Three-Phase Induction Motors (TIMs) are widely applied in industries. Therefore, there is a need to reduce the operational and maintenance costs since their stoppages can impair production lines and lead to financial losses. Among all the TIM components, bearings are crucial in the machine operation once they couple the motor housing to the rotor. Also, they are constantly subjected to friction and mechanical wearing. Consequently, they represent around 41% of the motor fault, according to IEEE. In this context, several studies have sought to develop monitoring systems based on different types of sensors. Therefore, considering the high demand, this article aims to present the state of the art of the past five years concerning the sensing techniques based on current, vibration, and infra-red analysis, which are characterized as promising tools to perform bearing fault detection. The current and vibration analysis is a powerful tool to assess damages in the inner race, outer race, cages, and rolling elements of the bearings. These sensing techniques use current sensors like hall effect-based, Rogowski coils, and current transformers, or vibration sensors like accelerometers. The effectiveness of these techniques is due to the previously developed mathematical models, which relate the current and vibration frequencies to the origin of the fault. Therefore, this article also presents the mathematical models of these bearing failures. The infra-red technique is based on heat emission, and several image processing techniques were developed to optimize the assessment of bearings conditions using thermal images, which are presented in this review. Finally, this work is a contribution to expanding the frontiers of the bearing fault diagnosis area.