MonitorX

MonitorX reports

Current status of condition monitoring in Norwegian and Swedish hydropower plant
T. Welte, M. Istad, M.L. Kolstad, E. Solvang (SINTEF)

Review of analytics methods supporting anomaly detection and condition based maintenance
M. A. Sanz-Bobi (Comillas University)

Models for lifetime and risk monitoring
T. Welte (SINTEF), J. Vatn (NTNU), M.A. Sanz-Bobi (Comillas University)

MonitorX - Summary of results, recommendations for use of results, and further work (present report)
T. Welte, J. Foros (SINTEF)

MonitorX – Articles

Mer vedlikehold for pengene
Atle Abelsen, Energiteknikk, no. 7, Oct. 2015, pp. 18-19

Nytteverdier av digital transformasjon
Thomas Welte, Børge Stafne (SINTEF), Energiteknikk, no. 2, March 2017, pp. 32-33

Digitalisering ska forbättra møjligheterna at hitta fel
Daniel Løfsted, ERA, no. 6, 2017, p- 33

Graver i fjellets hemmelige gullgruve
Claude Olsen, Gemini (web), December 2017

Deep Learning Approach to Multiple Features Sequence Analysis in Predictive Maintenance
Jin Yuan (NTNU, Shandong Agricultural University), Kesheng Wang (NTNU), Yi Wang (Plymouth University)
In: Wang K., Wang Y., Strandhagen J., Yu T. (eds) Advanced Manufacturing and Automation VII. IWAMA 2017. Lecture Notes in Electrical Engineering, no. 451, pp. 581-590

Allerede resultater fra MonitorX
Atle Abelsen, Energiteknikk, no. 5, June 2018, p. 38

Anomaly indicators for Kaplan turbine components based on patterns of normal behavior
Miguel A. Sanz-Bobi (Comillas), Thomas Welte (SINTF), Lasse Eilertsen (Glitre).
S. Haugen, C. van Gulijk, T. Kongsvik, J.E. Vinnem: Safety and Reliability – Safe Societies in a Changing World, CRC Press, June 2018, pp. 1003-1010

MonitorX – Experience from a Norwegian-Swedish research project on industry 4.0 and digitalization applied to fault detection and maintenance of hydropower plants
Thomas Welte, Jørn Foros (SINTEF), Martin H. Nielssen (Energy Norway), Monika Adsten (Energiforsk), Proceedings Hydro 2018

Bør finne digitale allierte
Atle Abelsen, Energiteknikk, no. 2, March 2019, pp. 16-17

LSTM Based Prediction and Time-Temperature Varying Rate Fusion for Hydropower Plant Anomaly Detection: A Case Study
Jin Yuan (NTNU), Yi Wang (Plymouth University), Kesheng Wang (NTNU).
In: Wang K., Wang Y., Strandhagen J., Yu T. (eds) Advanced Manufacturing and Automation VIII. IWAMA 2018. Lecture Notes in Electrical Engineering, vol. 484, pp. 86 - 94

Anomalideteksjon for å avdekke feil i vannkraftanlegg
Thomas Welte, Jørn Foros (SINTEF). Energiteknikk, no. 4, June 2019, pp. 36-37.

Anomaly detection method based on the evolution of patterns in industrial components. Application to a hydropower plant
Pablo Calvo Báscones, Miguel Ángel Sanz Bobi (Comillas), Thomas M. Welte (SINTEF). Engineering Applications of Artificial Intelligence, June 2019