توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱A GA–Based Optimized Fault Identification System Using Neural Networks
نویسنده(ها): ، ، ،
اطلاعات انتشار: اولین کنفرانس بین المللی و هفتمین کنفرانس ملی مهندسی ساخت و تولید، سال
تعداد صفحات: ۹
This paper presents an optimized gear fault identification system using Genetic Algorithm (GA) to investigate the type of gear failure of a gearbox system using Artificial Neural Networks (ANN) with a well–designed structure suited for practical implementations due to its short training duration and high accuracy. Slight–worn, medium–worn, and broken–teeth of gears are categorized as gear faults. Wavelet analysis which is implemented for non–stationary signals, is capable of providing both time–domain and frequency–domain information simultaneously and therefore recognized in this research as the most reliable signal analysis method to extract a feature vector to train ANN using normalized wavelet packet energy rate index of the vibration signal. GA was exploited to settle on an optimized system by determination of best values for wavelet function type, decomposition level and number of neurons of hidden layer leading to a high–speed, meticulous two–layer ANN with a particularly small size.<\div>
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