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.

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

The presented research addresses the problem of dependency analysis of the ultrasonic signal measured by a sensor in an engine oil bath. The dependency analysis is performed on a selected ultrasonic signal sensor solution containing its own generator and an ultrasonic signal receiver detecting the level of the oil in which it is immersed. The influence of the resulting amplitude of the received ultrasonic signal is mainly due to the level of the measured oil level and the oil temperature, as shown by the regression analysis and ANOVA (Analysis of Variance) testing performed. The analyzed dependence of the time determination of the length of the generated ultrasonic signal envelope is given by a set threshold value, which can be dynamically adjusted based on the backtracking evaluation. The analysis results in the form of approximation by the dependency algorithm confirm the assumption of possible standardization of the envelope parameters with the achievement of accuracy up to 99.02%. The analyzed parameters approximated by the temperature and oil level dependence algorithms include the amplitude of the measured signal, steepness of the rising edge, duration of the envelope, and the digitally processed amplitude value.

The integration of autonomous mobile robots in Smart Home and their secure communication within Internet of Things with 5G networks represents a transformative shift towards more efficient, responsive, and adaptable healthcare and service delivery systems to support independent living for older people at home. This article presents a unique proposal for the possibility of implementing interoperability and secure data transmission within the communication between autonomous mobile robots and building automation technology in a Smart Home using 5G networks and also presents a novel design and application of a time-ahead \(\textrm{CO}_2\,\)concentration prediction method for sending presence and occupancy information in monitored Smart Home Care spaces without the use of cameras to an autonomous mobile robot for time-ahead detection of deviations from the daily routine. In this study, nonlinear input-output neural network models and nonlinear autoregressive neural network model with exogenous inputs neural network models with the following best results (\(\textrm{MSE} = 3.322 \cdot 10^{-5}, \textrm{R} = 99.913\%\) and MAPE = 0.0565) were used. Levenberg-Marquardt algorithm, Bayes regularization algorithm and Scaled Conjugate Gradient algorithm have been used as learning algorithms. Measured waveforms of operational and technical variables for indoor environmental quality (temperature, relative humidity, light intensity and \(\textrm{CO}_2\,\)concentration) and binary information from magnetic contacts placed on windows and doors (opening/closing of windows and doors) were used to monitor the presence of occupants in the Smart Home Care with autonomous mobile robots without the use of cameras within IoT platform with 5G networks.