Vibration Signal Prediction of Gearbox in High-Speed Train Based on Monitoring Data
Vibration Signal Prediction of Gearbox in High-Speed Train Based on Monitoring Data
复制标题
基于监测数据的高速列车齿轮箱振动信号预测
DOI：
10.1109/access.2018.2868197
复制
发表时间：
2018-01-01
期刊：
影响因子：
3.9
通讯作者：
Zhuang, Jiaojiao
中科院分区：
文献类型：
--
作者：
Liu, Yumei;Qiao, Ningguo;Zhuang, Jiaojiao
振动信号包含丰富的信息，可以反映高速列车的运行状态。准确的振动信号预测可以为高速列车齿轮箱的异常检测提供参考。本文建立了一个集成经验模态分解（EEMD）与自回归（AR）和支持向量回归（SVR）模型相结合的混合模型。首先，采用EEMD方法对齿轮箱振动加速度信号进行分解。其次，利用AR模型对固有模态函数进行预测，并将输出汇总为AR的最终结果。第三，重构相空间，建立SVR模型对各分量进行预测；预测结果汇总为SVR的最终结果。最后，对AR和SVR模型的预测结果进行加权求和，并用混沌粒子群优化算法对权重进行优化。用实际运行监测数据对混合模型进行了验证。数据分析表明，与AR模型、SVR模型和RBF神经网络模型相比，该方法具有更好的逼近性。
Vibration signals contain abundant information which can reflect the running state of high-speed trains. Accurate vibration signal prediction can provide references for anomaly detection of the gearbox in high-speed trains. This paper develops a hybrid model combining ensemble empirical mode decomposition (EEMD) with auto regression (AR) and support vector regression (SVR) models. First, the EEMD method is applied to decompose the vibration acceleration signal of gearbox. Second, AR models are employed to predict the intrinsic mode functions and the outputs are aggregated as the final result of AR. Third, reconstruct phase space and establish SVR models to predict the components; The predictions are aggregated as the final result of SVR. Finally, the results predicted using the AR and SVR models are weighted and summed together, with the weights being optimized by the chaotic particle swarm optimization algorithm. The actual operation monitoring data are used to validate the hybrid model. Data analysis demonstrates that the proposed method has better approximation compared with the AR model, the SVR model and the RBF neural network model.