Neural network data processing for analysis of the industrial networks parameters

We used artificial neural networks and diagnostic network information to assess the condition of PROFINET (Process Field Network). An artificial neural network determines whether the network works fine or not. An important part of this work is data preprocessing. An essential part of the work is data preprocessing. It is done using quantization, data aligning, reducing the number of inputs and other preprocessing techniques to create a new version of the dataset to improve the accuracy. The obtained data makes possible to do a number of experiments and to find out what approach of data preprocessing shows the best results. The results were evaluated on two datasets. The first dataset contains diagnostic data of a well-functioning network, and the second one consists of data in which network problems were detected. The highest accuracy obtained in this work is 98.91% of recognizing problems in the network and the accuracy of 87.70% when the network is working fine. The work also opens up opportunities to improve accuracy in the future.