In this study, we developed an algorithm for identifying failures caused by broken bars in the rotors of three-phase induction motors through the analysis of their dynamic deformation using fibre optic Bragg gratings (FBGs) as sensing elements. The analysis of dynamic deformation enables the detection and diagnosis of various mechanical faults, such as misalignment, imbalance, bearing failures, and mechanical looseness. Furthermore, it allows for the identification of electrical faults, such as fractures or cracks in the rotor rings or bars To measure the dynamic deformation, we employed FBG-based sensors known for their key features, including high multiplexing capability, electromagnetic radiation immunity, and long-distance operation. Experimental tests were conducted on a small-scale induction motor (3 HP) to validate the method and explore its applicability to medium and large-scale machines. The motor was powered by two different supply sources: the utility power grid and a controled power sources, under load conditions of 75% and 100% of the rated load. During the tests, we used a rotor without any bar defects and subsequently a rotor with a broken bar. The presence of a broken bar was successfully identified under both load conditions and across all two power supply sources. The fault caused by the broken bar in the rotor was detected in two frequency regions obtained from the three sets of experiments. The first region was centred around the mechanical rotational frequency of the rotor, while the second region was approximately twice the electrical frequency of the power supply. The system demonstrated high sensitivity with a good signal-to-noise ratio and showcased advantages over conventional methods and sensors commonly used for identifying broken bar faults in induction motors.

This study developed an algorithm running on the cloud that makes data process and diagnoses broken rotor bar faults in three-phase induction motors (TIMs), by analyzing stator dynamic deformation using fibre Bragg gratings (FBGs) as sensors. This method can diagnose mechanical faults (misalignment, imbalance) and electrical faults (fractures or cracks in rotor rings or bars). FBG-based sensors were used due to their high multiplexing capability, electromagnetic radiation immunity, and long-distance operation. Tests were conducted on a small-scale induction motor (3 HP) coupled to a generator to simulate load and the generator supplied by the grid. Were used 1 healthy rotor and one rotor with a broken bar fault running at two load conditions, 75% and 100%. The algorithm successfully identified broken bar faults in two frequency regions: around the mechanical rotational frequency of the rotor 28.06 Hz and 31.014 Hz operating at 75%; 25.325 Hz and 32.995 Hz operating at 100% and approximately twice the electrical frequency supply, 118.455 Hz and 123.942 Hz operating at 75%; 117.237 Hz and 124.683 Hz operating at 100%. The system showed high sensitivity, a good signal-to-noise ratio, and advantages over conventional methods for detecting broken bar faults in induction motors.