No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Using image pre-mapping for applications of monitoring electrical switchboards
... This method requires the operator to know how to use thermal cameras and to have knowledge of thermal imaging and image processing. To simplify the method, a classifier has been developed, which, through computational processing, detects the operating status of components [31]. In the case of an energy audit, in addition to thermography, measurements of electrical parameters are also necessary [32]. ...
With technological development and the decrease in the price of electrical appliances for domestic consumers, combined with users’ desire for a certain level of comfort, the number of electrical devices in homes has increased. Although current equipment is largely energy-efficient, the high number of these devices, used simultaneously, has led to an increase in electricity consumption. Electrical installation in homes has been replaced in most cases, but the connections have remained the same, without any checks being carried out in terms of load and operational safety. This paper presents an exploratory case study based on a questionnaire answered by 678 individuals from Romania. The objectives of the questionnaire were to identify the characteristics of the domestic consumer and their opinion on the state of the installation, energy costs, and energy consumption. Based on the analysis, 74.15% of the respondents stated that the power installed in their homes as domestic consumers increased. Based on the analysis conducted on-site, the risk of fire outbreaks in the power supply columns of apartment buildings was identified. To avoid critical situations, it is necessary to periodically check the connection and supply columns so that they are functional and safe to operate.
... In the scientific and technological field, the use of infrared thermography has been oriented towards veterinary, medical and safety applications. Among the studies developed using infrared technology, it is possible to find the work done by [4], in which a study of the inflammation caused by mastitis in the mammary glands of women was carried out, making use of a data bank of thermographic images using the gray scale color palette [5]. In the veterinary field, thermal analysis has helped to detect different diseases in animals through the interpretation of the hot spots obtained in the thermograms. ...
The constant technological innovation in devices for the acquisition of digital images such as: energy-efficient and high-pixel sensors, memories with greater storage capacity and processors capable of sampling digital signals more quickly, have made it possible to digitize with greater reliability real life scenes in an instant of time, making it possible to analyze and interpret different physical phenomena [1][2][3] such as fractures in materials, evasion of obstacles, weather conditions, injury detection, among others, giving rise to a new line of research called Artificial Vision (AV) focused on generating algorithms to improve image quality, segment characteristics of interest and eventually recognize patterns, in order to make more efficient image processing for the solution of problems in robotics, automation, security, medicine, veterinary, and others. The research aims to develop a database of thermographic images of pregnant and non-pregnant sheep, providing a tool for specialists in the area of computer intelligence and artificial vision.
... Due to the fast advance in the features of infrared thermal imagers, which has come together with their progressive cost reduction, the use of this technique has expanded to many industrial applications [11]. For example, it is used in civil engineering [12], mechanical engineering [13], manufacture of electrical [14] and mechanical [15] components, as well as in the detection of failures in electrical systems [16,17] and assessment of the condition of electrical equipment [18,19]. ...
During the lifelong maintenance of low-voltage electrical installations in any facility special attention must be given to fire-causing failures, which cannot be detected by conventional protection devices (miniature circuit breakers, fuse-links, residual current devices, etc.). Such failures are most often caused by poor electrical contacts. International regulations and standards, which define periodic verification of the quality of low-voltage electrical installations, do not contain procedures by which a poor electrical contact would be detected at an early stage. However, one of the techniques used for this purpose today is performing preventive thermographic inspections (a standard covering this area is available only in the USA). By such inspections a poor electrical contact which creates prerequisites for the occurrence of an initial fire in a low-voltage electrical installation can easily and effectively be detected. Detected failures and irregularities can most frequently be eliminated by simple interventions of electricians or facility technical services. Experience from a large number of periodic verifications of the quality of low-voltage electrical installations in industrial and administrative facilities, conducted by personnel of the Laboratory for testing low-voltage electrical and lightning protection installations at the School of Electrical Engineering in Belgrade, showed that many dangerous failures would not be detected without performing preventive thermographic inspections. Several practical examples of such failures, detected in low-voltage electrical installations in industrial and administrative facilities during preventive thermographic inspections, are presented and analysed. The procedure of performing preventive thermographic inspections within periodic verifications of the quality of low-voltage electrical installations, as well as the explanation for proper interpretation of the measurement results based on the ΔТ and absolute temperature criteria, are also given in this paper.
... Due to the fast advance in the features of infrared thermal imagers, which has come together with their progressive cost reduction, the use of this technique has expanded to many industrial applications [11]. For example, it is used in civil engineering [12], mechanical engineering [13], manufacture of electrical [14] and mechanical [15] components, as well as in the detection of failures in electrical systems [16,17] and assessment of the condition of electrical equipment [18,19]. ...
During the lifelong maintenance of low-voltage electrical installations in any facility special attention must be given to fire-causing failures, which cannot be detected by conventional protection devices (miniature circuit breakers, fuse-links, residual current devices, etc.). Such failures are most often caused by poor electrical contacts. International regulations and standards, which define periodic verification of the quality of low-voltage electrical installations, do not contain procedures by which a poor electrical contact would be detected at an early stage. However, one of the techniques used for this purpose today is performing preventive thermographic inspections (a standard covering this area is available only in the USA). By such inspections a poor electrical contact which creates prerequisites for the occurrence of an initial fire in a low-voltage electrical installation can easily and effectively be detected. Detected failures and irregularities can most frequently be eliminated by simple interventions of electricians or facility technical services. Experience from a large number of periodic verifications of the quality of low-voltage electrical installations in industrial and administrative facilities, conducted by personnel of the Laboratory for testing low-voltage electrical and lightning protection installations at the School of Electrical Engineering in Belgrade, showed that many dangerous failures would not be detected without performing preventive thermographic inspections. Several practical examples of such failures, detected in low-voltage electrical installations in industrial and administrative facilities during preventive thermographic inspections, are presented and analysed. The procedure of performing preventive thermographic inspections within periodic verifications of the quality of low-voltage electrical installations, as well as the explanation for proper interpretation of the measurement results based on the ΔТ and absolute temperature criteria, are also given in this paper.
Many efforts have been made to define patterns, predict, and forecast energy use. However, changes in energy consumption may be studied in detail using various methodologies. This work presents a statistical methodology to assess changes in a facility consumption profile. Consumption patterns are obtained from a historical database of a predefined time interval, according to the type of day (day of the week, working or non-working), and an index that assesses change in the electrical consumption profile is proposed. Assessing these changes enables associating these values with possible events in a facility, which can serve to generate alarms in an energy management system, and reduce costs and maintenance periods. Additionally, a multi-criteria interpretation of the applied test table is presented that offers explanations and identifies possible causes of anomalous consumption.
Purpose
Taking into account the current relevance of the concept of smart city connected with the Internet of Things, this work aims to study the implementation of this concept by applying a new energy model in an existing public building. The purpose of this paper is to enhance the sustainability and energy autonomy of the building.
Design/methodology/approach
The building referred to in the case study is a library, and simulations related to the ongoing study are based on an energy audit, comprising a survey on electrical and thermal energy consumption. The innovative proposed model consists of a mix of energy production processes based on photovoltaic panels and biomass boilers. Economic analysis of the energy model has already yielded some results regarding the payback on investment, as well as avoided emissions in the context of development of a low-carbon economy with avoided emissions and socioeconomic advantages.
Findings
It is possible to enhance the sustainability of the library studied by the retrofit of the current energy system. With the integration of photovoltaic panels and the conversion or replacement of boilers from natural gas to biomass, the GHG emissions could drop around 121 t CO2 per year. Another benefit would be the inclusion of endogenous resources over imported energy resources. The payback period for the measures proposed ranges from 2.5 to 8 years, proving that the increase in environmental sustainability is viable.
Originality/value
The intention here is to implement the concept of smart city, in more sustainable buildings, bringing them to the lowest possible energy consumption levels, hence increasing performance and comfort. Also, taking into account that the energy-consuming buildings are already constructed, it is urgent to reconvert them to lower the use of energy and emissions using technologies based on renewable energy, boosting the use of local resources.
The 'bag of visual features' image representation was applied to create generic microstructural signatures that can be used to automatically find relationships in large and diverse microstructural image data sets. Using this representation, a support vector machine (SVM) was trained to classify microstructures into one of seven groups with greater than 80% accuracy over 5-fold cross validation. In addition, the bag of visual features was implemented as the basis for a visual search engine that determines the best matches for a query image in a database of microstructures. These novel applications demonstrate the potential and the limitations of computer vision concepts in microstructural science. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND.
An automatic diagnosis system is proposed by this paper for a more and more important issue, preventive maintenance. Every year, various workplace accidents happen due to undesirable maintenance. No matter how stringent the rules governing the maintenance of electrical equipment may be, it is always a challenge for the power industry due to the large number of electrical equipment and the shortage of manpower. In this paper, an automatic diagnosis system for testing electrical equipment for defects is proposed. Based on nondestructive inspection, infrared thermography is used to automate the diagnosis process. Thermal image processing based on statistical methods and morphological image processing technique are used to identify hotspots and the reference temperature. Qualitative and quantitative analyses are carried out on the gathered information and inspection results are presented after being processed by the diagnosis. The thermal diagnosis system proposed by this paper can be used at the various power facilities to improve inspection efficiency as illustrated in the experiment results.
Analysis and processing of databases obtained from infrared thermal inspections made on electrical
installations require the development of new tools to obtain more information to visual inspections.
Consequently, methods based on the capture of thermal images show a great potential and are increas-
ingly employed in this field. However, there is a need for the development of effective techniques to anal-
yse these databases in order to extract significant information relating to the state of the infrastructures.
This paper presents a technique explaining how this approach can be implemented and proposes a sys-
tem that can help to detect faults in thermal images of electrical installations. The proposed method clas-
sifies and identifies the region of interest (ROI). The identification is conducted using support vector
machine (SVM) algorithm. The aim here is to capture the faults that exist in electrical equipments during
an inspection of some machines using A40 FLIR camera. After that, binarization techniques are employed
to select the region of interest. Later the comparative analysis of the obtained misclassification errors
using the proposed method with Fuzzy c means and Ostu, has also be addressed.
�
Distribution network components connect machines and other loads to electrical sources. If resistance or current of any component is more than specified range, its temperature may exceed the operational limit which can cause major problems. Therefore, these defects should be found and eliminated according to their severity. Although infra-red cameras have been used for inspection of electrical components, maintenance prioritization of distribution cubicles is mostly based on personal perception and lack of training data prevents engineers from developing image processing methods. New research on the spatial control chart encouraged us to use statistical approaches instead of the pattern recognition for the image processing. In the present study, a new scanning pattern which can tolerate heavy autocorrelation among adjacent pixels within infra-red image was developed and for the first time combination of kernel smoothing, spatial control charts and local robust regression were used for finding defects within heterogeneous infra-red images of old distribution cubicles. This method does not need training data and this advantage is crucially important when the training data is not available.
Aims
Developing a new method to detect defective electrical components in the power distribution cubicles.
Place and duration of study
Tehran province, Iran, 2011–2013.
Methodology
Combination of kernel smoothing, spatial control charts and local robust regression used for finding defects within heterogeneous infra-red image of old distribution cubicles.
Results
This study showed that the IM-R control chart that plots forecasting residual of local robust regression and EWMA control chart with proper λ parameter with proper scan window size are powerful control charts which can be used to finding defected components in the power distribution cubicles.
Conclusion
In some applications like analyzing thermal images of the old power distribution cubicles, it is not possible to train a sophisticated model like artificial neural network to identified defects. Therefore, spatial control chart that does not need training data is a valuable tool for these applications.
In this paper review analysis capturing images with high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology. Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible. So we need an automated devise to process these videos. In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior. Simple object detection compares a static background frame at the pixel level with the current frame of video. The existing method in this domain first tries to detect the interest object in video frames. One of the main difficulties in object tracking among many others is to choose suitable features and models for recognizing and tracking the interested object from a video. Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points.
Infrared thermography, which has been widely used, is an important electrical equipment monitoring and fault diagnosis technology. It has two key steps about infrared thermal image processing and artificial intelligence diagnosis faults. In order to improve the accuracy of diagnosing electrical equipment thermal fault, the algorithms of denoising, segmentation and feature extraction in image processing, the BP and RBF network model of neural networks for intelligent diagnosis are discussed with the specific experimental conditions, the advantages and disadvantages of the various technologies and the improved methods are pointed out.
Infrared thermography (IRT) has gained more attention and become an interesting method in electrical preventive maintenance due to its high precision and sensitivity imaging characteristics. This paper provides a review of the application of IRT for diagnosing electrical equipment, including their thermal anomalies and methods of measurement. Improvement of the inspection techniques is highlighted in order to investigate the reliability of electrical equipments due to the effect of the environmental factors and equipment condition. Factors related to the target equipment and the inspection tool together with their characteristics is also presented. Due to the complex analysis, various automatic diagnostic systems are proposed for faster and more accurate analysis. Typical engineering solutions using recent technologies are reviewed which could be used to improve the quality of IRT inspection.
Implementation of predictive maintenance in thermography in panels power substations, Portuguese
- L R Mendes
- R N Wazen
L.R. Mendes, R.N. Wazen, Implementation of predictive maintenance in thermography in panels power substations, Portuguese, Journal of Engineering and
Technology, 8 2016 91-102p.
Implementation of predictive maintenance in thermography in panels power substations
- Mendes
A review of detection and tracking of object from image and video sequences
- Tiwari