A method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Senthil Kumar, R.; Gerald Christopher Raj, I.; Suresh, K. P.; Leninpugalhanthi, P.; Suresh, M.; Panchal, Hitesh; Meenakumari, R.; Sadasivuni, Kishor Kumar
• Is Part Of: International journal of ambient energy. Volume 43:Issue 1(2022)
• Page Start: 5138
• Page End: 5144
• Publication Date: 2022-12-31
• Subjects: Artificial Neural Networks -- Broken Rotor Bars -- Direct Torque Control -- Fast Fourier Transform; Power resources -- Periodicals; Renewable energy sources -- Periodicals; 621.04205
• DOI: 10.1080/01430750.2021.1934117
• Languages: English
• ISSNs: 0143-0750

Abstract

Abstract : This paper presents a high accuracy detection of Broken Rotor Bar (BRB) fault by Artificial Neural Network (ANN) through advanced signal processing tool as Hilbert Transform (HT) where three phase Induction Motor Drives (IMD) is operated under Direct Torque Control (DTC) topology with steady state. The major significance of all diagnostic methods is, need information about the characteristic's frequencies and amplitude. The diagnosing of machine fault requires the spectrum into isolated various frequency components. The Discrete Fourier Transform (DFT) cannot produce good output at low slip. So, in this paper ANN and HT are proposed. DTC method is efficient technique in industrial drives with variable torque applications. The stator current envelope can be formed by HT. Then samples of amplitude and side band frequency are given as ANN inputs. In order to diagnose the quantity of BRB in IM, the findings are qualified and checked to the minimal Mean Square Error (MSE)