# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 7 Conclusions  
In the paper, a simple procedure for long-term diagnostic data segmentation is proposed. The procedure is developed to find the regime change point, i.e., borders  
Figure 39: The segmentation results based on clustering algorithm over whole of wind turbine data: (**a**) results for EM algorithm, (**b**) results for K-Means algorithm, (**c**) results for Birch algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **1 st Point** & **2 nd Point** \\ \hline EM & Undefine & Undefine \\ K-Means & Undefine & Undefine \\ BIRCH & Undefine & Undefine \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Final changing points for wind turbine data set. Figure 38: Wind turbine clustering: (**a**) results for EM algorithm, (**b**) results for K-Means algorithm, (**c**) results for Birch algorithm. between three regimes corresponding to three phases of machine life: good condition, slow linear degradation and fast (exponential) degradation. To achieve the goal, we propose the statistical parameterization of the data performed locally, i.e., using moving short window and then simple techniques for clustering. It is found that for Gaussian distribution of noise in the data, the efficiency of the method is good--we are able to detect regime change point. As the difference between good condition and beginning of slow degradation is not really significant, border detection is a bit biased. It is not critical (the machine is still in almost good condition). Unfortunately, non-Gaussian distribution of the noise influences the procedure in several ways. First, the idea of moving window is not really suitable because one outlier will provide several features with bias. Moreover, simple statistics suitable to describe data with Gaussian noise are not optimal for non-Gaussian data. For example, the mean value will be affected by outliers; if the window is short, this influence may be significant. Standard deviation is even more sensitive to outliers, and some heavy-tailed processes cannot use variance at all as it is not defined for such processes. Finally, PCA should not be used in the classical form for heavy-tailed data because it maximize variance (model will follow outliers) and all these steps will impact regime change point detection. This implies a need to search for new approaches. For future work, it is planned that used statistics may be replaced with a robust version (median instead of mean, robust scale instead of variance, etc.). There are many solutions for dimensionality reduction for non-Gaussian data sets. Additionally, clustering methods for non-Gaussian data are available. The main purpose of this work is to highlight (by simulated and real data analysis) that the non-Gaussian character of the random component in our long-term data should be investigated and evaluated also during the segmentation step before data modeling for machine health prognosis. Conceptualization, H.S. and A.W. ; Funding acquisition, A.W. ; Investigation, H.S. and J.W. ; Methodology, F.M., H.S., J.W., A.W. and R.Z. ; Project administration, A.W. and R.Z. ; Software, F.M. and R.Z. ; Writing--review & editing, J.W. and R.Z. All authors have read and agreed to the published version of the manuscript. Part of this work was supported by European Commission via the Marie Sklodowska Curie program through the ETN MOIRA project (GA 955681)--[PERSON]. Project no. POIR.01.01.01-00-0350/21 entitled \"A universal diagnostic and prognostic module for condition monitoring systems of complex mechanical structures operating in the presence of non-Gaussian disturbances and variable operating conditions\" co-financed by the European Union from the European Regional Development Fund under the Intelligent Development Program. The project is carried out as part of the competition of the National Center for Research and Development no. : 1/1.1.1/2021 ([PERSON])--[PERSON], [PERSON] and [PERSON]. Not applicable. Archived data sets cannot be accessed publicly according to the NDA agreement signed by the authors.