a b s t r a c tAccurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure.
45.0 (W) 2.90 (A) 2.68 (A) 16.8 (V) 908 x 400 (mm) 21.2Abstract: This paper presents an application of artificial neural network for the estimation of maximum power generation from the PV module. The output power from the PV module depends on the environmental factors such as irradiation, and cell temperature. For the operation planning of power systems, the prediction of the power generation is inevitable for the PV systems. For this purpose, irradiation, temperature, and wind velocity are utilized as the input information to the proposed neural network. The output is the predicted maximum power generation under the condition given by those environmental factors. Efficiency of the proposed estimation scheme is evaluated by using the actual data on daily, monthly, and yearly bases. The proposed method gives highly accurate prediction compared with the prediction by using the conventional multiple regression model.
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