Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor. (2018)

• Record Type: Journal Article
• Authors: Rajeswaran, N.; Lakshmi Swarupa, M.; Sanjeeva Rao, T.; Chetaswi, K.
• Is Part Of: Materials today. Volume 5:Number 1(2018)Part 1
• Page Start: 565
• Page End: 571
• Publication Date: 2018
• Subjects: Voltage Source Inverter -- Fault Diagnosis -- Hybrid Artificial Intelligence -- Back Propagation Neural Networks -- Genetic Algorithm; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2017.11.119
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Induction motors are widely used in various industries for production processes due to their inherent characteristics like high reliability, robustness, low cost and ease of construction and operation. However, while in operation they face harsh and severe conditions imposed on them by the electrical, mechanical, thermal and environmental stresses which give rise to abnormalities in machine parameters such as voltages and currents. If the motor drive is allowed to continue to run under such situations, it will lead to catastrophic failure of the motor disrupting the production process and huge loss of revenue. Hence, an intelligent drive control method based on Hybrid Artificial Intelligence (AI) technique of Neuro-Genetic Algorithm is proposed in this paper for condition monitoring, fault diagnosis and evaluation of induction motor without any additional information. The hybrid architecture of Back Propagation Neural Network (BPN) to get the desired output fast and with Genetic Algorithm to overcome the computational complexity and time consuming process involved in BPN.