## 1 Introduction  
In the conditions of the impermanence of the modern world and the economy, the issue of ensuring the safety and continuity of production is acute in all spheres [1]. For these purposes, strategies for the development and transformation of the relevant industries have been adopted in Russia and Slovakia. In Slovakia, the main document is <<The 2030 Strategy for Digital Transformation of Slovakia>> [2]. In Russia, this is the action plan (\"a road map\") \"TECHNET\" (advanced production technologies) of the National Technology Initiative for 2020-2036 years [3]. The listed regulatory documents are supplemented with specialized industry ones depending on the specific industry sector.  
The basis of modern industries consists of complex and multifactorial technical systems that have a certain structural and functional organization and have all the parameters of complex systems [4].  
In Refs. [5; 6; 7] a technical system is an artificially created object that interacts with the external environment and has a complex internal structure, a large number of components and elements. It can exist as [8]:* product of manufacture;
* device or equipment potentially ready to produce a beneficial effect;
* technological process of transformation or interaction with environmental components resulting in a beneficial effect;  
A part of every more complex industrial machine is a PLC system, which has output-input peripherals--whether they are PLC cards with digital or analog inputs or more complex output devices such as, e.g., frequency converters and devices for controlling various other drives and actuators [9]. It is some sensed quantities that may indicate problems and incorrect operation of the device, e.g., low values sensed by the pressure sensor may indicate a problem with the hydraulic system, in particular, the hydraulic hose may be ruptured or the hydraulic pump may be damaged. Knowing such behavior of the equipment in advance is important in terms of cost savings for repairing the equipment, especially if such damage can be prevented by specific maintenance actions. In this article, we have decided to avoid available commercial analytical platforms such as Microsoft Power BI, Thingworx Analytics, or others that require frequent interventions by a data analyst in the form of setting up structures, types of models designed for predictive purposes, etc. [10,11].  
One of the possible solutions is to implement a predictive module in the control systems, which will estimate the expected development of specific quantities based on their historical data. We decided to use nonlinear autoregressive models of NAR neural networks for such prediction [12]. The reason why we decided to use nonlinear autoregressive networks is that their structure is almost identical with the feed-forward neural networks. They differ only in the input layer, which is in this case called time delay layer. Therefore, there is a possibility to use open-sourced libraries which are offering only feed-forward neural network models. The time delay input number can be created in a script or a program. Other reason is that for forecasting the desired quantity or parameter the model only needs a historical record only of this one quantity or parameter. Training data set is created from this time series data [13, 14, 15]. In general, neural network models require higher computational demands, especially the training process of models. Therefore, the industrial machine must be supplemented with IPC, which has a higher computing power than most PLCs used in industrial machines.