基于双自适应滑动时间窗滚动轴承故障预测模型  

A Fault Prediction Model for Rolling Bearing Based on Double Adaptive Sliding Time Window

作　　者：郭基联[1] 张保山 周章文[1] 李波 刘晓欣 GUO Jilian;ZHANG Baoshan;ZHOU Zhangwen;LI Bo;LIU Xiaoxin(Aviation Engineering School,Air Force Engineering University,Xi’an 710038,China;Unit 91504,Taizhou 318050,Zhejiang,China;Unit 93786,Zhangjiakou 075000,Hebei,China)

机构地区：[1]空军工程大学航空工程学院,西安710038 [2]91504部队,浙江台州318050 [3]93786部队,河北张家口075000

出　　处：《空军工程大学学报》2023年第4期1-7,共7页Journal of Air Force Engineering University

摘　　要：针对传统方法和基于神经网络方法在滚动轴承故障预测中存在的问题,提出一种双自适应滑动时间窗故障预测模型。首先,通过设置能够去除相关性的状态估计非线性算子,将滚动轴承振动信号映射为能够表征其退化状态的故障特征—故障程度指标DR。其次,以损失函数为判据,设置模型参数自适应更新机制,以及建立能够自适应选取数据长度的滑动时间窗口。最后,通过西安交通大学发布的滚动轴承全寿命周期数据,模拟实际中突发性故障和渐发性故障综合作用下的故障发生情况,验证了所提出的故障预测模型的有效性。实验结果表明,提出的预测模型能够准确判断滚动轴承退化阶段的开始时刻和故障时刻,真实反映滚动轴承性能退化的趋势,预测误差仅为0.068%,预测时间仅占2次故障间隔时间的1.385%,满足复杂工况下滚动轴承故障预测的需求。Traditional and neural network-based methods being in existence of rolling bearing fault prediction,a dual adaptive sliding time window fault prediction model is proposed.Firstly,the rolling bearing vibration signal is mapped into fault features characterized as its degradation state by setting up a state estimation non-linear operator capable of removing correlations.Secondly,taking a loss function as a criterion,an adaptive update mechanism for the model parameters is set up,and a sliding time window capable of adaptively selecting the data length is constructed.Finally,the validity of the proposed failure prediction model is verified by simulating the occurrence of failures under the combined sudden and gradual failures in practice using the whole life cycle data of rolling bearings released by Xi’an Jiaotong University.The experimental results show that the prediction model proposed can accurately identify at the beginning moment and at the failure moment for the rolling bearing at the degradation stage,and truly reflect the trend of equipment performance degradation.The prediction error is only 0.068% and the prediction time is only 1.385% of the interval between failures,meeting the needs of rolling bearing failure prediction under condition of complex operation.

关 键 词：滚动轴承 故障预测 滑动时间窗 自适应 

分 类 号：V263[航空宇航科学与技术—航空宇航制造工程]