Wireless IoT universal approach based on Allan variance method for detection of artificial vibration signatures of a DC motor's shaft and reconstruction of the reference signal

Abstract

In this paper, a workbench for remote control of DC motors is proposed. We intend to monitor and control rotating machines. Artificial failures are simulated by injecting different signals into the rotating motor's shaft. A comprehensive, original, and novel approach combining mathematical models, physical phenomena, and smart measurement methods is proposed to eliminate fluctuation faults caused by IoT sensor systems. These consist of a whole set of sensors (Accelerometer in Micro Electro Mechanical Systems [MEMS] technology, etc.) linked to a wireless node which constitutes a monitoring and control generic system of a DC motor running locally or via a remote control through an Internet platform. A reliability test of the IoT sensor system and an exploration of a proposed experimental bench in two measurement environments are successfully achieved. The results prove the reliability of devices for possible detection of acceleration signals corresponding to different speeds and injected vibrations with different forms and frequencies. A universal new approach, based on the Allan VARiance method, that has been proven robust when various disturbing signals are combined, has been successfully implemented to identify artificial vibration signatures. A reconstruction of MEMS sensor outputs using genetic algorithms is successfully fulfilled to get a compromise between the time of data acquisition and the time of processing via a learning phase of our system by means of a pseudo calibration of each motor applying our pseudo real-time control system. After the calibration phase, our control system aims to space out the measurements without degrading the effectiveness of the monitoring process.