Health Assessment for Crane Pumps based on Vehicle Tests using Deep Autoencoder and Metric Learning

Dengyu Xiao, Yixiang Huang, Chengjin Qin, Haotian Shi, Chengliang Liu, Zenghai Shan
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Abstract

Pump is one of the key components in a crane, which once fails will severely hurt the reliability of the hydraulic system and cause great loss. Therefore, accurate, reliable and effective crane pump health assessment must be performed. However, the research about pump health assessment still stays at the stage of bench tests, which have the limited help for the real-world pump health prognosis. In this paper, to evaluate crane pump health status and avoid the issue above, the real-world vehicle tests of several cranes with different service years are performed to acquire the pump signals during the cranes’ actual operations. Deep Autoencoder (DAE), a kind of unsupervised learning approach, which possesses the capacity to learn meaningful representations from raw signal, is used reduce the data dimension before they are sent to Mahalanobis-Taguchi System in metric learning. Mahalanobis distance (MD) is utilized to reveal the performance degradation and assess the health condition. Performances of other feature learning methods such as statistical features, EMD, MLP, CNN are tested and contrasted. Results show that the proposed approach achieves the best performance.
基于深度自编码器和度量学习的车辆试验起重机泵健康评估
泵是起重机的关键部件之一,一旦发生故障,将严重影响液压系统的可靠性,造成巨大的损失。因此,必须对起重机泵进行准确、可靠、有效的健康评估。然而,目前对泵健康评价的研究还停留在台架试验阶段,对实际泵健康预测的帮助有限。为了评估起重机泵的健康状况,避免上述问题的发生,本文通过对多台不同服役年限的起重机进行实际车辆试验,获取起重机实际运行时的泵信号。深度自编码器(Deep Autoencoder, DAE)是一种无监督学习方法,具有从原始信号中学习有意义表示的能力,在度量学习中,将数据在发送到Mahalanobis-Taguchi系统之前进行降维。利用马氏距离(MD)来揭示性能退化和评估健康状况。对统计特征、EMD、MLP、CNN等其他特征学习方法的性能进行了测试和对比。结果表明,该方法达到了最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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