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.

Due to the low efficiency of internal combustion engine (ICE) and throttling losses of load-sensing (LS) system, conventional excavators which employed ICE with LS system as power source have high energy waste. To avoid this problem, a distributed independent electro-hydraulic actuator (iEHA) that directly driven by a electric motor is designed for excavators. And a speed control strategy based on mode switching and pump map is proposed. The results of proposed control strategy are carried out to verify the velocity stability. Besides, different signal delays for switching valve are explored to obtain better control characteristics. Furthermore, the influence of different precharged pressure of accumulator is explored when boom and arm share an accumulator. Results demonstrate that the energy efficiency of iEHA system improves from 19.19% to 59.56% compared to load-sensing system, the comprehensive response performance is better when signal delay time is 0.1s and response speed of system increases by 16.6% compared to no delay, the system can achieve good results when the precharged pressure is set to 20 bar.