SmarTEG: An autonomous wireless sensor node for high accuracy accelerometer-based monitoring

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually

Advances of Italian Machine Design

This 2028 Special Issue presents recent developments and achievements in the field of Mechanism and Machine Science coming from the Italian community with international collaborations and ranging from theoretical contributions to experimental and practical applications. It contains selected contributions that were accepted for presentation at the Second International Conference of IFToMM Italy, IFIT2018, that has been held in Cassino on 29 and 30 November 2018. This IFIT conference is the second event of a series that was established in 2016 by IFToMM Italy in Vicenza. IFIT was established to bring together researchers, industry professionals and students, from the Italian and the international community in an intimate, collegial and stimulating environment

Cumulative index to NASA Tech Briefs, 1970-1975

Tech briefs of technology derived from the research and development activities of the National Aeronautics and Space Administration are presented. Abstracts and indexes of subject, personal author, originating center, and tech brief number for the 1970-1975 tech briefs are presented

Low-power wireless accelerometer-based system for wear detection of bandsaw blades

The paper provides a framework to save energy and reduce the operative cost of some of today's industrial machinery. Low cost and low power wireless sensor networks is a novel approach to monitoring the tools in order to save energy and keep the tools monitored. Cutting tool wear degrades the product quality in manufacturing processes and also could have implications in health and safety of use. Monitoring tool wear value online is therefore needed to prevent degradation in machine quality. Unfortunately there is no direct way of measuring the tool wear online which is also very low cost. In this work is presented a low power and low cost accelerometer-based system for wear detection of bandsaw blade. The algorithm uses a simple data processing directly on board that can extract features and perform a classification on the state of the blade. Low power design of the node, on board processing and wake up radio capabilities reduce the wireless communication and the power consumption of the node significantly. Experimental results show the high accuracy, up to 100%, of the algorithm and the low power of the proposed approach

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Reinforcement Learning Approach for Autonomous UAV Navigation in 3D Space

In the last two decades, the rapid development of unmanned aerial vehicles (UAVs) resulted in their usage for a wide range of applications. Miniaturization and cost reduction of electrical components have led to their commercialization, and today they can be utilized for various tasks in an unknown environment. Finding the optimal path based on the start and target pose information is one of the most complex demands for any intelligent UAV system. As this problem requires a high level of adaptability and learning capability of the UAV, the framework based on reinforcement learning is proposed for the localization and navigation tasks. In this paper, Q-learning algorithm for the autonomous navigation of the UAV in 3D space is implemented. To test the proposed methodology for UAV intelligent control, the simulation is conducted in ROS-Gazebo environment. The obtained simulation results have shown that the UAV can reach the target pose autonomously in an efficient way

Low-power wireless accelerometer-based system for wear detection of bandsaw blades

The paper provides a framework to save energy and reduce the operative cost of some of today's industrial machinery. Low cost and low power wireless sensor networks is a novel approach to monitoring the tools in order to save energy and keep the tools monitored. Cutting tool wear degrades the product quality in manufacturing processes and also could have implications in health and safety of use. Monitoring tool wear value online is therefore needed to prevent degradation in machine quality. Unfortunately there is no direct way of measuring the tool wear online which is also very low cost. In this work is presented a low power and low cost accelerometer-based system for wear detection of bandsaw blade. The algorithm uses a simple data processing directly on board that can extract features and perform a classification on the state of the blade. Low power design of the node, on board processing and wake up radio capabilities reduce the wireless communication and the power consumption of the node significantly. Experimental results show the high accuracy, up to 100%, of the algorithm and the low power of the proposed approach