Signal processing and feature extraction for automated machine diagnostics: methodology and experiments
Thesis event information
Date and time of the thesis defence
Topic of the dissertation
Signal processing and feature extraction for automated machine diagnostics: methodology and experiments
Doctoral candidate
Master of Science (Technology) Konsta Karioja
Faculty and unit
University of Oulu Graduate School, Faculty of Technology, Intelligent Machines and Systems
Subject of study
Machine diagnostics
Opponent
Professor Matti Vilkko, Tampere University
Custos
Professor Enso Ikonen, University of Oulu
Methods for automation of machine diagnostics
The objective of the study in this doctoral thesis was to determine methods suitable for automated machine diagnostics. Successful automated diagnostics requires methods that are sufficiently sensitive to faults, and often the capability of identifying the fault and keeping track of its progression is desirable.
The methods introduced in the thesis allow e.g. automatic determination of the frequency range in which the observed changes have taken place. Separating overall level changes from phenomena that cause shock-like vibration is also possible. Moreover, the proposed stress evaluation methodology can be utilised to monitor cumulative stress levels and generate forecasts of the risk level for the faults that occur. The thesis includes an overview of methods and study of their applicability through practical experiments. The primary focus of this study is on vibration measurements, but the methods can be applied to any type of signal.
Succesful diagnostics provides opportunities for optimising maintenance actions. Proper maintenance significantly improves the reliability and safety of machinery. In addition, well-performed maintenance can save energy.
The methods introduced in the thesis allow e.g. automatic determination of the frequency range in which the observed changes have taken place. Separating overall level changes from phenomena that cause shock-like vibration is also possible. Moreover, the proposed stress evaluation methodology can be utilised to monitor cumulative stress levels and generate forecasts of the risk level for the faults that occur. The thesis includes an overview of methods and study of their applicability through practical experiments. The primary focus of this study is on vibration measurements, but the methods can be applied to any type of signal.
Succesful diagnostics provides opportunities for optimising maintenance actions. Proper maintenance significantly improves the reliability and safety of machinery. In addition, well-performed maintenance can save energy.
Last updated: 20.3.2025