摘要
现存的两种分别基于信号处理技术和大数据处理技术的滚动轴承故障诊断方法,存在着过度依赖信号处理、模型复杂、可解释性弱等特点。针对传统故障诊断技术的不足,本文将基于shapelets学习算法的时间序列分类方法引入故障诊断领域,通过动车组轮对台架滚振实验建立了动车组轴箱轴承故障的非平衡数据集,并基于Dropout思想对诊断模型进行了改进。实验结果表明,该方法在保证故障诊断精确度的同时,保留了shapelets作为"最具代表性的时间序列子序列"的强可解释性。同时,基于Dropout的模型改进提升了模型的泛化性能,在轴承故障数据的训练集和测试集上都取得了100%的诊断精度,证明了基于shapelets的改进学习算法是一种可行的应用于动车组轴箱轴承故障诊断的方法。
The two currently existing fault diagnosis methods for rolling bearings based on signal processing technology and big data processing technology have the disadvantages of over-reliance on signal processing, complicated model and weak interpretability. Aiming at the shortcomings of traditional fault diagnosis technologies, this paper introduces the time series classification method based on shapelets learning algorithm into the field of fault diagnosis, and establishes the unbalanced data set of the faults of the EMU axle box bearing through the EMU wheelset bench rolling vibration experiment. The diagnosis model is improved based on the idea of Dropout. Experiment results show that the method guarantees the accuracy of fault diagnosis while retaining the strong interpretability of the shapelets as "the most representative time series subsequences". At the same time, the improvement of the model based on Dropout improves the generalization performance of the model. The diagnostic accuracy of 100% on the training set and test set of bearing fault data was achieved, which proves that the improved learning algorithm based on shapelets is a feasible method applied to the fault diagnosis of axle box bearing of electric multiple unit.
作者
宋志坤
徐立成
胡晓依
任海星
李强
Song Zhikun;Xu Licheng;Hu Xiaoyi;Ren Haixing;Li Qiang(School of Mechanical,Electronic and Control Engineering,Beijing Jiatong University,Beijing 100044,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2021年第2期66-74,共9页
Chinese Journal of Scientific Instrument
