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- W4386232275 abstract "With the development of hardware and software, the pressure control of construction machinery embraced new characteristics of sensors failure. In recent years, soft sensors have been widely used in construction machinery for fault-tolerant controlling. To cope with failure of some pressure sensors which cause great harm to the electro-hydraulic proportional control of excavators and accurately measure hydraulic pressure without actual pressure sensors, a pressure soft sensor based on Long Short-Term Memory (LSTM) neural network is proposed in this work. And the Bayesian Optimization (BO) is used to tune the hyperparameters of LSTM. The hydraulic system of a SY375IDS excavator is analyzed and the Mutual Information (MI) strategy is adopted to determine features which are given to LSTM. For case of multiple sensors fail at the same time, a (multiple input multiple output) MIMO prediction mechanism based on BO-LSTM and a MIMO cyclic prediction mechanism based on two (multiple input single output) MISO models are proposed. Finally, the performance of the proposed pressure soft sensor is evaluated in the compared experiments with BP, CNN and RnN. Results shows that the proposed method is superior to other methods in terms of pressure prediction." @default.
- W4386232275 created "2023-08-29" @default.
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- W4386232275 date "2023-01-01" @default.
- W4386232275 modified "2023-09-27" @default.
- W4386232275 title "A data-driven LSTM soft sensor model based on bayesian optimization for hydraulic pressure measurement of excavator" @default.
- W4386232275 doi "https://doi.org/10.1109/jsen.2023.3304701" @default.
- W4386232275 hasPublicationYear "2023" @default.
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