Data-driven Strain Sensor Modelling in Mining Applications : Artificial strain sensors for material fatigue estimation

Abstract: When boring machines are used, large loads are exerted on their structure. The load cycles cause material fatigue on the boring machine structure. If the material fatigue can be estimated in real-time, maintenance can be planned more efficiently and the effect of different types of usage can be evaluated. Because of the many advantages of knowing the material fatigue, the goal of this thesis is to develop a model to predict the strain of a boring machine structure and then derive an estimate of the material fatigue caused by the strain. To do this several approaches using machine learning techniques are evaluated. The input signals were selected using both coherence analysis and mutual information. It was found that linear models outperform the tested non-linear model structures, and that non-linear mechanical connections cause difficulties. The signals to be modelled contained high frequency components that were not present in the available input signals. The results show that given favorable sensor positions, an estimate of the material fatigue can be made with sufficient accuracy when using a noise model and noise realization to cover the non-existent high frequency components.