scholarly journals Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

2014 ◽  
Vol 19 (4) ◽  
pp. 55-62 ◽  
Author(s):  
Won-Chul Jang ◽  
Jong-Myon Kim
Keyword(s):  
Acoustic Emission ◽  
Induction Motor ◽  
Texture Analysis

Hybrid System Using Acoustic Emission and Electric Current for Fuzzy Diagnosis of Surface Wear in Gearbox Coupled to Induction Motor

2017 ◽  
Author(s):  
Leonardo José Cavalcante Vasconcelos ◽  
ARLESON KENNEDI FRANÇA DOS SANTOS ◽  
Dalton Valadares ◽  
Alexander Sena
Keyword(s):  
Acoustic Emission ◽  
Electric Current ◽  
Induction Motor ◽  
Hybrid System ◽  
Surface Wear ◽  
Fuzzy Diagnosis

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199691
Author(s):  
Omar AlShorman ◽  
Fahad Alkahatni ◽  
Mahmoud Masadeh ◽  
Muhammad Irfan ◽  
Adam Glowacz ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Vital Role ◽  
Remaining Useful Life ◽  
Useful Life ◽  
Review Study ◽  
Public Datasets ◽  
Fatigue Detection ◽  
Types Of Faults

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

scholarly journals Mechanical Fault Classification of an Induction Motor using Texture Analysis

2013 ◽  
Vol 18 (12) ◽  
pp. 11-19 ◽  
Author(s):  
Won-Chul Jang ◽  
Yong-Hoon Park ◽  
Myeong-Su Kang ◽  
Jong-Myon Kim
Keyword(s):  
Induction Motor ◽  
Texture Analysis ◽  
Fault Classification

scholarly journals Undervoltage Identification in Three Phase Induction Motor Using Low-Cost Piezoelectric Sensors and STFT Technique

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 72
Author(s):  
Leonardo Carvalho ◽  
Guilherme Lucas ◽  
Marco Rocha ◽  
Claudio Fraga ◽  
Andre Andreoli
Keyword(s):  
Acoustic Emission ◽  
Induction Motor ◽  
Large Scale ◽  
New Technologies ◽  
Low Cost ◽  
Industrial Applications ◽  
Piezoelectric Sensors ◽  
Time Frequency ◽  
Three Phase ◽  
Ae Signals

Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

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