FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods

Abstract

[EN] Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. Trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals with non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and K-nearest-neighbor classi-ficators. The development of the diagnostic system was done with digital hardware implemen-tations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The pro-posed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.