Automatic Mechanical Fault Diagnosis in Induction Motors Through Thermography and Gradient Boosting

Induction motors are electric machines widely used in mechanisms requiring rotary motion. Despite being robust machines, they are prone to failure. Among the most common and challenging failures to differentiate are those caused by mechanical issues such as imbalance and misalignment. Although various methodologies for diagnosing mechanical faults in these motors exist, most employ computationally intensive techniques or require costly equipment. This study proposes a methodology for automatically diagnosing imbalance and misalignment faults using a low-cost infrared sensor. This involves processing the sensor data by calculating two statistical indicators (mean and kurtosis) from regions of interest and employing a classifier based on the GradientBoost algorithm. The methodology is validated on an induction motor with variable load using a DC motor.