In the era of rapid technological growth, we are facing increased energy consumption. The question of using renewable energy sources is also essential for the sustainability of wireless sensor networks and the Industrial Internet of Things, especially in scenarios where there is a need to deploy an extensive number of sensor nodes and smart devices in industrial environments. Because of that, this paper targets the problem of monitoring the operations of solar-powered wireless sensor nodes applicable for a variety of Industrial IoT environments, considering their required locations in outdoor scenarios and the efficient solar power harvesting effects. This paper proposes a distributed wireless sensor network system architecture based on open-source hardware and open-source software technologies to achieve that. The proposed architecture is designed for acquiring solar radiation data and other ambient parameters (solar panel and ambient temperature, light intensity, etc.). These data are collected primarily to define estimation techniques using nonlinear regression for predicting solar panel voltage outputs that can be used to achieve energy-efficient operations of solar-powered sensor nodes in outdoor Industrial IoT systems. Additionally, data can be used to analyze and monitor the influence of multiple ambient data on the efficiency of solar panels and, thus, powering sensor nodes. The architecture proposal considers the variety of required data and the transmission and storage of harvested data for further processing. The proposed architecture is implemented in the small-scale variants for evaluation and testing. The platform is further evaluated with the prototype sensor node for collecting solar panel voltage generation data with open-source hardware and low-cost components for designing such data acquisition nodes. The sensor node is evaluated in different scenarios with solar and artificial light conditions for the feasibility of the proposed architecture and justification of its usage. As a result of this research, the platform and the method for implementing estimation techniques for sensor nodes in various sensor and IoT networks, which helps to achieve edge intelligence, is established.

The aim of the research conducted in this particular paper was related to a comprehensive reliability analysis of SKF 626 Open Deep Groove ball bearings, conducted under controlled laboratory conditions. The experimental research consisted of: measurements of radial internal clearance (RIC) in ball bearings, measurement methods for the detection of bearing operating temperature and rolling element bearing wear, complete with the cage deformation of the the damaged bearing, the failure of which was revealed after the examination. The inspected SKF 6206 open deep groove ball bearings operating under laboratory conditions – simulated the model of dynamic behavior under real conditions by using the crankshaft of an air compressor. The results of research conducted based on the inspected samples of rolling bearings, showed that there was a significant dependence of the predictors of internal radial clearance on operating bearing temperature. Out of five test samples of rolling bearings, there was only one rolling ball bearing detected to be with failure after the bearing inspection. The examination of the damaged bearing revealed the final radial clearance value of 0.045 mm, i.e. operating temperature of 75 °C.

The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R² of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R² of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

This study examines the influence of altering hydraulic parameters on the temperature of ISO HM VG 46 hydraulic mineral oil. Hydraulic mineral oils find extensive application in industrial power transmission systems, where precise temperature control is crucial for achieving optimal performance and prolonging system lifespan. Variations in hydraulic parameters, encompassing flow rate, pressure, and viscosity, can significantly impact the thermal characteristics of hydraulic oil.In this research, experimental studies were carried out to study the influence of changing hydraulic parameters on the temperature of hydraulic mineral oil. Experiments were performed on a hydraulic press, where different working conditions were simulated.The quality of hydraulic fluid is considered, according to all research, to be the most influential factor in ensuring the reliable operation of hydraulic systems.