HV Power Equipment Diagnosis Based on Infrared Imaging Analyzing

Li, Baoshu; Zhu, Xijing; Zhao, Shutao; Niu, Wendong

doi:10.1109/icpst.2006.321512

Cited by 28 publications

(17 citation statements)

References 2 publications

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“…The simplest method of identifying hot spot regions within a thermal image of electrical equipment is to use thresholding techniques where the hot spot area is detected by filtering the image using a certain threshold value [4][5][6][7]. The hot region can be extracted using morphological segmentation where the maximum gray pixel value determines the maximum temperature of the hot region.…”

Section: Advanced Methods For Condition Monitoring Of Electrical Ementioning

confidence: 99%

Thermal condition monitoring of electrical installations based on infrared image analysis

Jadin

Ghazali

Taib

2013

2013 Saudi International Electronics, Communications and Photonics Conference

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Infrared imaging is a commonly used tool for monitoring and assessing the thermal condition of electrical installations for ensuring a reliable power supply. However, the conventional evaluation approach is quite time consuming as the image is analyzed manually by a qualified personnel. Therefore, this paper proposed a fast thermal anomaly detection and classification based on qualitative infrared image analysis. First, regions of interest (ROIs) are semi-automatically selected by employing normalized cross correlation (NCC) for finding similar objects in the image. Statistical features are extracted from each detected region and classified using multilayer perceptron (MLP) neural network for determining the thermal condition of electrical equipment. The overall accuracy obtained by the proposed method is approximately 95 %, which is highly encouraging.

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Section: Advanced Methods For Condition Monitoring Of Electrical Ementioning

confidence: 99%

Thermal condition monitoring of electrical installations based on infrared image analysis

Jadin

Ghazali

Taib

2013

2013 Saudi International Electronics, Communications and Photonics Conference

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“…It has been extensively employed in the area of electric installations maintenance [8] and predictive maintenance of static electrical machines [9]. The few papers in the induction motors condition monitoring field, have focused on the diagnosis of faults such as: insulation failures in the magnetic circuit, and deficient connections or misalignments [10]- [14]. However, most of the works aim to detect simple failures, often external to the machine.…”

Section: Introductionmentioning

confidence: 99%

An automated thermographic image segmentation method for induction motor fault diagnosis

Karvelis

Georgoulas

Stylios

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

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“…[7][8][9][10] For diagnosis of thermal defects, regions of interest (ROIs) are selected by feature descriptions. Various intelligent techniques, such as neural network (NN), 11,12 support vector machine (SVM), 13,14 and neurofuzzy algorithm, 15 have been used for the classification. In literature, the simplest approach to distinguish hot/cold spot regions in the thermal image of a building is to use statistical methods and morphological image processing technique in conjunction with quantitative analyses on the inspection results.…”

Section: Review On Approaches For Hollowness Assessmentmentioning

confidence: 99%

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Liu

Yan

Maruvanchery

et al. 2017

International Journal of Advanced Robotic Systems

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We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.

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HV Power Equipment Diagnosis Based on Infrared Imaging Analyzing

Cited by 28 publications

References 2 publications

Thermal condition monitoring of electrical installations based on infrared image analysis

Thermal condition monitoring of electrical installations based on infrared image analysis

An automated thermographic image segmentation method for induction motor fault diagnosis

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

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