Problems of Tribology = Проблеми трибології - 2014 рік

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https://elar.khmnu.edu.ua/handle/123456789/2595

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Перегляд Problems of Tribology = Проблеми трибології - 2014 рік за Ключові слова "539.375.6+539.538+519.237.8"

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  • Ескіз
    Документ
    Accuracy improvement in wear state discontinuous tracking model regarding statistical data inaccuracies and shifts with boosting miniensemble of two-layer perceptrons
    (Khmelnitskiy National University, 2014) Romanuke, V.V.; Романюк, В.В.
    There is presented a method of improving accuracy in tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is presumed to be wholly sampled into those factors. The tracker is a static model based on boosting mini-ensemble of three twolayer perceptrons with nonlinear transfer functions. It regards statistical data inaccuracies and shifts. For making the ensemble, the AdaBoost technique is used. A distinction of the presented method of boosting from the AdaBoost is in the rule for finding the decreasing coefficient in order to re-distribute weights over training samples. Another one is that the ensemble is aggregated at once. The averaged gain of the boosting mini-ensemble in tracking 24 wear states with 16 influencing factors exceeds 50 %. The wear state tracking model is going to be perfected on optimizing two parameters of the training set and the naive rule for finding the decreasing coefficient before re-distributing training samples’ weights.
  • Ескіз
    Документ
    Дискретна модель відслідковування стану зносу на основі двошарового персептрону з нелінійними передавальними функціями, що навчається на розширеній генеральній сукупності з урахуван- ням похибок і зсувів у статистичних даних.
    (Хмельницький національний університет, 2014) Романюк, В.В.; Romanuke, V.V.
    There is presented a framework for tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is presumed to be wholly sampled into those factors. The tracker is two-layer perceptron with nonlinear transfer functions. It is a static model, unlike evolutionary dynamic models of forecasting wear. Its identification starts with forming the initial finite general totality containing correspondence between influencing factors and each known wear state. Two-layer perceptron is then trained on an extended general totality, whose elements are sum of pure representatives and normal variates’ values in two terms. The first term models jitter inaccuracies and omissions in statistical data or measurements. The second term models possible shifts of wear influencing factors’ values in every state. The identification final stage is the input of two-layer perceptron is re-fed with the pure representatives for making sure that they have not been disassociated from the initially given wear states. It is said also about liable and easy realizability of the tracking model. When range of wear states embraces all practiced wears, the presented two-layer perceptron tracker will control metal tool object wear states with minimized error, ensuring negligibility of underuse or overuse of materials.