Diagnosis of three-phase induction motor and the impact on the kinematic chain using non-destructive technique of infrared thermography

Jeffali, F.; Ouariach, Abdelaziz; Kihel, Bachir El; Nougaoui, A.

doi:10.1016/j.infrared.2019.07.001

Cited by 34 publications

(18 citation statements)

References 13 publications

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“…The analyzed motor was mounted on a base plate with an alignment device. The presented results proved the usefulness of thermal-based fault diagnosis [ 15 ]. A convolutional neural network (CNN) and thermal images were used for fault diagnosis of a gearbox.…”

Section: Theoretical Backgroundsupporting

confidence: 55%

See 1 more Smart Citation

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz

2021

Sensors

148

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The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences—Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix V or PCA of matrix V or histogram of matrix V. Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes (TRAG) was in the range of 98.5–100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.

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Section: Theoretical Backgroundsupporting

confidence: 55%

“…Thermal imaging can detect many electrical faults, namely: broken bars, shorted coils, insulation faults, fan faults, overvoltages, ventilation faults. Analysis of thermal images can detect the type and location of fault [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz

2021

Sensors

148

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“…As shown in Figure 16, some researchers use the short-time Fourier transform (STFT), the Hilbert-Huang transform (HHT) [97], the continuous wavelet transform (CWT) [98], the synchrosqueezing transform (SST) [99], and other methods to convert 1D vibration signals into 2D time-frequency images as the input of CNN. Some other researchers used gray image [100], wavelet packet energy diagram [101], infrared thermal image [102][103][104], root mean square diagram [105], feature statistics diagram [106], and other images as the input of CNN for fault diagnosis of rotating machinery. Wang et al used the short-time Fourier transform (STFT) to obtain the time-frequency map of the vibration signal and then adaptively extracted the timefrequency map features through CNN [107].…”

Section: Application Of Cnn In Fault Diagnosismentioning

confidence: 99%

“…[102] Ref. [103] Ref. fault diagnosis of planetary gearbox under severe noise environment is carried out [127,128].…”

Section: Application Of Cnn In Fault Diagnosismentioning

confidence: 99%

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Cui¹,

Meng²,

Wang³

et al. 2021

Shock and Vibration

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With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

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“…Infrared thermal imaging is a noncontact and nonintrusive measurement technique that can detect all the monitored system component temperature variation. This technique has been largely used in nondestructive examination, 6 medical science, 7 defense, 8 and automotive. 9 In recent years, it has been known that rich information is contained in IRT images and can be used as diagnosis data for several electrical machines such as bearing diagnosis of rotating machinery, 10 Grinder 11 diagnosis, and fault diagnosis of electric impact drills.…”

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

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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.

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Diagnosis of three-phase induction motor and the impact on the kinematic chain using non-destructive technique of infrared thermography

Cited by 34 publications

References 13 publications

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

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

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