Automatic Fault Detection using Wavelet Method

S. Ezekiel, D. Pazzalia, and G. Greenwood (USA)


Wavelet, fault detection, identification, isolation, logistical interface, morphological operations


In this paper, 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. 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.

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