Hardware Accelerated Thermal Image Processing for the Detection of Induction Motor Faults Based on Statistical Features

Condition monitoring and predictive maintenance of induction motors have great relevance in industrial applications. Nowadays, there are different techniques to analyze electronic signals from different types of sensors. At the same time, the application of FPGA-based hardware acceleration has gained traction in recent years due to the increasing demand for time and energy optimization. However, the application of hardware-accelerated algorithms in condition monitoring applications has yet to be explored despite its potential to improve online fault detection systems. This paper presents the implementation of an FPGA-based hardware-accelerated thermal image processing pipeline on the PYNQ Z2 board based on two image statistical features (mean and standard deviation) to detect five different induction motor mechanical fault conditions: misalignment, unbalanced load, bearing defect on the outer race, two broken rotor bars, and a healthy case that was used as reference. The hardware implementation of the thermal image statistical feature computation made it possible to reduce the computational load and the computation time on the development board.