首页> 外文期刊>International Journal of Industrial Ergonomics >Artificial intelligence models for predicting the performance of hydro-pneumatic suspension struts in large capacity dump trucks
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Artificial intelligence models for predicting the performance of hydro-pneumatic suspension struts in large capacity dump trucks

机译:预测大容量自卸卡车水电气动悬架支柱性能的人工智能模型

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Large dump trucks are being matched with large shovels to achieve bulk economic production in surface mining operations. This process results in high impact shovel loading operations (HISLO) and exposes operators to severe levels of whole-body vibrations (WBV). The performance of the hydro-pneumatic suspension struts, responsible for vibration attenuation in large dump trucks, decreases as a truck age. There is a need for a system for monitoring and predicting the performance of the suspension struts in real time. Artificial intelligence (AI) has been applied for modeling and predicting the suspension system performance for light/smaller vehicles. However, no work has been done to implement AI for modeling and predicting the performance of hydro-pneumatic struts in large dump trucks. This paper is a pioneering effort towards developing AI models for solving this problem. These AI models would incorporate the Artificial Neural Networks (ANN), Mamdani Fuzzy Logic (MFL) and a hybrid system, the Hybrid Neural Fuzzy Interference System (HyFIS), for achieving this goal. Experiments were conducted using a 3D virtual simulator for the CAT 793D in MSC.ADMAS. RMS accelerations in the vertical and horizontal directions at the operator seat were recorded as the two main outputs for the suspension system performance. Eighty percent (80%) of the total experimental data was used in training and developing the models and the remaining 20% for testing and validating the developed models. With an R2and RMSE of 0.98168505 and 0.00852251 for the training phase, respectively, and 0.9660429 and 0.0195620 for the testing phase, HyFIS model showed the best accuracy for predicting the hydro-pneumatic suspension struts performance for dump trucks. This is the first time that AI models have been developed for dump truck suspension system performance prediction. With the implementation of these models in the dump truck, maintenance personnel can monitor the performance of the suspension system in real-time and schedule proper maintenance and/or replacement. Implementation of such a system will improve the workplace safety, operator's health and the overall system efficiency.
机译:大型自卸卡车与大型铲斗相匹配,以实现表面采矿业务的散装经济生产。该过程导致高冲击铲装载操作(HIRSLO),使运营商暴露于严重的全身振动(WBV)。水力气动悬架支柱的性能,负责大型自卸卡车中的振动衰减,随着卡车时代减少。需要一种用于实时监视和预测悬架支柱的性能的系统。人工智能(AI)已被应用于建模和预测光/较小车辆的悬架系统性能。然而,没有完成任何工作来实施AI用于建模和预测大型自卸卡车中水气动支柱的性能。本文是开发旨在解决这个问题的AI模型的开拓性努力。这些AI模型将包含人工神经网络(ANN),Mamdani模糊逻辑(MFL)和混合系统,混合神经模糊干扰系统(HYFIS),用于实现这一目标。在MSC.ADMAS中使用3D虚拟模拟器进行实验,用于猫793D。操作座椅上的垂直和水平方向上的RMS加速度被记录为悬架系统性能的两个主要输出。八十(80%)总实验数据用于培训和开发模型,剩下的20%用于测试和验证开发的模型。对于训练期的R2和RMSE为0.98168505和0.00852251,分别为0.9660429和0.0195620,用于测试阶段,HyFIS模型显示了预测自卸卡车的水气动悬架支柱性能的最佳精度。这是首次开发了AI模型,用于转储卡车悬架系统性能预测。通过在转储卡车中实施这些模型,维护人员可以实时监控悬架系统的性能,并安排适当的维护和/或更换。这种系统的实施将提高工作场所安全,运营商的健康和整体系统效率。

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