Article: Simulation studies of 3-phase induction motor using soft-computing techniques under multiple faults Journal: International Journal of Advanced Intelligence Paradigms (IJAIP) 2025 Vol.30 No.7 pp.1 - 23 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Simulation studies of 3-phase induction motor using soft-computing techniques under multiple faults

Authors: Puja Pohakar; Ravi V. Gandhi; Biswajeet Champaty; Gulshan Sharma; Pitshou N. Bokoro

Addresses: Ajeenkya D.Y. Patil University, Pune, Maharashtra, India ' G H Patel College of Engineering and Technology, CVM University, India ' E(QARAT) Andra Pradesh Med Tech Zone Ltd., Government of Andhra Pradesh, India ' Department of Electrical and Electronics Engineering Technology, University of Johannesburg, Johannesburg, 2006, P.O. Box 524, South Africa ' Department of Electrical and Electronics Engineering Technology, University of Johannesburg, Johannesburg, 2006, P.O. Box 524, South Africa

Abstract: 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.

Keywords: 3-phase induction motors; fault prediction; soft computing techniques; fuzzy inference system; FIS; artificial neural networks; ANN; adaptive neuro-fuzzy inference systems; ANFIS.

DOI: 10.1504/IJAIP.2025.148594

International Journal of Advanced Intelligence Paradigms, 2025 Vol.30 No.7, pp.1 - 23

Received: 26 Sep 2024
Accepted: 11 Dec 2024
Published online: 14 Sep 2025 *

Title: Simulation studies of 3-phase induction motor using soft-computing techniques under multiple faults

Keep up-to-date