New technologies bring opportunities to deploy AI and machine learning to the edge of the network, allowing edge devices to train simple models that can then be deployed in practice. Federated learning (FL) is a distributed machine learning technique to create a global model by learning from multiple decentralized edge clients. Although FL methods offer several advantages, including scalability and data privacy, they also introduce some risks and drawbacks in terms of computational complexity in the case of heterogeneous devices. Internet of Things (IoT) devices may have limited computing resources, poorer connection quality, or may use different operating systems. This paper provides an overview of the methods used in FL with a focus on edge devices with limited computational resources. This paper also presents FL frameworks that are currently popular and that provide communication between clients and servers. In this context, various topics are described, which include contributions and trends in the literature. This includes basic models and designs of system architecture, possibilities of application in practice, privacy and security, and resource management. Challenges related to the computational requirements of edge devices such as hardware heterogeneity, communication overload or limited resources of devices are discussed.

Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park’s Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

This study deals with the technology of autonomous mobile robots (AMR) and their implementation on the SmartFactory production line at the Technical University of Ostrava. The task of the mobile robot is to cooperate with the production line, take over the manufactured products, and then deliver them. The content also includes a description of the individual steps that were necessary to make the mobile robot operational, such as loading a virtual map of the space, creating a network for communication with the mobile robot, and programming it. The main part of the experiment deals with testing the accuracy of moving the mobile robot to each position and establishing communication between the production line and the mobile robot. A high accuracy is a necessity in this process. The result of the study is the configuration of the autonomous mobile robot. The repetitive precision of the approach of the autonomous mobile robot to a position is ±3 mm.

This article reviews and analyzes the approaches utilized for monitoring cutting tool conditions. The Research focuses on publications from 2012 to 2022 (10 years), in which Machine Learning and other statistical processes are used to determine the quality, condition, wear, and remaining useful life (RUL) of shearing tools. The paper quantifies the typical signals utilized by researchers and scientists (vibration of the cutting tool and workpiece, the tool cutting force, and the tool’s temperature, for example). These signals are sensitive to changes in the workpiece quality condition; therefore, they are used as a proxy of the tool degradation and the quality of the product. The selection of signals to analyze the workpiece quality and the tool wear level is still in development; however, the article shows the main signals used over the years and their correlation with the cutting tool condition. These signals can be taken directly from the cutting tool or the workpiece, the choice varies, and both have shown promising results. In parallel, the Research presents, analyzes, and quantifies some of the most utilized statistical techniques that serve as filters to cleanse the collected data before the prediction and classification phase. These methods and techniques also extract relevant and wear-sensitive information from the collected signals, easing the classifiers’ work by numerically changing alongside the tool wear and the product quality.