Testing and analysis fault of induction motor for case study misalignment installation using current signal with energy coefficient

7Citations
Citations of this article
17Readers
Mendeley users who have this article in their library.

Abstract

An induction motor is a key device for an industrial machine. The installation misalignment of the motor will result in derating problems and energy consumption that is generally used to analyze signal faults using the fast Fourier transform (FFT) method. Problems with the rotor affect the non-stationary signal and FFT can be utilized to analyze this problem inefficiently. This paper proposed the testing and analysis of faults in an eccentric rotor at different levels using the stator current detection technique and the calculation of the energy signal coefficient via the wavelet decomposition (WD) method. The experimental results showed that an increase in eccentricity had a linear relation with the energy signal, where R2 was 80.81%. Moreover, the test results illustrated that the proposed method was more efficient than FFT and applicable to motor fault analysis and application in the industrial.

References Powered by Scopus

Fault detection in induction machines using power spectral density in wavelet decomposition

488Citations
N/AReaders
Get full text

Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages

362Citations
N/AReaders
Get full text

Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation

212Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Development of Intelligent Fault Diagnosis Technique of Rotary Machine Element Bearing: A Machine Learning Approach

28Citations
N/AReaders
Get full text

Determining An Additional Diagnostic Parameter For Improving The Accuracy Of Assessment Of The Condition Of Stator Windings In An Induction Motor

17Citations
N/AReaders
Get full text

Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions

8Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Prainetr, S., Tunyasrirut, S., & Wangnipparnto, S. (2021). Testing and analysis fault of induction motor for case study misalignment installation using current signal with energy coefficient. World Electric Vehicle Journal, 12(1). https://doi.org/10.3390/wevj12010037

Readers' Seniority

Tooltip

Lecturer / Post doc 4

100%

Readers' Discipline

Tooltip

Engineering 4

80%

Computer Science 1

20%

Save time finding and organizing research with Mendeley

Sign up for free