AUTOMATIC FAULT DETECTION BY USING WAVELET METHOD

1. AUTOMATIC FAULT DETECTION BY USING WAVELET METHOD Soundararajan Ezekiel, Gary Greenwood, David Pazzaglia Computer Science Department Indiana University of Pennsylvania Indiana, PA, USA

2. we propose a diagnostic model to automatically detect and identify faults in manufacturing processes by using a wavelet-based method. • The idea behind our method is to use an image processing system that performs the following phases: • image capturing, • image preprocessing, • determination of region of interest, • object segmentation, • computations of object features and • decision-making. • For the above phases, we use a bank of filters, statistical, morphological, and wavelet operations. • Developed in this paper is a method that automatically detects and isolates faults in manufacturing products by dividing our system into three sub modules. • These sub modules are the sensor, computer, and logistical interface modules that are straightforward to analyze. ABSTRACT

3. Continue • We have focused only on the design and object features. • We demonstrate our method for various product images and extract • characters, • numbers, • and object features such as area, major/minor axis length, orientation, diameter, convex area, • Euler number and centroid. • The availability of this system may significantly impact the quality control process of the manufacturing sector. • The underlying algorithms and system architecture are described, as well as the hardware and software aspect of the implementation.

4. Introduction • Over the last two decades, the natural quality control process of manufacturing has undergone many technological advances. • The nature of diagnosis, in general, has been done by visual inspection. • Due to the complexity of the problem the demand on the workforce has increased tremendously. • At the same time the product quality assurance has been reduced, while the cost of goods sold have risen. • As a direct result of this, supply and demand are not in equilibrium. • Image processing can provide tools to solve this problem.

5. Introduction -- Continue • A well-designed image processing system will increase the product quality assurance and lower production costs. • In this paper, we propose a reliable robust system for automatic fault detection and identification. • The system is further divided into sub modules depending on their task. • The required software and the sub modules are integrated reliably. • The scope of our system is not only automatic fault detection and isolation, but it also encompasses data storage for further research and development analysis.

6. Introduction --- continue • The data stored is composed of a number of elements. • These elements include the following: the image itself, the physical characteristics, image faults, and the image analysis results. • Our results are based on thresholding functions. We use a threshold value predefined by the manufacturer of the product. • All of these parameters can be easily modified by the graphical user interface (GUI). • Modern processing plants are very complex and consist of a large number of parameters. • These can be implemented in our GUI, which is portable and adapts easily.

7. Introduction … Continue • In this paper, we use wavelet, • statistical, • and morphological methods for automatic detection and isolations • it is simple, effective, and it can be implemented in embedded systems. • This method seems to be well suited for a wide variety of products.

8. Wavelet • A wavelet is a waveform of effectively limited duration that has an average value of zero. • So, wavelet analysis is done by breaking up a signal into shifted and scaled versions of the original (mother) wavelet. • From this observation, we can define a continuous wavelet transform as the sum over all time of the signal multiplied by a scaled and shifted version of the wavelet function i.e. • where scaling means stretching (or compressing) and position means shifting the wavelet.

9. Basics Thresholding is the transformation of an input image I to an output binary image BI as follows: where T is the threshold. Morphological Operations Morphological operations can be used to construct spatial filters in image enhancement . The basic operators such as dilation, erosion, opening and closing are defined, but many others exist. Let and be input image and structured element, respectively .

11. SYSTEM DESIGN • A variety of methods are widely used for automatic detection. • Most methods of fault detection rely on a single statistical parameter thresholding . • Thresholding represents the difference between the calculated value and the expected value. • For quality purpose we would like to see the threshold value is zero, but in practical this is not the case. • Typically one parameter is measured and quality control is based on this parameter that may lead to undetected defects in other parameters. To avoid such problems, it is necessary to check all possible parameters. • Since we are using the wavelet, morphological, and statistical methods, the system is able to provide in-depth analysis. • Based upon this analysis, faults can be effectively detected.

12. Image Processing System

13. Continue • The system is divided into three sub modules: • sensors, • computers, and • logistical interfaces. Sensor Module This model consists of the charge coupled device (CCD) image sensors, lenses, driver control circuits or high quality cameras and illumination setups.

14. Continue Computer Module • The computer module determines if the picture sent to it is an analog or digital picture. If the picture is analog, the frame grabber will convert it to a digital image. If the picture is digital, it will bypass the frame grabber and the analysis process will begin. The analysis process determines if the image of the product matches the predefined criteria. Logical Interface Module • The logistic interface receives a message from the computer containing a number or parameters. These parameters include when the product will reach the control arm, whether the product matches the criteria, and what to do with the product. Based on the parameters the control arm will take action and accept or deny the product when the time is right

15. RESULTS Original and enhanced image Edges and gear segmentation

16. Various segmentations Original image and Object perimeters

17. Original and enhanced image Object perimeters

18. Original and enhanced washer Object perimeters

19. barcodes with noise added Extracted and matching characters

20. Conclusion • Using the system described above, we have been able to automatically detect and identify faults in manufacturing processes by using wavelet, morphological, and thresholding operations. • Our experimental results have demonstrated that our algorithm is effective for image capturing, image preprocessing, determination of region of interest, object segmentation, computations of object features 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. • More information – check out website http://www.cosc.iup.edu/sezekiel Contact: sezekiel@iup.edu