• Abstract and Figures
• Public Full-text

AUTOMATIC FAULT DETECTION BY USING WAVELET METHOD

Soundararajan Ezekiel, David Pazzaglia, Gary Greenwood Computer Science Department Indiana University of Pennsylvania Indiana Pa, USA ABSTRACT software and the sub modules are integrated reliably. The scope of our system is not only automatic fault detection In this paper, we propose a diagnostic model to and isolation, but it also encompasses data storage for automatically detect and identify faults in manufacturing further research and development analysis. The data processes by using a wavelet-based method. The idea stored is composed of a number of elements. These behind our method is to use an image processing system elements include the following: the image itself, the that performs the following phases: image capturing, physical characteristics, image faults, and the image image preprocessing, determination of region of interest, analysis results. Our results are based on thresholding object segmentation, computations of object features and functions. We use a threshold value predefined by the decision-making. For the above phases, we use a bank of manufacturer of the product. All of these parameters can filters, statistical, morphological, and wavelet operations. be easily modified by the graphical user interface (GUI). Developed in this paper is a method that automatically Modern processing plants are very complex and consist of detects and isolates faults in manufacturing products by a large number of parameters. These can be implemented dividing our system into three sub modules. These sub in our GUI, which is portable and adapts easily. In this modules are the sensor, computer, and logistical interface paper, we use wavelet, statistical, and morphological modules that are straightforward to analyze. We have methods for automatic detection and isolations because it focused only on the design and object features. We is simple, effective, and it can be implemented in demonstrate our method for various product images and embedded systems. This method seems to be well suited extract characters, numbers, and object features such as for a wide variety of products. The paper is organized as area, major/minor axis length, orientation, diameter, follows. In section II, the mathematical basics of convex area, Euler number and centroid. The availability wavelets, thresholding, and morphological operators are of this system may significantly impact the quality control discussed. In section III, the system design and the process of the manufacturing sector. The underlying functionality of its modules are described. The algorithms and system architecture are described, as well experimental results are then described in section IV. as the hardware and software aspect of the Finally, in section V we summarize and conclude our implementation. work. KEY WORDS 2. MATHEMATICAL BASICS

Wavelet, fault detection, identification, isolation, Wavelets are presently used in many disciplines of logistical interface, morphological operations science and engineering. In this section, we present the concept of wavelets, thresholding, and morphological 1. INTRODUCTION operations. The development of wavelets resulted from the need to generate algorithms that would compute Over the last two decades, the natural quality control compact representations of functions and data sets at an process of manufacturing has undergone many accelerated pace. In the last few years, the wavelet technological advances. The nature of diagnosis, in transform has become a cutting edge technology in the general, has been done by visual inspection. Due to the image-processing field. complexity of the problem the demand on the workforce has increased tremendously. At the same time the product Wavelets quality assurance has been reduced, while the cost of goods sold have risen. As a direct result of this, supply Jean Morlet and Alex Grossmann introduced the concept and demand are not in equilibrium. Image processing can of wavelets. It was mainly developed by Y. Meyer [6]. provide tools to solve this problem. A well-designed Stephen Mallet developed the first algorithm in 1988 [5]. image processing system will increase the product quality After that, many scientists like Ingrid Daubechies [4] and assurance and lower production costs. In this paper, we Ronald Coifmen contributed to this field. A wavelet is a propose a reliable robust system for automatic fault waveform of effectively limited duration that has an detection and identification. The system is further divided average value of zero. So, wavelet analysis is done by into sub modules depending on their task. The required breaking up a signal into shifted and scaled versions of the original (mother) wavelet. From this observation, we see the threshold value is zero, but in practical this is not can define a continuous wavelet transform as the sum the case. Typically one parameter is measured and quality over all time of the signal multiplied by a scaled and control is based on this parameter that may lead to shifted version of the wavelet function Y i.e. undetected defects in other parameters. To avoid such + problems, it is necessary to check all possible parameters. C( scale , position ) f ( t )Y ( scale , position ) Since we are using the wavelet, morphological, and statistical methods, the system is able to provide in-depth - where scaling means stretching (or compressing) and analysis. Based upon this analysis, faults can be position means shifting the wavelet[13]. effectively detected. In this section, the design and implementation of our image processing system is Thresholding discussed. The system is divided into three sub modules: sensors, computers, and logistical interfaces. Figure 1 Thresholding is the transformation of an input image I to shows the diagram of our image processing system. an output binary image BI as follows:

1 for I(i,j) T  Camera Frame Computer BI( i , j )    Grabber 0 for I(i,j)

Morphological operations [9] can be used to construct d Conveyer Belt o spatial filters in image enhancement[12]. The basic o G operators such as dilation, erosion, opening and closing are defined, but many others exist. Let f( x , y ) and

B a b( x , y ) be input image and structured element, Logistical d respectively[9][10]. Controller Definition: The dilation of f by b , denoted by f b , Figure 1: Image Processing System is defined as (f� b )( s , t )- max{ f ( s - x , t + y ) b ( x , y ) Sensor Module

| (s- x ),( t - y )挝 Df ;( x , y ) D b } This model consists of the charge coupled device (CCD) image sensors, lenses, driver control circuits or high where Df and D are the domains of f and b . b quality cameras and illumination setups. Since the Definition: The erosion fe b is defined as invention of CCD in 1970 by W. Boyle and G. Smith at Bell Labs [1] there has been significant technological (fe b )( s , t )= min{ f ( s + x , t + y ) - b ( x , y ) progress in CCD regarding the design of digital sensors versus analog sensors[3]. The CCDs are an excellent | (s+ x ),( t + y )挝 Df ;( x , y ) D b } imaging device but not are very flexible and lack desirable features [2][8]. A camera, depending on the where D and D are the domains of f and b . f b application, can easily replace the CCD[15]. Standard cameras (still/video) produce analog signals, which can Definition: The opening of image f by sub image b connect to the computer via frame grabber[14]. These denoted by f° b= ( f b ) b . The closing of image mostly rely on the hardware functionality increasing e maintenance requirements, hardware must be f by b , is f� b( f b ) e b continuously upgraded and there is a possibility of signal loss or noise additions. Modern cameras (digital) produce 3. SYSTEM DESIGN digital signals, which can be directly connected to the computer system. These rely on software and can be A variety of methods are widely used for automatic remotely transmitted across the network. When the detection. Most methods of fault detection rely on a single manufactured product is complete, the product is put on a statistical parameter thresholding[7]. Thresholding conveyer belt that brings the product to a stop on a sensor. represents the difference between the calculated value and This sensor then relays a message to a set of cameras the expected value. For quality purpose we would like to telling them that the product is ready for inspection. The cameras or CCD proceed to take an exact picture of the memory of the frame grabber causing transfer speeds to product. The picture is then sent to the computer for be limited to the speed of a PCI burst. analysis. Logical Interface Module Computer Module The logistic interface receives a message from the The computer module determines if the picture sent to it computer containing a number or parameters. These is an analog or digital picture. If the picture is analog, the parameters include when the product will reach the frame grabber will convert it to a digital image. If the control arm, whether the product matches the criteria, and picture is digital, it will bypass the frame grabber and the what to do with the product. Based on the parameters the analysis process will begin. The analysis process control arm will take action and accept or deny the determines if the image of the product matches the product when the time is right predefined criteria. The computer will then store the 4. EXPERIMENTAL RESULTS image and its individual characteristics in some type of external storage, perhaps a RAID server. Now, based on To illustrate our proposed image processing the analysis process, the computer will send a signal to system for automatic fault detection and isolation the logistical controller. The basic operations of a frame we have applied it to a set of product images. grabber vary because of the wide range of programmatic Then we extracted various objects from the controls. These controls include support for various field region of interest and calculated various scan modes, pixel synchronous sampling, external parameters such as area, major/minor axis triggering, etc. However, in general, taking an analog length, orientation, diameter, convex area, Euler video signal from a camera and assembling it into a number and centroid. Figures 2, 3, and 4 show an digital image is the basic operation of the frame image of a gear enhanced with the various filters, grabber[14]. In the majority of frame grabbers there are the edges, and segmented objects for two main modes of image processing: acquisition mode computation of object features and live mode. Both modes start by taking a sample from the incoming video signal based on an interval given by a pixel clock. An A/D converter takes the images voltage values and converts them into a numerical. It then stores this value until it completes the image. In acquisition mode, a vertical synchronization signal tells the frame grabber to digitize the incoming image and store the digitized image until the next signal is received. Now a complete image will be stored in the buffer area that can be retrieved by the image processing software. In the other mode, live mode, the frame grabber performs the above processes with a minor twist. The frame grabber constantly overwrites the current image in the buffer thus giving a live image. Because frame grabbers use a buffer to store a temporary image, frame grabbers can be characterized into two different types: FIFO and frame memory boards. A FIFO frame grabber usually uses a four-kilobyte buffer that has to be emptied every 68 to 272 µs for a typical pixel clock of 15 MHz. However, the buffer usually is emptied when it is half full. The image Figure 2: Original and enhanced image data is then transferred to the computer’s main memory by direct memory access. This transfer takes approximately five µs for ninety megabytes. A frame memory board already has sufficient energy for at least one full frame. This creates a problem though. It has trouble overwriting data because it has to overwrite a complete frame at a time. This causes data transfer to be too slow and thus it cannot occur. This means that the images must be transferred to the computer’s main memory as well because the CPU has no access to the Figure 5: Original image and Figure 3: Edges and gear segmentation

Figure 6: Object perimeters

Figures 7, 8, and 9 show an image of a bolt and various washers enhanced with the various filters, the edges, and segmented objects for computation of object features

Figure 4: Various segmentations

Figures 5 and 6 show an image of a bolt and three washers enhanced with the various filters, the edges, and segmented objects for computation of object features

Figure 7: Original and enhanced image Figure 12: Code 128 barcode with noise added

Figure 8: Object perimeters

Figure 9: Object perimeters Figure 13: Extracted and matching characters

Figures 10, and 11 show an image of a locking Figures 14 and 15 show images of a code 39 washer enhanced with the various filters, the barcode with noise added, and the extracted edges, and segmented objects for computation of characters with the corresponding matching object features characters.

Figure 14: Code 39 barcode with noise added

Figure 10: Original and enhanced washer

Figure 15: Extracted and matching characters

Figure 11: Object perimeters 5. CONCLUSION

Figures 12 and 13 show images of a code 128 Using the system described in this paper, we barcode with noise added, and the extracted have been able to automatically detect and characters with the corresponding matching identify faults in manufacturing processes by characters. using wavelet, morphological, and thresholding operations. Our experimental results have demonstrated that our algorithm is effective for image capturing, image preprocessing, [15] Gupta, R., Hartley, R.I., Linear Pushbroom determination of region of interest, object Cameras, segmentation, computations of object features PAMI (19), No. 9, September 1997, pp. 963-975. and decision-making. Although the system has not been fully implemented, a foundation for an automatic image processing system for fault detection and isolation has been set forth. Our system can be applied to a variety of manufacturing processes. However, further experimental analysis needs to be carried out for different manufacturing processes in order to adapt to the vast range of manufactured products. REFERENCES

[1] W. Boyle, G. Smith Charged Coupled Semiconductor Devices Bell System Tech.(49), 1970, pp. 587-593. [2] J. Janesick, T. Elliott, S. Collins, M. Blouke, J. Freeman, Scientific Charge-Coupled Devices, OptEng(26), 1987, pp. 692-714. [3] M. Subbarao, M.C. Lu, Image-Sensing Model and Computer-Simulation for CCD Camera Systems, MVA(7), No. 4, 1994, pp. 277-289. [4] I. Daubechies. Ten Lectures on Wavelets, volume 61 of CBMS-NSF conference series in applied mathematics. SIAM, Philadelphia, 1992. [5] S. Mallat. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Pattern Anal. And Machine Intell., 11:674-693, 1989. [6] Y. Meyer. Ondelettes et operateurs. Tome 1, Hermann Ed., 1990. [7] P.D. Carman, W.N. Charman, Detection, Recognition, and Resolution in Photographic Systems,JOSA(54), September 1964, pp. 1121-1130. [8] G. Amelio, M. Tompsett, G. Smith, Experimental Verification of the Charge Coupled Device Concept, Bell System Tech.(49), 1970, pp. 593-600. [9] J. Serra (ed.) Image Analysis and Mathematical Morphology, vol. 2, Academic Press, New York, 1998 [10] R. C. Gonzalez, R. E. Woods. Digital Image Processing, 2nd ed., Prentice Hall, New Jersey, 2002 [11] R.E. Flory, Image Acquisition Technology, PIEEE (73), 1985, pp. 613-637. [12] J.C. Russ. The Image Processing Handbook, 3rd ed., CRC Press Boca Raton, Fla. , 1999 [13] G. Strang,, T. Nguyen. Wavelets and Filter Banks, Wellesley-Cambridge Press Wellesley, Mass. , 1996 [14] Horner, J.L., (Ed.) Special Section on Optical Devices and Computing, PIEEE(77), No. 10, October 1989, pp. 1511-1583

Load more

  6

Copy Link