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New Monitoring System for Thermal Power Plants using Digital Image Processing and Sound Analysis
Copyright © IFAC Control of Power Plants and Power Systems, Cancun, Mexico, 1995
New Monitoring system For Thennal Power Plants using Digital Image Processing and Sound Analysis Toshinori Oikawa, Masaaki Tomizawa and Sadao Degawa
Ishikawajima-Harima Heavy Industries Co. ,Ltd. 1-15, Toyosu 3-Chome, Koto-ku, Tokyo /35, JAPAN I phone: +81 -3-3534-3564 fax:+8I -3-3534-35/0 E-mail:[email protected]
Abstract: Recently in thermal power plants, many components are operated on severe condition because of various demands for enormous electric power and frequent load regulation. In a control room. it is difficult to know accidents such as fue. fuel oil leak. and unusual sound, at a moment's notice. Maintenance operators. therefore. have to patrol the plant yard to find out them. To release from the patrol, the needs for automatic monitoring system to detect those accidents are increasing. We have developed a new intelligent monitoring system using advanced image and sound analysis. which is reference to human senses, to realize the request. This system can detect oil and steam leak from burners, unusual sound from machines, and. fire and smoke in the circumference. This paper describes the system outline, features, and methods of image processing and sound analysis. Keywords: Monitoring. Fault detection. Image processing, Plants, Computer controlled systems
1. INTRODUCTION
2. APPROACH
Recently the needs to prolong plant components' lives, using monitoring and diagnostic technologies. becomes increasingly important. The data which are necessary for diagnoses of conditions of the plant equipments can be obtained by observing performance of the components in the central operation room . Such a system contributes to appropriate plant operation and planned maintenance. But incipient stage of failures such as steam leak, fuel oil leak. and unusual sound can hardly be detected in the control room. Maintenance operators, therefore. have to patrol the plant yard to find out them . Detecting such accidents in early stage is important to prevent catastrophic failure. To release from the patrol, the needs for automatic monitoring system to detect those accidents are increasing. We have developed a new intelligent monitoring system using advanced image and sound analysis , which is reference to human senses. to realize the request. This system can detect oil and steam leak from burners. unusual sound from machines, and, fire and smoke in the circumference. It gives great benefits for early detection of serious power plant accidents.
2.1 System description
The automatic monitoring system is composed of three parts : sensing systems which are made up of TV camera, microphone, and thermometer, and are set up in front of target components; an operation console, a TV display monitor, and a speaker in the control room ; a controller which samples and analyzes sensory data in the computer room. Major hardware components of the system is depicted in Fig. 1. Sensors
Usually plant operators pay attention to a lot of important components by their eyes, ears, and hands on patrol. Instead of human senses, we chose and set up TV cameras, microphones and thermometers. in front of the components. A pair of TV camera and thermometer is fit on a~wo-degree-of-freedom mount which is controlled by CPU, and monitors burners. (Fig.2)
221
In control room
In front of eguipments
.. ....
16 TV cameras
Sound Analyur
~.
Power Supply
.....
2 depee- of -freedom mount
~
I=:::::::=~
A monorail
7 Microphones
lmag< Processor
a=J-t----I "
i
I
Power Supply
i
Fig.I. Major hardware components of the system Microphones are used to measure noise levels from rotary machines, such as FDFs (Forced-Draft-Fans), GMFs (Gas-Mixing-Fans), and IDFs (Induced-DraftFans). (Fig.3) User interface of the system
An operation panel which has buttons and lamps is used as interface for plant operators. The system also has a TV display monitor and a speaker in the control room , and plant operators can monitor images and sound at each site. Any command operation can be carried out by merely pressing one of keys . Text information (for example, component's name and alarm messages etc .) is superimposed on the TV display monitor. And, all the status information of the system is output on lamps of the key buttons; i.e. on, off, or flickering . Thus, it is very simple and easy to operate this system. The operation panel , the TV display monitor, and the speaker in the control room are shown in Fig.4.
Fig.2. A pair of TV camera and thermometer at the burner floor
Fig.4. The operation panel, the TV display monitor, and the speaker in the control room
Fig.3. A Microphone for the bearing of the FDF
222
Controller
Because the tray is not fixed, first, the area is found by searching the mark of the tray . Second, the problematical part in the area is extracted using the gray scale morphology algorithm. After this process, the above mentioned color matching technology is applied in the same way.
The controller which is composed CPU(MC68030), an image processor and a sound analyzer, are mounted to VME-bus system. It also provides a terminal interface for maintenance. The controller samples all information (image, sound, generated power loads, signals of burner ignition, and operation signals from the user interface panel) and performs data-processing to detect accidents. And historical data are stored in a hard disk.
2.2 Methods of Detecting Accidents
.
•
Sn.1
: In ·/n., __~ InG]-J
Image Processing
The serious accidents are divided broadly into following two categories. The first is fire, smoke, and! or steam leak from high-pressure pipe lines, and the other is oil leak from burner injection guns. We have individually developed the algorithms for them as follows.
Fig.5. The detecting method of swaying area Measurement by thermometer
Detecting fire , smoke, and/or steam leakage
The radiation thermometer measures the temperature of a hot object by focusing the thermal radiation emitted by the object. The radiation thermometer is fit on a two-degree-of-freedom mount (with TV camera) and measures the surface temperature of the boiler.
We utilized their common characteristic that they sway continuously. First, the system acquires several successive images from a color CCD camera, and gets differential images between two contiguous images, to obtain the swaying area.
Sound Analysis
That is: Sn =In+l - In St = Q=I + Sn In : n-th acquired image (n~ 1) Sn : n-th differential image St : accumulated image of Sn N : the number of differential images
We use a sound analyzer to detect unusual sound that occurs from rotary machines. The system divides sound information caught through a microphone into ten octave bands for audible frequency domain (032KHz), and gives an alarm when more than one of the sound levels on the octave bands exceed(s) the threshold ranges which are preset for several plant conditions. Result of sound analysis is displayed on the TV display monitor in the form of a graph.
r
At this point, any objects swaying or moving in the field , including plant operators, name plates and others, are extracted.
3. EXPERIMENTAL RESULTS Next, to eliminate an incorrect information, the verification for which accident happens, is carried out using our original color matching algorithm. RGB color information in the extracted area is transformed into opponent color coordinates, that is : RG (RedGreen), BY (Blue-Yellow), and WB (White-Black). This feature vector for each pixel is examined with typical color vectors which are taught using actual standard images, and voted into each class. Finally, in accordance with the voting data, the system automatically judges whether what kind of accident occurs or not.
In this section, we present experimental results using the approach outlined in the previous section. A result image of detecting steam is shown in Fig.6. Steam is simulated by using a humidifier. Swaying area which is extracted by image processing is colored for finding it easy in the image. After color matching analysis, the result of recognition is shown on the TV display monitor. An example of detecting oil on the oil-tray is shown in Fig.7. Extracted area is also colored in the image. If color description of extracted object does not match with those of oil, it is deemed something else. A result image which a wrench is recognized as something other than oil is shown in Fig.8.
Detecting oil leakage
An oil tray with mark is put under each burner injection gun, and the system detects whether fuel oil leaks, in the oil tray area.
223
Fig.8. A result image which a wrench is recognized as something other than oil
Fig.6. A result image of detecting steam
Fig.7. A result image of detecting oil
Fig.9. A result example of sound analysis
When the sound analyzer detects unusual sound from the rotary machine, a graph shown in Fig .9 is displayed on the TV display monitor.
4. CONCLUSIONS This paper has described the new intelligent monitoring system for thermal power plants. That is a feature that the system can detect automatically accidents such as leak of fuel oil and steam from burners, fire , smoke, and unusual sound of rotary machines utilizing image processing technique and sound analysis.
Now, the monitoring system works quite well in a thermal power plant and supports plant operators . The system detects any accidents accurately whenever those are happened. But it is difficult to exclude completely objects other than accidents, we should detect. As our method uses only color information, if the color distribution is matched with that of some accidents, the system may consider an object to be the accidents. For example, a plant operator who has on white uniform. To eliminate this kind of incorrect report, at this point on time, we use adopt "Double Check Method" we named, when some result is detected twice consecutively, the system gives an alarm . Using this method, incorrect alarms are dramatically decreased. However we will aggressively pursue further study for intrinsic solution.
By means of using TV cameras and microphones instead of human eyes and ears, the system riot only reduces operators' load but also rises the intelligence of thermal power plants.
REFERENCES Brad Bond (1993). Applications of Monitoring and Diagnostics to Predictive Maintenance, Technology for the '90s, pp. 273, ASME, U.S.A. P Maragos and R.D.Ziff (1990). Threshold S~perp;sition in Morphological Image Analysis Systems, Trans. Pattern Anal. Machine In tell. , vo1.12, pp.498, IEEE, U.s.A. 224
New Monitoring system For Thennal Power Plants using Digital Image Processing and Sound Analysis Toshinori Oikawa, Masaaki Tomizawa and Sadao Degawa
Ishikawajima-Harima Heavy Industries Co. ,Ltd. 1-15, Toyosu 3-Chome, Koto-ku, Tokyo /35, JAPAN I phone: +81 -3-3534-3564 fax:+8I -3-3534-35/0 E-mail:[email protected]
Abstract: Recently in thermal power plants, many components are operated on severe condition because of various demands for enormous electric power and frequent load regulation. In a control room. it is difficult to know accidents such as fue. fuel oil leak. and unusual sound, at a moment's notice. Maintenance operators. therefore. have to patrol the plant yard to find out them. To release from the patrol, the needs for automatic monitoring system to detect those accidents are increasing. We have developed a new intelligent monitoring system using advanced image and sound analysis. which is reference to human senses, to realize the request. This system can detect oil and steam leak from burners, unusual sound from machines, and. fire and smoke in the circumference. This paper describes the system outline, features, and methods of image processing and sound analysis. Keywords: Monitoring. Fault detection. Image processing, Plants, Computer controlled systems
1. INTRODUCTION
2. APPROACH
Recently the needs to prolong plant components' lives, using monitoring and diagnostic technologies. becomes increasingly important. The data which are necessary for diagnoses of conditions of the plant equipments can be obtained by observing performance of the components in the central operation room . Such a system contributes to appropriate plant operation and planned maintenance. But incipient stage of failures such as steam leak, fuel oil leak. and unusual sound can hardly be detected in the control room. Maintenance operators, therefore. have to patrol the plant yard to find out them . Detecting such accidents in early stage is important to prevent catastrophic failure. To release from the patrol, the needs for automatic monitoring system to detect those accidents are increasing. We have developed a new intelligent monitoring system using advanced image and sound analysis , which is reference to human senses. to realize the request. This system can detect oil and steam leak from burners. unusual sound from machines, and, fire and smoke in the circumference. It gives great benefits for early detection of serious power plant accidents.
2.1 System description
The automatic monitoring system is composed of three parts : sensing systems which are made up of TV camera, microphone, and thermometer, and are set up in front of target components; an operation console, a TV display monitor, and a speaker in the control room ; a controller which samples and analyzes sensory data in the computer room. Major hardware components of the system is depicted in Fig. 1. Sensors
Usually plant operators pay attention to a lot of important components by their eyes, ears, and hands on patrol. Instead of human senses, we chose and set up TV cameras, microphones and thermometers. in front of the components. A pair of TV camera and thermometer is fit on a~wo-degree-of-freedom mount which is controlled by CPU, and monitors burners. (Fig.2)
221
In control room
In front of eguipments
.. ....
16 TV cameras
Sound Analyur
~.
Power Supply
.....
2 depee- of -freedom mount
~
I=:::::::=~
A monorail
7 Microphones
lmag< Processor
a=J-t----I "
i
I
Power Supply
i
Fig.I. Major hardware components of the system Microphones are used to measure noise levels from rotary machines, such as FDFs (Forced-Draft-Fans), GMFs (Gas-Mixing-Fans), and IDFs (Induced-DraftFans). (Fig.3) User interface of the system
An operation panel which has buttons and lamps is used as interface for plant operators. The system also has a TV display monitor and a speaker in the control room , and plant operators can monitor images and sound at each site. Any command operation can be carried out by merely pressing one of keys . Text information (for example, component's name and alarm messages etc .) is superimposed on the TV display monitor. And, all the status information of the system is output on lamps of the key buttons; i.e. on, off, or flickering . Thus, it is very simple and easy to operate this system. The operation panel , the TV display monitor, and the speaker in the control room are shown in Fig.4.
Fig.2. A pair of TV camera and thermometer at the burner floor
Fig.4. The operation panel, the TV display monitor, and the speaker in the control room
Fig.3. A Microphone for the bearing of the FDF
222
Controller
Because the tray is not fixed, first, the area is found by searching the mark of the tray . Second, the problematical part in the area is extracted using the gray scale morphology algorithm. After this process, the above mentioned color matching technology is applied in the same way.
The controller which is composed CPU(MC68030), an image processor and a sound analyzer, are mounted to VME-bus system. It also provides a terminal interface for maintenance. The controller samples all information (image, sound, generated power loads, signals of burner ignition, and operation signals from the user interface panel) and performs data-processing to detect accidents. And historical data are stored in a hard disk.
2.2 Methods of Detecting Accidents
.
•
Sn.1
: In ·/n., __~ InG]-J
Image Processing
The serious accidents are divided broadly into following two categories. The first is fire, smoke, and! or steam leak from high-pressure pipe lines, and the other is oil leak from burner injection guns. We have individually developed the algorithms for them as follows.
Fig.5. The detecting method of swaying area Measurement by thermometer
Detecting fire , smoke, and/or steam leakage
The radiation thermometer measures the temperature of a hot object by focusing the thermal radiation emitted by the object. The radiation thermometer is fit on a two-degree-of-freedom mount (with TV camera) and measures the surface temperature of the boiler.
We utilized their common characteristic that they sway continuously. First, the system acquires several successive images from a color CCD camera, and gets differential images between two contiguous images, to obtain the swaying area.
Sound Analysis
That is: Sn =In+l - In St = Q=I + Sn In : n-th acquired image (n~ 1) Sn : n-th differential image St : accumulated image of Sn N : the number of differential images
We use a sound analyzer to detect unusual sound that occurs from rotary machines. The system divides sound information caught through a microphone into ten octave bands for audible frequency domain (032KHz), and gives an alarm when more than one of the sound levels on the octave bands exceed(s) the threshold ranges which are preset for several plant conditions. Result of sound analysis is displayed on the TV display monitor in the form of a graph.
r
At this point, any objects swaying or moving in the field , including plant operators, name plates and others, are extracted.
3. EXPERIMENTAL RESULTS Next, to eliminate an incorrect information, the verification for which accident happens, is carried out using our original color matching algorithm. RGB color information in the extracted area is transformed into opponent color coordinates, that is : RG (RedGreen), BY (Blue-Yellow), and WB (White-Black). This feature vector for each pixel is examined with typical color vectors which are taught using actual standard images, and voted into each class. Finally, in accordance with the voting data, the system automatically judges whether what kind of accident occurs or not.
In this section, we present experimental results using the approach outlined in the previous section. A result image of detecting steam is shown in Fig.6. Steam is simulated by using a humidifier. Swaying area which is extracted by image processing is colored for finding it easy in the image. After color matching analysis, the result of recognition is shown on the TV display monitor. An example of detecting oil on the oil-tray is shown in Fig.7. Extracted area is also colored in the image. If color description of extracted object does not match with those of oil, it is deemed something else. A result image which a wrench is recognized as something other than oil is shown in Fig.8.
Detecting oil leakage
An oil tray with mark is put under each burner injection gun, and the system detects whether fuel oil leaks, in the oil tray area.
223
Fig.8. A result image which a wrench is recognized as something other than oil
Fig.6. A result image of detecting steam
Fig.7. A result image of detecting oil
Fig.9. A result example of sound analysis
When the sound analyzer detects unusual sound from the rotary machine, a graph shown in Fig .9 is displayed on the TV display monitor.
4. CONCLUSIONS This paper has described the new intelligent monitoring system for thermal power plants. That is a feature that the system can detect automatically accidents such as leak of fuel oil and steam from burners, fire , smoke, and unusual sound of rotary machines utilizing image processing technique and sound analysis.
Now, the monitoring system works quite well in a thermal power plant and supports plant operators . The system detects any accidents accurately whenever those are happened. But it is difficult to exclude completely objects other than accidents, we should detect. As our method uses only color information, if the color distribution is matched with that of some accidents, the system may consider an object to be the accidents. For example, a plant operator who has on white uniform. To eliminate this kind of incorrect report, at this point on time, we use adopt "Double Check Method" we named, when some result is detected twice consecutively, the system gives an alarm . Using this method, incorrect alarms are dramatically decreased. However we will aggressively pursue further study for intrinsic solution.
By means of using TV cameras and microphones instead of human eyes and ears, the system riot only reduces operators' load but also rises the intelligence of thermal power plants.
REFERENCES Brad Bond (1993). Applications of Monitoring and Diagnostics to Predictive Maintenance, Technology for the '90s, pp. 273, ASME, U.S.A. P Maragos and R.D.Ziff (1990). Threshold S~perp;sition in Morphological Image Analysis Systems, Trans. Pattern Anal. Machine In tell. , vo1.12, pp.498, IEEE, U.s.A. 224