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Mónica Chamay,오세도,김영진 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.9
Diverse techniques have been developed for dimension reduction, especially to facilitate the implementation of artificial neural networks(ANNs). For ANNs, the training process can become very complex and demand a great deal of hardware resources, making priordimension reduction very important; accordingly, this research proposes a new algorithm to increase the degree of dimension reduction. A new procedure is applied to extract important meaningful non-parametric characteristics from the data. The data in this research wasobtained from accelerometers installed in a wind power machine and processed using a linear predictive coefficient/cepstrum coefficientsprocedure. The procedure consists of the extraction of linear predictive coefficients from the signal data, and subsequent extraction of sixfeatures from those coefficients, thereby reducing the amount of data to process and enabling processing of that information using neuralnetworks. The features employed were selected carefully based on the error obtained from a neural network implementation. As a resultof the implementation was shown to reduce the data to only six input variables for the ANN, thereby enabling the ANN to achieve a verylow rate of classification error and training time consuming.
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