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.

This paper shows the preliminary results of a proposed methodology for outer race fault diagnosis of contaminated rolling bearings. The proposed method is based on a low-cost thermographic sensor. This sensor allows for the acquisition of thermographic images, which are subsequently processed by means of statistical and non-statistical indicators. The obtained results shows a correct fault classification of 4 condition states as follows: healthy rolling bearing, outer race fault + 1 g of magnetite-contaminated bearing, outer race fault + 2 g of magnetite-contaminated bearing, and outer race fault + 3 g of magnetite-contaminated bearing

Induction motors play a fundamental role in most industrial processes due to their high efficiency and robust performance. For this reason the condition monitoring of these equipment is of great relevance. One of the most studied motor components is the bearing. Bearings reduce friction in the rotor allowing it to move freely, being a key factor in the correct operation of the induction motor. The most common bearing failures occur in the outer race. In this work we present the development of a system based on thermographic image processing to identify gradual failures in the outer bearing race of an induction motor. The bearing failure cases were induced by drilling holes in the outer race of metallic bearings with ascending diameters of 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm. The experiments were carried out in a kinematic chain integrated by an induction motor and load elements. Thermographic images were acquired with a low-cost infrared sensor, and then segmented into three zones: motor rotor, motor body, motor backside. Subsequently, fifteen statistical parameters were calculated and processed by principal component analysis. The system obtained two characteristic features capable of effectively differentiating the bearing failures.

Kinematic chains have a fundamental role in the modern industry thanks to the great variety of applications where they can be found. For this reason the development of new fault detection methods has gained traction in recent years. Different physical signals have been used for the diagnosis of kinematic chains such as electric current, mechanical vibrations, or stray flux; nonetheless, the analysis of temperature signals measured by thermographic cameras has proven to be an effective way to detect certain types of failures complementing the existing work on this area. This paper presents the development of an infrared image processing system for the detection of electromechanical faults (misalignment, unbalance, broken bars, bearing defects, and gearbox wear) on a kinematic chain composed by an induction motor, an output pulley, a plastic transmission band, and an alternator. The system is based on the automatic segmentation of a region of interest associated to the kinematic chain through the implementation of image processing techniques and the calculation of statistical characteristics from the histogram of a thermal image acquired by a low-cost thermographic camera.