• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 추계학술대회 논문집 Vol.15
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• pp.317-321
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• 2002

Piezoelectric properties of $(Pb_{1-x}Y_x)[(Ni_{1/3}Nb_{2/3})_{0.15}(Zr_{1/2}Ti_{1/2)})_{0.85}]O_{3}$ (x=0~0.05) ceramics were investigated, The stoichiometric PNN-PZT ceramics required the sintering temperature above $1100^{\circ}C$, but the addition of $Y_{2}O_{3}$ in the PNN-PZT ceramic lowered the sintering temperature down to $1000^{\circ}C$. In case of x=0.005, the electro-mechanical coupling $factor(K_p)$, the piezoelectric $constant(d_{33})$, and the maximum strain ratio of PNN-PZT ceramics sintered at $1000^{\circ}C$ were 53.1%, 395pC/N, and $2200{\times}10^{-6}$ respectively, A 30-layer piezoelectric actuator$(10{\times}10{\times}1.7mm)$ fabricated with the above material showed the maximum strain of $2.09{\mu}m$ under 100V DC bias.

• 한국해양공학회지
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• 제35권4호
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.