论文标题
使用人工智能优化基于初级扭矩的基于原始扭矩的静水压动力传动系统
Optimization of Operation Strategy for Primary Torque based hydrostatic Drivetrain using Artificial Intelligence
论文作者
论文摘要
2018年引入了一种新的用于静液压移动机的主要扭矩控制概念。提到的概念通过更改液压泵的角度来控制闭合电路中的压力，以实现基于反馈系统的所需压力。得益于这个概念，预计将有一系列优势。但是，在Y周期中工作时，由于缺乏恢复能力，与次级控制的EarthMover相比，主要扭矩控制的车轮装载机的效率性能较差。另外，我们使用深度学习算法来改善机器的再生性能。在本文中，我们首先进行了潜在的分析，以通过利用再生过程来证明益处，然后提出一系列CRDNN，这些CRDNN结合了CNN，RNN和DNN，以精确检测Y周期。与现有算法相比，具有双向LSTM的CRDNN具有最佳的精度，并且具有LSTMS的CRDNN具有可比的性能，但训练参数却少得多。根据我们的数据集，包括119个卡车加载周期，我们最好的神经网络显示出98.2％的测试精度。因此，即使使用简单的再生过程，如果使用主要的扭矩概念，我们的算法也可以提高Y周期过程中最高9％的移动机器的整体效率。
A new primary torque control concept for hydrostatics mobile machines was introduced in 2018. 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. 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.
