Automated methods for vibration-based condition monitoring of rotating machines

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

OP-Pohjola auditorium (L6), Pentti Kaiterankatu 1, Linnanmaa, Oulu

Topic of the dissertation

Automated methods for vibration-based condition monitoring of rotating machines

Doctoral candidate

Master of Science in Technology Riku-Pekka Nikula

Faculty and unit

University of Oulu Graduate School, Faculty of Technology, Environmental and Chemical Engineering

Subject of study

Process Engineering

Opponent

Professor Matti Vilkko, Tampere University

Custos

Professor Mika Ruusunen, University of Oulu

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Automation facilitates data analysis in condition monitoring

The sustainable and safe use of rotating machines can be enhanced by condition monitoring. Condition is often measured indirectly by using accelerometers but the analysis of measurements can be complicated. Data-driven methods can enhance the time management and accuracy of analysis but their implementation is challenging in real measurement environments.

In this thesis, automated methods were developed to facilitate the implementation of condition monitoring algorithms. Additionally, methods were studied for the automated detection of anomalies in acceleration signals. The methods were studied based on measurement data from azimuth thrusters and a roller leveler, and based on data from rolling element bearings in laboratory and simulation tests.

The results indicated that automated selection of training samples systematized the identification of models and their operating areas. Automated feature selection also revealed previously unknown dependencies between the acceleration signals and operating parameters of a machine. In addition, certain patterns of local faults in rolling element bearings could be detected automatically from short time series that contained only a few fault impulses. The results of this dissertation can be utilized in condition monitoring applications in real measurement environments, where adaptive and automated methods are required.
Last updated: 22.11.2022