# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 2 State of the Art  
There is a lot of data collected every minute in various branches of the economy, such as industry, financial sector and many others. Data regarding currency exchange [1], stock exchange, raw materials prices [2; 3], energy consumption/prices [4; 5], etc., are collected, processed and analyzed to understand the nature of important phenomena. However, each case is specific and requires appropriate methodology for data analysis. In this paper, we focus on long-term data from condition monitoring (CM) systems; however, some inspirations from other areas are also helpful. Nowadays, the condition monitoring systems are frequently used to monitor current state of the given machine. Although there are at least several freely-available data sets, there are just a few practical examples of how to process and analyze long-term data acquired in industrial installations. There are many papers on data modeling for prognosis, but these works seem to be more theoretical (data sets from test rig  
Figure 1: Long-term data variation model used in this paper. tests with artificially initiated fault, etc.) than really applied to the actual industrial systems with natural degradation processes. Just to mention several practical implementations, one may focus on [6] where the procedure of load-dependent features processing with application to wind turbine bearings was proposed. Another example proposed by [PERSON] et al. [7] discussed a comparative study on statistical and energy-based parameters analysis in a long-term context, also for a wind turbine. [PERSON] et al. [8] analyzed the overheating problem for heavy-duty machines operating in the underground mine. Their method focused on temperature change detection in the long-term data set using Anderson-Darling statistics. [PERSON] et al. [9] proposed a statistical test for anomaly detection in long-term temperature data analysis for gearbox diagnostics used in underground mine. [PERSON] et al. [10] introduced a hybrid prognostics method for estimating the remaining useful life of wind turbine bearings. [PERSON] et al. [11] developed an approach for predicting for a lithium-ion battery under vibration stress using the Elman neural network. Analysis of the non-stationary data using moving window is commonly used to investigate local properties of the data, and was used, among others, by [PERSON] et al. (moving window and kurtosis estimation in raw vibration for local damage detection) [12] or [PERSON] et al. (moving window for HI from wind turbine condition monitoring system to estimate regression between health index (HI) and operational parameters) [6]. It was also shown that window selection is critical in forecasting [13]. In signal processing, the window length selection was clearly discussed in [14], where short time Fourier transform (STFT) was proposed to analyze non-stationary data. [PERSON] et al. [15] introduced a methodology based on long short-term memory for predicting gear remaining useful life (RUL). As mentioned, simple statistics such as sample mean, sample variance, sample kurtosis, etc., are often used in condition monitoring for signal parameterization. In this paper, it is proposed to use such statistical descriptors in order to differentiate segments from different regimes. Initially, several statistics with theoretical potential in considered context are proposed. Finally, the mean value, standard deviation (STD) and root mean square (RMS) are applied for each segment. Machine learning is frequently used for long-term data analysis for classification, detection, prognosis, etc., [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. In this case, unsupervised classification, i.e., clustering, is required because every machine works in different conditions, and labeling data is not an easy task. Hence, the clustering approach has massive potential for dividing long-term data into several regimes. The authors of [17] proposed the long short-term memory (LSTM) network with the clustering method for multi-stage predicting RUL. [PERSON] et al.