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Abstract:In this paper, a dynamic model is developed for high-voltage circuit breakers to extract fault features and causes. Lagrange's method is applied using geometric equations of mechanism components to write the model.Unlike previous approaches, the proposed method reveals and analyzes all types of circuit breaker operating mechanism faults. Early fault detection, which is a vital requirement of the fault diagnosis system, becomes feasible by keeping track of changes in the contact travel curve in the proposed model. Resulting faults in the travel curve are analyzed mathematically in order to find out the exact origin of the fault. Field test results show the accuracy and reliability of the fault detection method.

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SUMMARYIn this paper, a new skin detection method using pixel color and image regional information, intended for objectionable image filtering is proposed. The method consists of three stages: skin detection, feature extraction and image classification. Skin detection is implemented in two steps. First, a Sinc function, fitted to skin color distribution in the Cb-Cr chrominance plane is used for detecting pixels with skin color properties. Next, to benefit regional information, based on the theory of color image reproduction, it's shown that the scattering of skin pixels in the RGB color space can be approximated by an exponential function. This function is incorporated to extract the final accurate skin map of the image. As objectionable image features, new shape and direction features, along with area feature are extracted. Finally, a Multi-Layer Perceptron trained with the best set of input features is used for filtering images. Experimental results on a dataset of 1600 images illustrate that the regional method improves the pixel-based skin detection rate by 10%. The final classification result with 94.12% accuracy showed better results when compared to other methods.