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This paper has been prepared with the support of the DZRO
MOBAUT of the K-202 University of Defence in Brno, Czech Republic; University of Economics and Innovation in Lublin (WSEI), Poland and Lublin University of Technology, Poland-Regional
Excellence Initiative, funded by the Polish Ministry of Science
and Higher Education (contract no. 030/RID/2018/19).
Technical systems used in adverse environments are subject to very intense degradation and their parts deterioration. Due to the problematic placement of some parts, it is sometimes very difficult, to indicate the level of degradation and possible failure occurrence. Therefore, it is very useful to work with the available field operation data. Since we possess such data and apply progressive methods to model the degradation, we are able to predict the possible failure occurrence and forecast residual useful life. At first, we apply spectral analysis approaches. The spectral analysis is used to capture extreme values in the data structure. The extreme values are later filtered out to avoid future estimations which might be affected by the deformed inputs. In the next step, we use non-parametric smoothing and state space models to acquire trend, variance and related statistics in the data structure. These characteristics are later used as input parameters for specific and new forms of diffusion processes. With these diffusion processes we would like to model the degradation evolvement and failure occurrence. The failure occurrence is represented as one of the statistics of the first passage time (FPT). FPT is a moment when the modelled trajectory hits the predefined threshold – such threshold represents a critical limit for our observation.
The outcomes are useful for i) degradation modelling, deterioration prediction and condition assessment, ii) operation and maintenance planning and rationalisation, and iii) life cycle cost optimisation and safety improvement.
