Three-phase induction motor fault detection based on thermal image segmentation

Al-Musawi, Ammar K.; Anayi, Fatih; Packianather, Michael

doi:10.1016/j.infrared.2019.103140

Cited by 68 publications

(30 citation statements)

References 22 publications

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“…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%

“…Next Skewness, Standard Deviation, Kurtosis, Mean, Mean Square Error, Variance, Peak Signal to Noise Ratio were computed. The results indicated that the proposed image segmentation methods and metrics were useful for image recognition [ 17 ]. The analysis of the specific region of thermal images of the induction motor was developed.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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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 Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Głowacz

2021

Sensors

148

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show abstract

“…After converting the image to grayscale, the image segmentation is applied. Segmentation is performed to partition the image in regions that are based on their characteristics of the image pixels [34].…”

Section: Image Pre-processingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Corso¹,

Perez²,

Stefenon³

et al. 2021

Preprint

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The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

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“…Some techniques are based on feature extraction whereas others deal with the classification of the processed data. Additionally, various forms of signals are also used for fault diagnosis [5], including magnetic, acoustic [6], electric [7], and thermal signals [8]. The objective of model-based methods is to generate effects identical to the ones observed in daily life by using similar loading at the fault location [9].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Stator Winding Inter-Turn Fault in Interior-Mount LSPMSM Using Acoustic Signals

et al. 2020

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This paper presents a novel stator inter-turn fault diagnosis method for Line Start Permanent Magnet Synchronous Motors (LSPMSMs) using the frequency analysis of acoustic signals resulting from asymmetrical faults. In this method, acoustic data are experimentally collected from a 1 hp interior mount LSPMSM for different inter-turn fault cases and motor loading levels, while including the background noise. The signals are collected using a smartphone at a sampling rate of 48,000 samples per second. The signal for each case is analyzed using fast Fourier transform (FFT), which results in the decomposition of the signal into its frequency components. The results indicate that, for both no-load and full-load conditions, 39 components are observed to be affected by the faults, whereby their amplitudes increase with the fault severity. The 40-turns fault shows the highest difference in the component amplitudes compared with the healthy condition acoustic signal. Therefore, this diagnostic method is able to detect the stator inter-turn fault for interior mount LSPMSMs. Moreover, the method is simple and cheap since it uses a readily available sensor.

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Three-phase induction motor fault detection based on thermal image segmentation

Cited by 68 publications

References 22 publications

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Recognition of Stator Winding Inter-Turn Fault in Interior-Mount LSPMSM Using Acoustic Signals

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