基于ANFIS的外啮合齿轮泵寿命预测研究

Study on life prediction of outer-tooth gear pump based on adaptive network-based fuzzy inference system

从流量退化趋势的角度提出了基于自适应网络模糊推理系统的寿命预测方法。首先利用改进的集合经验模态分解(MEEMD)方法对加速退化试验的振动数据进行多尺度重构降噪,提取重构信号的峭度值、均方频率、小波包能量,与转矩、转速、压力信号作为齿轮泵性能退化特征;然后使用核主元分析方法(KPCA)进行多特征融合,进而实现外啮合齿轮泵退化评估指标的建立和分析;再利用其退化评估指标与流量信号作为输入量对自适应网络模糊推理系统模型(ANFIS)进行训练,得到的齿轮泵剩余寿命预测模型,为了进一步验证该算法的有效性将其与liner回归模型、三次指数预测模型算法进行了比较,最后基于蒙特卡罗样本扩充方法实现外啮合齿轮泵的可靠性评估。结果表明,该方法的结果与实际阈值的预测误差约为8%,能够对外啮合齿轮泵的寿命进行比较准确的评估。

From the perspective of flow degradation trend, a life prediction method based on Adaptive Network-based Fuzzy Inference System(ANFIS) is proposed. Firstly, the modified ensemble empirical mode decomposition(MEEMD) method is used to perform multi-scale reconstruction and noise reduction on the vibration data of accelerated degradation test. The kurtosis, mean-square frequency, wavelet packet energy of the reconstructed signal are extracted, which together with the signals of torque, rotation speed and pressure are used as the characteristics of performance degradation of outer-tooth gear pump. Then, kernel principal component analysis(KPCA) method is used to perform the multiple feature fusion. Furthermore, the establishment and analysis of the degradation evaluation indices of the outer-tooth gear pump are realized. The degradation evaluation indices and flow signals are used to train the ANFIS model, and the remaining life prediction model of the gear pump is obtained. In order to further verify the effectiveness of the algorithm, the gear pump remaining life prediction model is compared with liner regression model and cubic exponential prediction model. Finally, based on the Monte Carlo sample expansion method, the reliability evaluation of the outer-tooth gear pump is achieved. The results show that the prediction error between the result of the proposed method and the actual threshold is about 8%, the proposed method can accurately evaluate the life of the outer-tooth gear pumps.