| 1. | Moosavi, Forough; Shiri, Hamid; Wodecki, Jacek; Wyłomańska, Agnieszka; Zimroz, Radoslaw Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation Journal Article In: Applied Sciences, vol. 12, no. 13, 2022, ISSN: 2076-3417. @article{app12136766,
title = {Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation},
author = {Forough Moosavi and Hamid Shiri and Jacek Wodecki and Agnieszka Wyłomańska and Radoslaw Zimroz},
url = {https://www.mdpi.com/2076-3417/12/13/6766},
doi = {10.3390/app12136766},
issn = {2076-3417},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Applied Sciences},
volume = {12},
number = {13},
keywords = {condition monitoring, Heavy-tailed Distributions, Machine Learning, Modeling, MOIRA, Predicon, statistical analysis, Vibration},
pubstate = {published},
tppubtype = {article}
}
|
| 2. | Goralczyk, Mateusz; Michalak, Anna; Sliwinski, Pawel Drill bit deterioration estimation with the Random Forest Regressor Journal Article In: IOP Conference Series: Earth and Environmental Science, vol. 942, no. 1, pp. 012013, 2021. @article{Gralczyk2021,
title = {Drill bit deterioration estimation with the Random Forest Regressor},
author = {Mateusz Goralczyk and Anna Michalak and Pawel Sliwinski},
url = {https://doi.org/10.1088/1755-1315/942/1/012013},
doi = {10.1088/1755-1315/942/1/012013},
year = {2021},
date = {2021-12-08},
urldate = {2021-12-08},
journal = {IOP Conference Series: Earth and Environmental Science},
volume = {942},
number = {1},
pages = {012013},
publisher = {IOP Publishing},
keywords = {drilling rig, Machine Learning, prediction, process performance, Random Forest Regressor, SafeMe4Mine},
pubstate = {published},
tppubtype = {article}
}
|
