Der Nutzen einer Fahr- und Arbeitszustandserkennung am Beispiel eines Radladers

Brinkschulte, Lars; Daiß, Kevin; Geimer, Marcus

doi:10.51202/9783181023327-41

Cited by 2 publications

(1 citation statement)

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“…In his research, he found out neural networks might work for some simple cases [4]. Another research is from Brinkschulte who points out that the prediction accuracy with bagged trees may dramatically decrease when the drivers have different driving skills [5]. Besides machine learning algorithm, research by Nilsson introduced a method that combined several individually simple techniques including signal processing, state automation techniques, and parameter estimation algorithms.…”

Section: Working Process Detection Algorithmsmentioning

confidence: 99%

Optimization of operation strategy for primary torque based hydrostatics drivetrain using artificial intelligence

Xiang¹,

Geimer²

2020

Volume 1 - Symposium

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A new primary torque control concept for hydrostatics mobile machines was introduced in 2018 [1]. The mentioned concept controls the pressure in a closed circuit by changing the angle of the hydraulic pump to achieve the desired pressure based on a feedback system. Thanks to this concept, a series of advantages are expected [2]. However, while working in a Y cycle, the primary torque controlled wheel loader has worse performance in efficiency compared to secondary controlled earthmover due to lack of recuperation ability. Alternatively, we use deep learning algorithms to improve machines regeneration performance. In this paper, we firstly make a potential analysis to show the benefit by utilizing the regeneration process, followed by proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely detect Y cycles. Compared to existing algorithms, the CRDNN with bi-directional LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable performance but much fewer training parameters. Based on our dataset including 119 truck loading cycles, our best neural network shows a 98.2% test accuracy. Therefore, even with a simple regeneration process, our algorithm can improve the holistic efficiency of mobile machines up to 9% during Y cycle processes if primary torque concept is used.

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Section: Working Process Detection Algorithmsmentioning

confidence: 99%