Digital Image Processing of Axis-Symmetric

Digital Image Processing of Axis-Symmetric Electric Arc Plasma Based on Chromatic Modulation Methods D. TOMTSIS TEI of West Macedonia, Koila, Kozani GR-50100 GREECE

V. KODOGIANNIS*, * Dept of Computer Science University of Westminster London, HA1 3TP UNITED KINGDOM

Abstract: An integrated experimental approach is presented for processing the optical information produced from axis-symmetric electric arc plasma. The method is based on digital image processing and chromatic modulation techniques. Chromaticity changes in a number of chromatic parameters are related to changes in physical electric arc characteristics and properties (e.g. regions, temperature). The results are in the form of a chromatic contour map which shows how the overall electric arc and its environment behave and respond. Such maps show the totality of information which can be accessed about the arcing event and the level of monitoring discrimination which is achievable with the chromatic methodology in a simple and easy to understand human vision compatible manner. The suggested method provides easier data analysis and high levels of data compression. Keywords: electric arcs, digital image processing, chromatic modulation, area contour map.

1.

Introduction

Monitoring electric arcs is a difficult process because of the complexity of the phenomena which govern arc behaviour. The physics of these processes and properties of the electric arcs are not fully understood due to the powerful mathematical analysis and considerable experimental effort required to model their complex characteristics [1]. For example, the arc plasma column has properties which vary with time and with radial, axial, and azimuthal coordinates. Phenomena at the arc electrodes are also complex and interactions with the plasma column as well as the interaction of the arc with its environment add to the complications [2]. Investigation of the electric arc phenomena has led to the identification of independent conditions which are simplified by eliminating some of the complexities described above, and perform detailed, highly accurate and localised measurements on that condition [3]. However, these measurements are made at the expense of determining how the overall electric arc and its environment behave and respond. Spectroscopic detection and analysis techniques were also used in plasma monitoring applications but they are cumbersome and slow for on-line process control. Often, such detailed knowledge is unnecessary for such process applications and all that is required is the identification of particular spectral signatures which are associated with certain quantities or parameters. An electric arc is a self sustaining electrical discharge consisting of a plasma at temperature in excess of 4000 K and usually less than 25000 K at high (>=1 Bar) pressures. It may be distinguished by other electrical discharges by its ability to conduct relatively high currents (from a fraction of an ampere to

hundreds of kilo amperes) whilst being sustained by a relatively low electric field strength. Examples of systems where electric arcs occur include power system protection devices (such as fuses and circuit breakers), material processing devices (such as welding systems and furnaces) and light sources (including arc lamps for illumination and as standard spectral sources). In general, electric arcs consist of three physically distinct regions which correspond to two electrode regions and an interconnecting plasma column. The relative importance of the three regions in governing the overall behaviour of the arc depends upon a number of factors which include the length of the arc gap, the type of arc and the electric power dissipated in the discharge. Electric arcs may be subdivided into two main categories which are axis-symmetric and non axissymmetric arcs. The axis-symmetric arc column burns symmetrically along the inter-electrode axis so providing good cylindrical symmetry, which is ideal not only for theoretical modelling but also for experimentation with sophisticated diagnostic techniques. A form of axis-symmetric arc without any boundary constraints is the ‘free burning arc’ and is representative of furnace type arcs. Non axissymmetric arc columns are less amenable to diagnosis because of the reduced cylindrical symmetry they provide. In this work an integrated experimental approach based on optical techniques and in particular chromatic modulation is used to provide information about the arc itself and the arcing event. This approach is concerned with information compression for reducing the dimensionality of information about complex arcenvironment interactions. Axis-symmetric arc

processing is involved with monitoring the optical emissions from low temperature plasma from the point of view of the signal processing and optical signal information it provides. The suggested method provides easier data analysis and high levels of data compression.

2.

Chromatic Modulation Theory

The essence of chromatic modulation - an integrated form of spectral monitoring - is the utilisation of polychromatic light for sensing spectral changes by monitoring the total profile of an optical signal within a spectral power distribution. Chromatic changes can be monitored by a number (n) of detectors with overlapping spectral responses. The output of each detector may then be expressed as [4]

Vn = ∫ P(λ )Rn(λ )dλ

(1)

where P(λ) is the spectral power distribution in the optical signal and Rn(λ) is the wavelength responsivity of the nth detector and λ is the wavelength. The chromatic model representing this mathematical formalism is generally called RGB and it is widely used in self-luminous display technologies. Each detector output may also be intensity normalised according to:

un =

Vn ∑n VT

with mi=0 for i=1, j=2, Vmin=V3, mi=1 for i=2, j=3, Vmin=V1, mi=2 for i=3, j=1, Vmin=V2

(4) (5)

with m2=0, m3=-1 for L≤50 and m2=m3=1 for L>50. Vmin and Vmax are the minimum and maximum detector outputs [6].

3.

Experimental Apparatus

Experiments on electric arc plasma were performed in order to evaluate the ability of the suggested remote monitoring system to detect chromatic changes in electric arc plasma and to develop and extend the necessary digital image processing tools into twodimensional space for processing electric arcs plasma. The remote monitoring system is computer based and is broken down into three distinct and important elements: a camera, a colour frame grabber card (C.F.G.), and a host personal computer.

(2)

where chromaticity maps may be formed in terms of the coordinates u1, u2, … u(n-1). The case of n=3 leads to a two-dimensional chromaticity map (u1:u2) on which changes in optical signals may be traced. The special case when R1(λ), R2(λ), R3(λ) correspond to the responsivities of the human eye leads to the chromaticity map reducing to the CIE diagram of colour science [5]. The chromatic model representing this mathematical formalism is called LXY and provides the relative magnitudes of the tri-stimulus values (i.e. X=u1; Y=u2; Z=u3). However, this method of displaying chromatic information does not easily lead to the identification of signal changes in terms of fundamental signal properties. An alternative approach to the processing of the signals from such chromatic detectors overcomes this limitation. For the tristimulus case (n=3) this approach utilises three chromatic parameters, namely dominant wavelength (H), intensity (L) and degree of monochromaticity or spectral width (S) which are defined as follows:

  Vi − Vmin   H = 120 mi +   V + V − 2V   j min    i

V + Vmin  L = 100  max  2     Vmax − Vmin S = 100    200m2 − m3 (Vmax + Vmin ) 

(3)

Fig. 1. Graphical illustration of the configuration of the remote monitoring system

The camera converts the spatially distributed optical information into analogue electric signals which are accepted by the colour frame grabber card (C.F.G.). The signal is then digitised and stored in the card's frame memory. The C.F.G. card is the interface between the camera and the host personal computer which is used to access and programme the card's registers, frame memory, and buffers. Fig. 1 illustrates graphically the configuration of the system. The CCD camera was pointing at the device illustrated in fig. 2 in order to capture a number of low current free burning electric arcs. A number of arc forms, such as free burning electric arcs, are easily monitored optically by CCD devices, since they are optically accessible and not mechanically enclosed, such as circuit breaker arcs. A white light source was not used since the arcing event by itself produced the necessary illumination. Two carbon electrodes and a 5A-fused wire were used to produce electric arcs that were approximately 2 to 5 cm in length. Neutral density filters were used to reduce the intensity of the image and to ensure that the

operation of the camera was in the correct range by

in an image. Although edge detection and region growing represent different approaches to image segmentation, solving one can help, enhance or automatically solve the other [7].

5.1 Image Noise Minimisation

Fig. 2. Apparatus used to produce low current free burning electric arcs.

avoiding saturation of the CCD elements, and also to provide information concerning emission from the weakly emitting peripheral regions of an arc column. A camera image of such an electric arc was obtained with the remote monitoring system equipped with the VISIONplus AT CFG card and the PULNIX colour CCD camera.

4.

Experimental Method

The optical information emitted from an electric arc can be analysed at various levels of sophistication by recording a transient or fluctuating arc in order to determine the spatial position, size, arc movement, light intensity or chromaticity. The methodology involves the successive recording of two-dimensional images each taken with a given exposure time which are then chromatically processed to provide high levels of data reduction and easier analysis. This method was applied to a number of electric arc images to generate area contour maps of constant chromaticity, dominant wavelength or monochromaticity.

5.

Processing of electric arc images

The strategy used to produce area contour maps of equal property values was based on the direct manipulation of pixels in an image. The adopted technique is based on a framework of the simultaneous modelling of edges and regions where region growing algorithms are first applied to emphasise each region and to reduce image noise as much as possible, and then edge detection methods are employed to produce strongly defined and continuous edges. The combination and simultaneous implementation of edge detection and region growing algorithms allows the definition and specification of both edges and regions

The first step of this procedure was to minimise the unwanted noise in the image by utilising the fact that electric arc images contain at least two distinct intensity levels: the actual arc image which is very bright and the background which is usually at a much lower intensity. Hence, intensity information was combined with hue or saturation information to eliminate the redundant image areas and noise. If the intensity of a pixel within an arc image was below a threshold value then its value was set to zero, otherwise its hue or saturation value was obtained, a procedure which was repeated for all the pixels in the image in order to produce a hue or saturation map of the original arc image.

5.2 Connected Components Connected components is a method that creates a collection of image regions, the pixels of which have some set of properties in common. The adopted approach was region growing by pixel aggregation [8], where image regions ‘grow’ in all directions starting from pixels that meet a detection criterion or pixel property. The neighbours of the initially accepted pixels are then examined and appended to those pixels if they satisfy a selected pixel property. Any number of properties can be the basis of region growing including colour, hue angle, texture, direction, as well as connectivity or adjacency information. The acceptance criterion can depend on the similarity of hue angle between the currently examined pixel and its neighbours, or a carefully selected threshold value, or the position, size and shape, depending on the application and the growing process which might be biased to favour certain types of object sizes and shapes. The process is then repeated with the neighbours of the newly collected pixels and continuous until no further pixels satisfy the condition. Then another pixel is selected to begin a new region, which is ‘grown’ in the same way. The method continues until all pixels have been processed or assigned to a region. For complex images which are not uniform and homogeneous, region growing techniques produce unsatisfactory results. The presence of ragged boundaries, small holes, and image noise leads to objects being connected when they are clearly distinct and vice versa, and the formulation of unwanted objects, usually small in size, which are absent in the original image. In order to reduce this effect the size in pixels of each detected object was monitored, which was compared

with a suitably selected threshold value. If the object size was less than the threshold then the object was characterised as image noise and depending on other connectivity or adjacency information, the object was either deleted or joined with neighbouring objects.

5.3 Image Contouring Image contouring is a process which identifies image areas of constant pixel property values, such as hue angle, saturation, intensity or any other desired property. There are two contour types [9]; line and area contours. Line contours are formed when an image is converted to a map showing only lines which enclose areas of equal pixel property values. On the other hand, a map showing pseudo-coloured areas between the contour lines is known as an area contour map which is usually preferred to a line contour map because it is easier to see and understand the information displayed. The edges or contour lines of the coloured areas are usually smoothed to give a better representation of reality.

represent contour lines. As a further improvement, the contour lines are smoothed by median filtering methods [11] to give a better representation of reality. The final map is an area contour map of equal hue angle. The same process can be used to produce area contours of equal saturation or any other desired property. Fig. 3 shows a schematic diagram of the above procedure.

6.

Experimental Results

Fig. 4 shows a photographic record of a low current free burning arc captured by the remote monitoring system. Figs 5 and 6 show respectively twodimensional, colour coded, area contour maps of both constant dominant wavelength and monochromaticity of the same electric arc. In such a way, contours of constant dominant wavelength and monochromaticity were produced to demonstrate how facets of an arc structure could be conveniently represented.

Original Map or image Hue Angle is the selected pixel property Hue Angle Map Region Growing Method Connected Components Map

Edge Detection

Area Map Edge Smoothing Area Contour Map of equal Hue angle

Fig. 3. Schematic diagram of the algorithm used to produce area contours of equal hue angle

The method devised for the implementation of area contours involves the application of region growing, edge detecting and image smoothing methods. If for example area contours of equal hue angle are required, then the selected pixel property in the region growing process is hue angle. Hence, a hue map is produced by replacing the value of each pixel in the original image by its corresponding hue value in the resulting map. Connected objects are then formed by examining the hue of a current pixel with the hue of its neighbours and depending on a threshold value, neighbours are appended to the current pixel or rejected. Each object formed in the image contains pixels with a constant (plus or minus an offset) hue value. Application of edge detection methods, such as the Sobel operators [10], to this intermediate result provide strongly defined edges enclosing objects of constant hue values. Hence, the defined edges in the image

Fig. 4. Photographic record of a typical electric arc image captured by the remote monitoring system

In particular, fig. 5 shows a vertical hue angle variation from an average value of 1650 to 3300 and a horizontal variation from 1650 to 2250 as we move away from the arc core towards the outer arc area. Both variations represent a dominant wavelength shift towards the red side of the spectrum. Fig. 6 also shows a saturation shift from a polychromatic state in the arc core (many wavelengths) towards a monochromatic state (fewer wavelengths present) in the outer arc regions. The above observations are related to arc temperature and mean that the outer arc area is cooler than the core. Hence, they provide spatial information about an arc (location of arc regions such as its core) and temperature variation between different arc regions, information that is not available from intensity maps or photographic records of the same electric arc image. In general, the chromatic information provides an intermediate level of information about the entire arc which is more detailed than that obtained from conventional photography and spatially more

extensive than can be obtained from localised spectroscopic measurements. The results of these figures are presented not for detailed physical 150

Hue Map

125 100 ABO VE 330 300 -330 270 -300 240 -270 210 -240 180 -210 150 -180 120 -150 90 -120 60 - 90 30 - 60 BELO W 30

75 50 25

25

50

75

100

125

150

Fig. 5. Area contour map of constant hue angle. The x and y axis of this map represent pixel positions.

interpretation but rather to indicate the level of monitoring discrimination which is achievable with the chromatic methodology and the totality of the information about the arcing event which may be accessed. The chromatic results were obtained directly from the pixels of the CCD camera to produce human vision compatible images. 150

Saturation Map

125 100 A BO V E 10 9 - 10 8- 9 7- 8 6- 7 5- 6 4- 5 4- 4 3- 4 2- 3 1- 2 B ELO W 1

75 50 25

25

50

75

100

125

150

Fig. 6. Area contour map of constant saturation. The x and y axis of this map represent pixel positions. In general terms, contours of equal property values compress unwanted information and present only vital components such as the direction and length of each line and the relationship of the lines to each other. The resulting image contains much less information than the original by producing a simple and easy to understand human vision compatible image.

7.

Conclusion

A feasibility study on electric arc images proved that the suggested remote monitoring system was capable of processing the morphological features of the plasma such as its shape, boundary, as well as the measurement of other quantities such as colour and chromaticity. Electric arc plasma experiments have

shown that chromaticity changes in a number of chromatic parameters can be related to changes in physical electric arc characteristics and properties (e.g. regions, temperature). It is also of interest that different chromatic parameters (such as hue and saturation) yield different information from the same optical emission. The combination of digital image processing techniques and chromatic analysis provided maps of dominant wavelength and monochromaticity produced from the same captured data, which are useful for locating plasma areas of similar properties. For monitoring applications it is changes in these areas rather than their detailed properties that are often of interest. Such maps show the totality of information which can be accessed about the arcing event and the level of monitoring discrimination which is achievable with the chromatic methodology in a simple and easy to understand human vision compatible manner. The experimental results have also demonstrated the potential of chromatic processing methods to be used as an arc classification method depending on the arc geometry. References: [1] Jones G.R., High Pressure Arcs in Industrial Devices: Diagnostic and Monitoring Techniques, Cambridge University Press, Cambridge, 1988. [2] Jones G.R., and Fang M.T.C., The Physics of High Power Arcs, Rep. Prog. Phys., 43, pp. 1415-1465, 1980. [3] Flurscheim C.H., Power Circuit Breaker Theory and Design, Peter Peregrinus Ltd., Herts, 1975. [4] Tomtsis D., Kodogiannis V., Zissopoulos D., Advances in Systems Science: Measurement, Circuits and Control: Analysis and Measurement of the Modal Power Distribution for Guiding Multimode Fibres, 5th WSES/IEEE Conf. CSCC-MCP-MCME 2001, Crete, Greece July 2001, pp. 3451-3456. [5] Hunt R.W., Measuring Colour, J. Wiley and Sons, New York, 1987. [6] Levkowitz H., Herman G.T., GLHS: A generalised lightness, hue and saturation colour model, CVGIP (Graphical Models and Image Processing), Vol. 55, 1993, pp.271–285. [7] Kartikeyan B. and Sarkar A., A Unified Approach for Image Segmentation Using Exact Statistics, Computer Vision, Graphics and Image Processing, 48:217-229, 1989. [8] Gonzalez R.C. and Wintz P., Digital Image Processing, Second Edition, Addison-Wesley, 1987. [9] Green W.B., Introduction to Digital Image Processing, Van Nostrand Reinhold Company, New York, 1983. [10] Foley J.D., Dam A.V., Feiner S.K., Hughes JF, Computer graphics: Principles and practice, 1995, Addison-Wesley. [11] Niblack W., An Introduction to Digital Image Processing, Prentice / Hall International, 1985.