Machine Learning for Condition Monitoring and Innovation

Niels Henrik Pontoppidan; Tue Lehn-Schiøler; Kaare Brandt Petersen

ICASSP 2019accepted0 citations

Machine Learning for Condition Monitoring and Innovation

Niels Henrik Pontoppidan, Tue Lehn-Schiøler, Kaare Brandt Petersen

Abstract

This paper is a tribute to the late Professor Jan Larsen's work on machine learning for condition monitoring of large diesel engines. We present the ultrasound signals used to monitor the condition of the large engines and revisit two methods for estimating the condition of the engines from the ultrasound signals. Finally, we touch upon the increased importance of condition monitoring in large diesel engine industry today.

BibTeX

@inproceedings{icassp2019_machinelearningf,
  title = {Machine Learning for Condition Monitoring and Innovation},
  author = {Niels Henrik Pontoppidan and Tue Lehn-Schiøler and Kaare Brandt Petersen},
  booktitle = {ICASSP 2019},
  year = {2019}
}