Automatic Diagnosis System of Electrical Equipment using Infrared Thermography. Ying-Chieh Chou Leehter Yao. Dept. of Electrical Engineering. National ...
2009 International Conference of Soft Computing and Pattern Recognition
Automatic Diagnosis System of Electrical Equipment using Infrared Thermography
Ying-Chieh Chou
Leehter Yao
Dept. of Electrical Engineering National Taipei University of Technology Taipei, Taiwan, R.O.C {t5319015, ltyao}@ntut.edu.tw technique, defective parts can be detected through simple observation of infrared images and there is no need to shut down the operation of a facility to look inside the equipment for inspection. The fact that many power installations require a large amount of manpower to conduct inspections, most power companies usually assign outside contractors to do electrical inspections for them. After outside contractors have submitted their inspection reports, the results of the inspections are further evaluated one by one by power companies to confirm their validity and to give approval for repair. Experienced personnel still have to take a lot of time to complete the difficult work of evaluation. Therefore, a system known as Infrared Thermography Anomaly Detection Algorithm (ITADA) is proposed by this paper. This computerized system uses a combination of Artificial Intelligence and digital image processing techniques so that the system can receive any amount of infrared images as input data before performing automatic inspections on them. The defective parts are detected by determining which of these areas on the infrared images are with higher temperatures than the normal prescribed levels. Inspection results are classified into different categories depending on the levels of temperatures detected that tell the power companies the seriousness of each situation in each of these areas. The advantage of the system is to enable power companies to carry out their repair and maintenance in a timely and cost effective manner. Infrared thermography technology is a technology that uses infrared sensors and optical lenses in a constructed electrical circuitry to capture images of thermal objects based on temperature variations. Infrared thermal camera stores the infrared pictures of thermal objects as thermal images that the human can see in order to understand the inside conditions of the objects. With the images, inspectors can analyze the temperature variations of thermal objects to look for defective parts. Infrared thermography technology is a nondestructive inspection technique [2]. The inspection can be conducted efficiently by keeping a distance from the inspected equipment. There is no need to halt equipment operation while an inspection is going on. Since the collection of information for inspection is by telemetry, hazardous operations can be avoided [3]. For these reasons, Infrared thermography is widely used for many applications involving preventive maintenance [4].
Abstract—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. Keywords- Infrared; Thermography; Image Processing; Preventive maintenance; Automatic Diagnosis.
I.
INTRODUCTION
In recent years, the repair and maintenance of equipment at important facilities has been a primary area of concern. Of these facilities, the repair and maintenance of equipment at power transmission facilities is listed as a task with the highest priority because our abilities to continue to enjoy the quality of life we are enjoying now depend solely on the continuous operation of these equipment in the future. Power installations are usually located in every corner of small villages and big cities. This is because electricity has to be provided to wherever the consumers are conducting their indoor or outdoor activities, leading to tens of thousands of such facilities. To provide proper maintenance for many equipment at these facilities has been an important objective for every power company, but such a goal has always been a challenging one. The repair and maintenance of a facility can be classified under three different categories: when equipment malfunction, time–based, and condition-based maintenance [1]. The most popular one is condition-based maintenance, also known as preventive maintenance. Infrared thermography technique is widely used in preventive maintenance for the advantage of carrying out quick, accurate, and wide area inspections by telemetry. With this 978-0-7695-3879-2/09 $26.00 © 2009 IEEE DOI 10.1109/SoCPaR.2009.41
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show vivid distinction between the main objects and their backgrounds must not be included in an inspector report. The abnormal conditions from infrared detections are usually explained by the extreme temperature values. For example, overheating that occurs to a certain area of a power installation is the result of the higher resistance encountered at a defective part where current flow is resisted [6]. Moreover, when the surrounding temperature of a piece of refrigeration equipment falls below an acceptable level, one of the likely causes is a coolant leakage from a damaged sidewall of the equipment. This paper only discusses the overheating issue caused by defective parts since it only focuses on inspection of electrical equipment and power facilities. By analyzing extreme temperature variations, our objective is to identify probable location of defective parts for those staff people responsible for repair and maintenance to carry out preventive maintenance [7].
This paper is organized as follows. Section II contains a brief summary of the problem statement. Section III states the main algorithm for anomaly detection. Finally, the experiment results and conclusion are shown in Section IVV. The results of our experiments are as shown to justify the effectiveness of the thermal diagnosis system applied at various power facilities to improve inspection efficiency. II.
PROBLEM STATEMENT
A typical report usually contains an infrared thermography and a visible spectrum photo, as indicated in Fig. 1.
Figure 1. A Infrared thermography and a visible spectrum photo.
The infrared thermography is usually converted into colorful images using a color palette for human observation. In order to expedite image processing steps, the colorful images are again converted into gray images as shown in Fig. 2.
Figure 3. The capacitor.
Figure 4. The transformer.
Figure 2. The hotspot and the reference point.
In this paper, our experiment samples include capacitors, transformers, and other power transmission equipment as illustrated in Fig. 3-5. When thermal images are captured inappropriately, the image color can appear to be too bright or too dark, monotonous [5]. These thermal images with flaws tend to increase the amount of mistakes inspectors made by making wrongful judgments during an inspection. It is suggested by this paper that thermal images that do not
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A morphological image processing theory is adopted [9] in order to extract the hotspots. Firstly, the maximum pixel value of the image γ has to be determined as shown in (3).
Figure 5. An electric cord.
For inspection, the diagnosis system needs to first find a hotspot (normally an overheated connecting point) and to choose a reference point from the neighborhood of the connecting point. The reference point has to be picked from an area with the same structure and similar features as the hotspot which is as shown in Fig. 2. The purpose of this diagnosis system is to replace the time consuming manual detection of defective parts. Hence, the “Infrared Thermography Anomaly Detection Algorithm” (ITADA) is developed to automate the diagnosis process. III.
Thot − gray =
⎧⎪1 if α ( x, y ) = Thot − gray Ω 0 ( x, y ) = ⎨ ⎪⎩0 otherwise ∀0 ≤ x < W , 0 ≤ y < H
(4)
Calculate the connected components with the seed image Ω0 in the foreground image γ with the experiential limit of neighbor’s difference of gradient value 16, as dictated in (5) where k is the iteration times. Ω k −1 ⊕ B expresses a dilation process. If Ω k −1 ( x, y ), ∀0 ≤ x < W , 0 ≤ y < H is equal to 1 then dilates itself according to Ω k −1 ( x + s, y + t ) , s = t = [−1,1] , s and t are discrete integers, due to B is an 8-neighbors mask. C is a constraint of the experiential limit of neighbor’s difference of gradient value 16 in the foreground image γ . Ω k = (Ω k −1 ⊕ B ) ∩ C , k =1,2,3,"
(5)
A dilation point would be ineffective if the constraint C not be satisfied. The algorithm has converged when Ω k = Ω k −1 then gets the converged image Ω∗ . Finally,
(1)
Ω∗ represents the image of all hotspots’ connected components as shown in Fig. 9. Define the every connected component’s area of image Ω∗ as the sets of points Ai , i =1" J , J = number of hotspots. Considering the noises elimination, the amount of every connected component in Ω∗ is calculated as Di , i =1" J then the small noise can be ignored and the maximum hotspot would be found, as indicated in (6).
∀0 ≤ x < W , 0 ≤ y < H Likewise, all the pixel values of image α are set to 1 when the pixel values are greater than T . On the contrary, the pixel values are set to 0 when they are less than T . In order to obtain object image γ from image α and β , the following formula is used:. ⎧α ( x, y ) if β ( x, y ) = 1 γ ( x, y ) = ⎨ if β ( x, y ) = 0 ⎩0
(3)
seed image Ω0 can be made to calculate the connected components, as shown in (4).
The implementation of “Infrared Thermography Anomaly Detection Algorithm (ITADA)” by the paper is based on the principle of Otsu’s statistical threshold selection algorithm using gray-level histograms [8]. The reason for using this algorithm on power facilities is their equipment always have temperatures higher than the environment. By using a thresholding method, the image of the main object can be separated from its background. Experienced photographer normally takes picture image that shows a clear distinction between the main object and the background of the picture as shown in Fig. 2. The ITADA algorithm can be used to easily separate the object from the background. Let α represents the original image and β represents the extracted binary image of the main object from α . As shown in (1), T is the threshold value by Otsu’s method; W is image width and H is image height. Since this is a digital image, x and y are discrete integers.
⎧1 if α ( x, y ) > T ⎩0 if α ( x, y ) ≤ T
( γ ( x, y ) )
According to the maximum hot value Thot − gray , an initial
INFRARED THERMOGRAPHY ANOMALY DETECTION ALGORITHM (ITADA)
β ( x, y ) = ⎨
max
0 ≤ x
