Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

The problem of the reliability of measurements of rotational parameters of electric motors was solved, which was focused on the development of an algorithm for evaluating measurements under conditions of additional noise. An analysis of methodological approaches and mathematical tools used to process and interpret the uncertainty of measurement results was carried out. Cases where they may not be effective due to high noise levels were considered. To detect anomalies in the signal, an algorithm for assessing the reliability of measurements using fuzzy logic was proposed. A structural diagram of the model for measuring the torque of an electric motor under the conditions of a noisy signal was developed, where transfer functions were used to model the angular velocity and torque parameters. A method for detecting anomalies in noisy signals is presented, which identifies the amplitude and time characteristics of spiking pulses. The method includes the application of a wide range of analytical tools for deep analysis of signals and is particularly effective for detecting anomalies that may be hidden in background noise. A prototype of a measuring bench was developed, which uses neural networks to detect anomalies when measuring the rotational parameters of electric motors, which made it possible to obtain a training sample using a sample electric motor and apply it to evaluate the parameters of another electric motor. In a practical aspect, the developed methods and technological solutions for improving the reliability of measurements of rotational parameters of electric motors could be used to make corrections in existing systems. In particular, they could be used in industry, electric transport, as well as in the aerospace and military sectors where the reliability of measuring systems is important