical vignette. By collecting the physician’s information queries and hypothesis revisions, highly detailed diagnostic reasoning trajectories are captured leading to a diagnosis and its justification. Four expert epileptologists with a mean experience of 19 years were recruited to evaluate the system and share their impressions in semi-structured interviews. We find that the recorded trajectories validate proposed theories on broader diagnostic reasoning, while also providing valuable additional details extending previous findings.}},
  author       = {{Battefeld, Dominik and Mues, Sigrid and Wehner, Tim and House, Patrick and Kellinghaus, Christoph and Wellmer, Jörg and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 46th Annual Conference of the Cognitive Science Society}},
  keywords     = {{Differential Diagnosis, Diagnostic Reasoning, Reasoning Process Analysis, Seizure, Epilepsy}},
  location     = {{Rotterdam, NL}},
  title        = {{{Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive Process Trajectories}}},
  year         = {{2024}},
}