# Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry
## 1 Introduction
The study points out that in order to obtain the best performance and accuracy of object detection, it is necessary to train the neural network on a special dataset consisting of synthetic data with segmented masks of original photos. Three modules are put forward in [12]. They include a module that considers the object relations in the frame, a module that considers the scale of objects, and a cross-transformation module to more effectively deal with the scene change detection challenge. References [31,32] demonstrate a Markov chain Monte Carlo method to segment the video into scenes. This method calculates the probability of finding the scene boundary at each iteration. They [33] developed an algorithm that automatically splits a video into short scenes using video, audio tracks, and subtitles. The resulting scenes are indexed based on the subtitles and stored in a database. The suggested technique [4] for splitting video into scenes, as well as others, trains a neural network to extract on-frame features from the video and splits them based on grouping. The study [34] describes a way to partition into scenes using the OSG (Optimal Sequential Grouping) approach. Scientists have extended the capabilities of this method for training neural networks. IBM scientists extract features from each frame using deep neural network models for visual and audio analysis. Based on those traits, a distance matrix is created from which an optimal sequence grouping (OSG) algorithm can be performed, resulting in the boundary detection of the video [35]. In the work [36], frames are combined into groups based only on their visual similarity, and each  
Figure 1: Hierarchical structure of video. frame is assigned a label. A sequence alignment algorithm is then applied to detect changes in frame labels, which provides the final result of scene segmentation. Among the previously listed studies, few researchers have applied singular value decomposition (SVD) to determine the principal components. In our work, we use singular spectral analysis (SSA), namely, an algorithm derived from it that processes multivariate singular spectral analysis (MSSA). The study of time series is often used to predict and study the behavior of a signal. Many scientific works are devoted to this area of research, applying exactly the singular spectral decomposition method (SSA) [37; 38]. For example, this work [39] performs forecasting of Iran's national accounts and [40] predicts the inflation rate in the United States. In addition, as the name of the method suggests, SSA is used to decompose the time series into several interpretable components that enable us to draw some conclusions and obtain new insights on the time series. Such research includes work in the field of equipment fault investigation [41; 42; 43; 44; 45]. For example, in [46], scientists determine the nature of defects in induction motor bearings by analyzing current and voltage [47]. The work [48] describes the method's applications in order to suppress noise in the signals coming from the antenna. Similarly, the effectiveness of SSA is reflected in the study [49] in filtering electromagnetic noise that appears in radio-electronic systems and is one of the most active types of interference. An eye blink detection method is proposed in [50], which can be used in driver fatigue systems as well as other applications. Based on analyzing the electroencephalograph (EEG) signal, which records brain signals, researchers using SSA extract a component in which the signals responsible for eye movement are identified. A great contribution to the study of the SSA method was made by works [51; 52; 53]. The above works describe the nature of SSA application to time series analysis and forecasting and consider derivative methods that were formed on the basis of SSA. One of such methods is multivariate time series analysis, the so-called multichannel SSA, and the name multivariate SSA is also encountered.