Publications

Thesis

The pyvib module was built during my PhD education at University of Agder, Grimstad, Norway. Because of the open-source community, I was able to produce most of my thesis with LaTeX, Inkscape and most importantly Python. In addition, the PhD thesis was funded by the public: Ministry of Education in Norway. In this spirit, I release the pyvib module to the public.

Here is a link to download the Thesis at the University of Agder AURA.

1

Andreas Klausen. Condition monitoring of rolling element bearings during low and variable speed conditions. Ph. D. thesis, University of Agder, 2019.

Relevant Papers

Here is a list of relevant papers written during my PhD.

1

Surya Teja Kandukuri, Andreas Klausen, Hamid Reza Karimi, and Kjell Gunnar Robbersmyr. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable and Sustainable Energy Reviews, 53:697–708, 2016.

2

Andreas Klausen, Roy Werner Folgerø, Kjell G Robbersmyr, and Hamid Reza Karimi. Accelerated bearing life-time test rig development for low speed data acquisition. Modeling, Identification and Control, 38(3):143–156, 2017.

3

Andreas Klausen and Kjell G Robbersmyr. Cross-correlation of whitened vibration signals for low-speed bearing diagnostics. Mechanical Systems and Signal Processing, 118:226–244, 2019.

4

Martin Hemmer, Andreas Klausen, Khang Huynh, Kjell Gunnar Robbersmyr, and Tor Inge Waag. Simulation-driven deep classification of bearing faults from raw vibration data. International Journal of Prognostics and Health Management, 2019.

5

Andreas Klausen, Huynh V Khang, and Kjell G Robbersmyr. Multi-band identification for enhancing bearing fault detection in variable speed conditions. Mechanical Systems and Signal Processing, 139:106422, 2020.

6

Martin Hemmer, Andreas Klausen, Huynh Van Khang, Kjell G Robbersmyr, and Tor I Waag. Health indicator for low-speed axial bearings using variational autoencoders. IEEE Access, 8:35842–35852, 2020.

7

Andreas Klausen, Kjell G Robbersmyr, and Hamid R Karimi. Autonomous bearing fault diagnosis method based on envelope spectrum. IFAC-PapersOnLine, 50(1):13378–13383, 2017.

8

Surya Teja Kandukuri, Andreas Klausen, Van Khang Huynh, and Kjell G Robbersmyr. Fault diagnostics of wind turbine electric pitch systems using sensor fusion approach. Journal of Physics: Conference Series, 1037(3):032036, 2018.

9

Andreas Klausen, Huynh Van Khang, and Kjell G Robbersmyr. Novel threshold calculations for remaining useful lifetime estimation of rolling element bearings. XIII International Conference on Electrical Machines (ICEM), pages 1912–1918, 2018.

10

Mohamed A. A Ismail and Andreas Klausen. Multiple defect size estimation of rolling bearings using autonomous diagnosis and vibrational jerk. 7th World Conference on Structural Control and Monitoring, 2018.

11

Arild Bergesen Husebø, Surya Teja Kandukuri, Andreas Klausen, and Kjell Gunnar Robbersmyr. Rapid diagnosis of induction motor electrical faults using convolutional autoencoder feature extraction. PHM Society European Conference, 5(1):10, 2020.

12

Andreas Klausen, Johannes Kalaoja, Surya Teja Kandukuri, and Kjell G Robbersmyr. Sensitivity analysis of online oil quality monitoring for early detection of water ingress in marine propulsion systems. PHM Society European Conference, 5(1):10, 2020.

Datasets

Some of the datasets that were created using a custom test-rig are available here

1

Andreas Klausen. UiA Accelerated Life Time Bearing Test Rig – Test 3, Variable speed around 50rpm. DataverseNO, 2021. URL: https://doi.org/10.18710/NZJOIS, doi:10.18710/NZJOIS.

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Andreas Klausen. UiA Accelerated Life Time Bearing Test Rig – Test 1, 250 rpm. DataverseNO, 2020. URL: https://doi.org/10.18710/BG1QNG, doi:10.18710/BG1QNG.