Electrical fault detection in three-phase induction motor using deep network-based features of thermograms

Khanjani, Majid; Ezoji, Mehdi

doi:10.1016/j.measurement.2020.108622

Cited by 59 publications

(25 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

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%

“…A convolutional neural network, K-means clustering, and support vector machine were used for three-phase induction motors. The accuracy was equal to 100% for real thermal images of the three-phase induction motor [14]. 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.…”

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

“…All four described errors are applied on the measured data, and the accuracy of the designed scheme is examined. To see the superiority of designed algorithm better, it is compared with a recurrent fuzzy neural network (RFNN) [36], SVM-NN [37] and neurofuzzy (NFLS) [38]. The accuracy of different methods has been shown in Table 1.…”

Section: Fault Detectionmentioning

confidence: 99%

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Wang

Tavoosi

Mohammadzadeh

et al. 2021

Sensors

View full text Add to dashboard Cite

The main contribution of this paper is to develop a new flowmeter fault detection approach based on optimized non-singleton type-3 (NT3) fuzzy logic systems (FLSs). The introduced method is implemented on an experimental gas industry plant. The system is modeled by NT3FLSs, and the faults are detected by comparison of measured end estimated signals. In this scheme, the detecting performance depends on the estimation and modeling performance. The suggested NT3FLS is used because of the existence of a high level of measurement errors and uncertainties in this problem. The designed NT3FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships and adaptive non-singleton fuzzification results in a powerful tool for modeling signals immersed in noise and error. The level of non-singleton fuzzification and membership parameters are tuned by maximum correntropy (MC) unscented Kalman filter (KF), and the rule parameters are learned by correntropy KF (CKF) with fuzzy kernel size. The suggested learning algorithms can handle the non-Gaussian noises that are common in industrial applications. The various types of flowmeters are investigated, and the effect of common faults are examined. It is shown that the suggested approach can detect the various faults with good accuracy in comparison with conventional approaches.

show abstract

“…IRT has been successfully used for fault diagnosis or condition monitoring of many kinds of electrical equipment, such as transformers, motors, circuit breakers, lightning arresters, measurement and control cabinets, power cables, and insulators [3][4][5][6][7]. However, a Correspondence to: Jianhua Ou.…”

Section: Introductionmentioning

confidence: 99%

Online Monitoring of Electrical Equipment Condition Based on Infrared Image Temperature Data Visualization

Wang

Fan

et al. 2021

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Temperature is one of the most common indicators for the health of electrical equipment in the substation. By now, infrared thermography has been an important monitoring tool due to its real-time temperature measurement and noncontact manner. However, at present, portable thermal imagers are usually used to collect and store the status image of single equipment at a certain time, which cannot continuously track the condition of the equipment and the efficiency is very low. In order to solve this problem, our team proposed a method for online condition of electrical equipment based on the visualization and processing of infrared image temperature data. We developed video inspection software, equipped with an infrared camera for automatic inspection to obtain infrared images. After that, the target equipment in a whole image are automatically extracted based on the reference image and the equipment type of each target is classified. Then, in order to visualize temperature data and extract temperature information for different types of equipment, the concepts of heating spot, heating section, and heating area are defined. Finally, diagnosis rules based on relevant related thermal fault diagnosis standards and past experience are proposed to evaluate the condition of each target equipment, using the temperature rise and heat generation percentage. The method has been applied in a real substation and proved its effectiveness.

show abstract

Electrical fault detection in three-phase induction motor using deep network-based features of thermograms

Cited by 59 publications

References 17 publications

Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Thermographic Fault Diagnosis of Ventilation in BLDC Motors

Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry

Online Monitoring of Electrical Equipment Condition Based on Infrared Image Temperature Data Visualization

Contact Info

Product

Resources

About