Method of creation of a two-level neural network structure for solving problems in mechanical engineering

There is an industrial trend of increasing technological effectiveness of production and growing autonomy of factories, resulting in generation of huge amounts of raw data that can be used to improve efficiency and continuity of industrial plants operation. Big data collection, processing and analysis technologies are already being actively implemented. Further use of big data will help with a variety of tasks, such as optimization and process monitoring, quality control of equipment and produced parts, modeling and forecasting of the facility operation and other mechanical engineering challenges. A new method of creation of a two-level neural network structure is proposed, designed to solve a number of problems by training individual neural networks for each subset of data used in the task at hand. This method combines two levels of information processing: the first level of the neural network classifier and the second level, which includes several neural network analyzers. Depending on the specific subject area and the data sets available, it is possible to use the method to solve various problems in mechanical engineering. The method allows to add new neural network analyzers and expand the scope of application. The practical application of the method in solving the problem of text message sentiment analysis is shown and an example of the Python programming language software implementation of the two-level structure is given. Use cases for the two-level structure method in mechanical engineering tasks are proposed. In addition, the proposed method can be used as a part of the hybrid intelligent information system that includes mivar expert systems. Combining neural networks with mivar expert systems as part of a hybrid intelligent information system is a promising direction for the development of artificial intelligence for mechanical engineering.