Optimization of Operation Strategy for Primary Torque based hydrostatic Drivetrain using Artificial Intelligence

Xiang, Yusheng; Geimer, Marcus

doi:10.48550/arxiv.2003.10011

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Additionally, the excessive rotary speed of the excavator body will cause an increase in the excavator power consumption, drive power, cost of extraction, and mechanical strength required. Regarding the use of artificial intelligence-based methods for the performance optimization of machines in general, Xiang et al [15] used deep learning algorithms to enhance the performance of machine regeneration, by invoking deep neural networks, convolutional neural networks, and recurrent neural networks. Also, Xiang et al [16] apply the same approach to the on-site training of mobile construction machines.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters

Kostić,

Stojković,

Ilić

et al. 2023

Processes

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In the present paper, we develop a new model, based on the implementation of deep neural networks, for the estimation of a series of excavation parameters, depending on the main environmental and excavation properties. The developed model, with high statistical accuracy (R > 0.79) and small RMSE (<17% of the actual output values), enables the simultaneous assessment of the following excavation parameters: effective capacity Qef, maximum current consumption Imax, maximum power consumption Nmax, maximum force consumption Pmax, maximum energy consumption Emax, and maximum linear and areal cutting resistance, KLmax and KFmax, respectively, based on the impact of the following environmental properties and excavation parameters: coal unit weight, coal compression strength, coal cohesion, friction angle, excavator movement angle in the left and right direction, slice height and thickness, and wheel velocity. The data analyzed in the present paper were previously collected from three neighboring open-pit coal mines in Serbia: Tamnava Western Field, Tamnava Eastern Field, and Field D. These mines have similar geological conditions and coal properties. Additionally, for each output factor, a complex analysis is provided on the impact of the examined input factors, by applying the multiple linear regression method. As far as we are aware, this is the first time such a comprehensive estimation model has been suggested for the operation of a bucket-wheel excavator in the Neogene coal basins. The deep neural network (DNN) model, trained over 300 epochs, shows an MSE range of 6.7–16.5% across various input factors, with distinct evaluations for Imax due to its unique values (4.8–12.5%).

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Section: Introductionmentioning

confidence: 99%

Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters

Kostić,

Stojković,

Ilić

et al. 2023

Processes

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“…The challenges of pump-controlled hydraulic systems, such as load-pressure variance for load-sensing hydraulics [5] and poor tracking for displacement control hydraulics, have been investigated [6]. A pump-controlled rotary hydraulic actuation system efficiency has been improved for the drivetrains of mobile platforms [7][8][9][10]. In this paper, we focus on a valvecontrolled linear hydraulic actuation system in Figure 1 with a single actuator because it represents a common system design found on mobile platforms with good tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Constrained Optimization of a Hydraulic Actuation System

Fox

Chong

2022

Energies

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Operators of mobile platforms that employ hydraulic actuation, such as excavators, seek more efficient power transfer from source to load. Pump-controlled architectures achieve greater efficiency than valve-controlled architectures but exhibit poor tracking performance. We present a system-design optimization technique that ensures compliance with design requirements and minimizes peak input power, which correlates inversely with efficiency. We utilize the optimization technique to size a valve-controlled hydraulically actuated stabilized mount on a mobile platform. Our optimization framework accounts for the disturbance spectrum, a stabilization performance measure, the system dynamics, and control system design. Our technique features automated requirement derivation in the form of a parameter estimation, which supports design decisions under constraints. Our results show that one of four inequality constraints is active. This constraint represents a common design rule and results in limiting efficiency. We show that relaxing this constraint is practically feasible and leads to higher efficiency in achieving the required performance. We propose adding an inerter to justify the relaxed constraint and present the resulting open-loop servo transfer function.

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Optimization of Operation Strategy for Primary Torque based hydrostatic Drivetrain using Artificial Intelligence

Cited by 2 publications

References 9 publications

Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters

Deep Neural Network Model for Determination of Coal Cutting Resistance and Performance of Bucket-Wheel Excavator Based on the Environmental Properties and Excavation Parameters

Constrained Optimization of a Hydraulic Actuation System

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