## 6 Discussion  
We were surprised that such a simple solution gave such high accuracy (up to approx. 87%), but it should be remembered that in the study we used real industrial data, and therefore the model is adapted to their structure, which can be relatively simple and quite easily predictable at the level of machine learning. We would also pay particular attention that computationally larger solutions cannot be trained on mobile devices; only learned neural networks can be transferred as inference. Discussing ANN's accuracy for each category and the ANN efficiency showed that we should take into consideration that accuracy alone does not provide full information about the effectiveness of ANN when using a dataset that is unbalanced across categories. However, it seems that in the case under study, i.e., the use of data from a real production line, we have to accept some data imbalance and resulting inaccuracy, as some types of failures are inherently more common than others. Due to the fact that this frequency is important for the operation of the algorithm, it is not always possible to artificially balance the data--in the target system, there is a need to create a framework for quantifying estimation reliability based on the current expert knowledge of technologists and maintenance engineers in the area in which the maintained system works industrial. The achieved results are worth interpreting and looking at from a broader perspective, taking into account the results and conclusions of previous studies. There are differences in the approaches to the development, implementation and modernization of maintenance solutions depending on the data analyzed and the models used to draw conclusions from the data and predict failures. We have found more than 30 studies concerning AI/ML-based maintenance within Industry 4.0 paradigm, but their spectrum of topics is very wide (from eHealth devices to bug detection in software) and it is hard to compare with our results. The review shows that with very advanced, complex and expensive solutions, failure prediction accuracy of 96% can be achieved. However, it makes sense to look for cheaper and simpler solutions for optimization in smaller companies that cannot always afford world-class solutions. The situation is similar to enterprise resource planning (ERP) class systems--initially intended for large enterprises, as the price was slashed, they also came to medium and small enterprises as a solution for increasing their efficiency and automating some of the functions previously performed by people. Gathering, analysis, and selection of data from the production process, technical control (in Industry 4.0 at every stage of production) and the entire life cycle allow you to search for and extract the most beneficial contribution to product development or even the entire technology, and not just to prevent anomalies on the production line. This can be used not only for predictive maintenance but also to improve actions aimed at ensuring a sufficiently high-quality mass-produced product, forecasting demand or material problems (e.g., the possibility of substituting one material for another), and better prioritization and scaling of resources [62]. It also favors faster adaptation to changes in order to maintain product competitiveness by testing the product or production process in a digital model [50]. The so-called constraint satisfaction, i.e., reconciling seemingly conflicting interests, e.g., customer satisfaction, sufficiently high quantity and high quality of production, with the lowest possible production costs. Anomaly detection for the maintenance of 5G networks has been developed NaConvAE-Latency, which coordinates multiple targets for correlation between multiple metrics [63]. The research to date shows that the wider implementation of PdM in production plants is limited by practical issues: the availability, sufficiency, completeness and reliability of the data gathered (all machine maintenance conditions and possible anomalies, contextual information) [11].