FPGA Hardware in the Loop Validation of Fuzzy MCSA current analysis-based fault diagnostic of induction motor

Currently, the analysis of the stator current signal of induction motors (known as MCSA) has gained popularity as a method to evaluate the operational condition of asynchronous motors and prevent potential breakdowns. Traditional approaches for detecting faults in rotating machinery, relying on micro-programmed sequential systems like microprocessors and DSPs, have demonstrated its limits in terms of real-time requirements and processing speed. To address these challenges, it has become essential to embrace cutting-edge technologies, such as ASICs or FPGAs, which offer more efficient diagnostic capabilities. The innovation in our research lies in the development of a fault detection algorithm implemented on FPGA hardware architecture for a induction machines. This algorithm leverages fuzzy logic and analyzes the stator current signal (MCSA), specifically by considering the "Root Mean Square" (RMS) of the stator current as an indicator of potential faults. To validate this architecture, we conducted hardware in the loop validation procedure using Xilinx Virtex-4 ML402 FPGA board and XSG within the Matlab/Simulink environment. Our hardware architecture has significantly enhanced the fault detection performance in induction machines, notably in terms of online real-time detection, detection speed, and resource utilization.