RETRACTED ARTICLE: Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system

Devarajan, G.; Muniraj, C.; Kaliappan, Lakshmi

doi:10.1007/s12652-020-01857-8

Cited by 11 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…All objects emit thermal radiation, which can be measured and analyzed. This study develops a new thermographic fault diagnostic method and was motivated by other research in this area [13][14][15][16][17][18][19][20][21]. Thermographic fault diagnosis of BLDC motors can also be used for other applications (other types of motors, combustion engines).…”

Section: Discussionmentioning

confidence: 99%

“…Fault diagnosis based on thermal imaging is non-contact and non-invasive. It is a highly effective method of fault diagnosis [13][14][15][16][17][18][19][20][21]. However, the high cost of thermal imaging cameras is a limitation for fault diagnosis applications.…”

Section: Related Workmentioning

confidence: 99%

“…Thermal-based fault diagnosis of an induction motor been described in which an adaptive neuro-fuzzy inference system was used for faults such as air gap eccentricity, cooling fan failure, and shaft bending. The accuracy of the classification of thermal images was equal to 91.27% [15]. A fault diagnosis technique for an induction motor (working in transient conditions) based on thermal images and a convolutional neural network has been described.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Głowacz

2021

Sensors

View full text Add to dashboard Cite

Thermographic fault diagnosis of ventilation in BLDC (brushless DC) motors is described. The following states of BLDC motors were analyzed: a healthy BLDC motor running at 1450 rpm, a healthy BLDC motor at 2100 rpm, blocked ventilation of the BLDC motor at 1450 rpm, blocked ventilation of the BLDC motor at 2100 rpm, healthy clipper, and blocked ventilation of the clipper. A feature extraction method called the Common Part of Arithmetic Mean of Thermographic Images (CPoAMoTI) was proposed. Test thermal images were analyzed successfully. The developed method, CPoAMoTI is useful for industry and society. Electric cars, trains, fans, clippers, computers, cordless power tools can be diagnosed using the developed method.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Głowacz

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Mechaninės vibracijos ir įtempiai gali sukelti fizinį izoliacijos nusidėvėjimą. Besilankstantys ir besislenkantys izoliuojančios medžiagos poslinkiai sukelia įtrūkimų ir tuštumų, dėl kurių ilgainiui mažėja dielektrinės medžiagos elektrinis atsparumas (Devarajan, Chinnusam and Kaliappan, 2020).…”

Section: Rezultatų Aptarimasunclassified

Besisukančių elektros mašinų izoliacijos varžos matavimo metodų tyrimas

Dikun,

Tamelienė

2024

DAV

View full text Add to dashboard Cite

show abstract

“…After that, fault pattern is identified by feeding the obtained features into the Softmax Regression (SR) classifier. Devarajan et al 15 propose a fault diagnosis method for induction machines, at first, by using the temperature pixels indicator of the thermal image, they addressed three types of faults that are known to provoke an increase in the stator temperature such as shaft misalignment, air gap eccentricity, and cooling system failure, for which different degree of temperature variation are directly related to pixels values of the IRT image. Then, the extracted features were classified using ANFIS structure model.…”

Section: Introductionmentioning

confidence: 99%

Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

Mahami

Rahmoune

Bettahar

et al. 2021

Advances in Mechanical Engineering

View full text Add to dashboard Cite

In this paper, a novel noncontact and nonintrusive framework experimental method is used for the monitoring and the diagnosis of a three phase’s induction motor faults based on an infrared thermography technique (IRT). The basic structure of this work begins with this applying IRT to obtain a thermograph of the considered machine. Then, bag-of-visual-word (BoVW) is used to extract the fault features with Speeded-Up Robust Features (SURF) detector and descriptor from the IRT images. Finally, various faults patterns in the induction motor are automatically identified using an ensemble learning called Extremely Randomized Tree (ERT). The proposed method effectiveness is evaluated based on the experimental IRT images, and the diagnosis results show its capacity and that it can be considered as a powerful diagnostic tool with a high classification accuracy and stability compared to other previously used methods.

show abstract

RETRACTED ARTICLE: Detection and classification of mechanical faults of three phase induction motor via pixels analysis of thermal image and adaptive neuro-fuzzy inference system

Cited by 11 publications

References 23 publications

Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Besisukančių elektros mašinų izoliacijos varžos matavimo metodų tyrimas

Induction motor condition monitoring using infrared thermography imaging and ensemble learning techniques

Contact Info

Product

Resources

About