The reliability and efficiency of 3-phase induction motors are crucial for various industrial applications. However, the timely fault detection and prediction in these motors remains a significant challenge. Traditional fault prediction methods cannot often effectively handle complex fault patterns and dynamic operating conditions. Due to this, researchers have focused towards soft computing techniques for enhanced fault prediction accuracy. This paper reviews the principles and methodologies of traditional fault prediction methods, highlighting their limitations in accurately predicting faults in 3-phase induction motors. This research paper explores the transition from conventional fault methods to soft computing techniques such as fuzzy inference systems, artificial neural networks and adaptive neuro-fuzzy inference systems. These techniques enable adaptive learning, helping the system to recognise and adapt to complex fault patterns and varying operating conditions. The results show the detailed analysis of eight types of crucial faults using various computing techniques under different parametric conditions.
