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Three-phase induction motors (TIMs) are present in most industrial processes, accounting for more than 60% of the energy consumption in industry. Despite their importance in the productive sector, few motors are properly monitored, mainly due to the high cost of the monitoring equipment and the invasiveness in their installation. This paper presents the implementation and deployment of an industrial wireless sensor network (WSN) to monitor three-phase induction motors. Embedded systems were developed to acquire signals of current and voltage from sensors installed in the motors' terminals, perform local processing to estimate torque and efficiency, and transmit the information through the WSN. The method used to estimate the variables is based on the air-gap torque method. Before the deployment in the industry, experiments were performed to validate the system in laboratory. Finally, the system was employed in a real industrial environment, where different analyses and diagnosis of three motors running were performed. Using the proposed system, the efficiency versus load curves of the motors could be obtained continuously, and an energy loss analysis due to the oversizing of the motors was performed.

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This paper describes a method for increasing the accuracy and precision of temperature measurements of a liquid based on the central limit theorem. A thermometer immersed in a liquid exhibits a response with determined accuracy and precision. This measurement is integrated with an instrumentation and control system that imposes the behavioral conditions of the central limit theorem (CLT). The oversampling method exhibited an increasing measurement resolution. Through periodic sampling of large groups, an increase in the accuracy and formula of the increase in precision is developed. A measurement group sequencing algorithm and experimental system were developed to obtain the results of this system. Hundreds of thousands of experimental results are obtained and seem to demonstrate the proposed idea’s validity.

This paper presents a theoretical study for verifying the impact of using smart nodes in motor monitoring systems in industrial environments employing Wireless Sensor Networks (WSNs). Structured cabling and sensor deployment are usually more expensive than the cost of the sensors themselves. Besides the high cost, the wired approach offers little flexibility, making the network deployment and maintenance a complex process. In this context, wireless networks present a number of advantages compared to wired networks as, for example, the ease and speed of deployment and maintenance and the associated low cost. However, WSNs have several limitations, such as the low bandwidth and unreliability, especially in harsh environments (e.g., industrial plants). This paper presents a theoretical study on the performance of WSNs for motor monitoring applications in industrial environments, taking into account WSNs' characteristics (i.e., unreliability and communication and processing latency). The results obtained through mathematical models were analyzed together with experimental results, and it was demonstrated that employing intelligent nodes with local processing capabilities is essential for the applications under consideration, because it reduces the amount of data transmitted over the network allowing monitoring even in scenarios with high interference rate, paying off the extra latency resulting from local processing.

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This work presents a new method, and its instrument, to measure the thermal conductivity of materials. It employs two different materials, so it is a differential instrument. In this proposed method a thermal wave is generated on one of the surfaces of a given sample, and the temperature is measured at the other surface. From the relationship between the input and output waves is possible to obtain the thermal conductivity of one material. The equation of heat conduction, which describes the behavior of the system, on the materials was deduced and simulated, allowing to determine the temperature at any point of the materials. Along with the equations, the Network Simulation Method was applied to solve the problem, but with a different approach. The experimental setup uses thermoecletric coolers to control temperature, and T thermocouples to measure the temperature. The materials used to apply the method were both thermal insulating and conductive, and with few millimeters thick. Several experiments were performed in order to validate the method and they indicate the feasibility of the instrument.Nomenclature: α -Thermal diffusivity (m 2 /s) κ -Thermal conductivity (W/(mK)) ρ -Density (kg/m 3 ) C p -Specific heat capacity (J/(kgK)) h -Heat Transfer Coefficient H -Sample thickness (mm) L -Total thickness (mm) β -Material parameter ( iω α ) ω -Angular frequency (rad/s)) κ -thermal conductivity (W/(mK)) R -Both sample and reference radius (mm)