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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.

Induction motors (IMs) are present in practically all production processes and account for two-thirds of the energy consumption in industrial settings. Therefore, monitoring them is essential to prevent accidents, optimize production, and increase energy efficiency. Monitoring methods found in the literature require a certain level of invasiveness, causing some applications to be unfeasible. In the present study, a new completely non-invasive method implemented in an embedded system performs the embedded processing of the sound signal emitted by an in-service IM to estimate speed, torque, and efficiency. Motor speed is estimated from the analysis in the frequency domain using the Fourier Transform. Torque and efficiency are estimated from the speed and motor nameplate information. To perform the tests and validate the proposed method/system, a workbench with a controllable torque was used. The workbench was also equipped to allow the results to be compared with the airgap torque method. The results indicate a high accuracy for the nominal load (error of approximately 1%) in the measurement of the efficiency and torque, and a mean relative error of 0.2% for the speed.

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