Bienefeld, Christoph (2024)
A Contribution on the Transferability of Data-Driven Models for Bearing Fault Diagnosis.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00027475
Ph.D. Thesis, Primary publication, Publisher's Version

Bienefeld, Christoph ; Becker-Dombrowsky, Florian Michael ; Shatri, Etnik ; Kirchner, Eckhard (2023)
Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis.
In: Entropy, 2023, 25 (9)
doi: 10.26083/tuprints-00024494
Article, Secondary publication, Publisher's Version

Wenzel, Jan ; Bienefeld, Christoph ; Kretschmer, Alexander ; Kirchner, Eckhard (2022)
Introducing an Open-Source Simulation Model for Track Rollers Considering Friction.
In: Applied Mechanics, 2022, 3 (2)
doi: 10.26083/tuprints-00022186
Article, Secondary publication, Publisher's Version

Bienefeld, Christoph ; Kirchner, Eckhard ; Vogt, Andreas ; Kacmar, Marian (2022)
On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor.
In: Lubricants, 2022, 10 (4)
doi: 10.26083/tuprints-00021279
Article, Secondary publication, Publisher's Version

Kirchner, Eckhard ; Bienefeld, Christoph ; Schirra, Tobias ; Moltschanov, Alexander (2022)
Predicting the Electrical Impedance of Rolling Bearings Using Machine Learning Methods.
In: Machines, 2022, 10 (2)
doi: 10.26083/tuprints-00021023
Article, Secondary publication, Publisher's Version