Remaining life prediction of rolling bearings based on an EM-IKF collaborative algorithm
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摘要:
针对滚动轴承性能退化过程具有平稳和退化两阶段的特点，提出基于EM-IKF（expectation maximization-incremental Kalman filter）协同算法的滚动轴承剩余寿命预测方法。对于平稳阶段，利用西沃兹信息准则（SIC）进行轴承健康状态变点识别，确定轴承的初始退化点；对于退化阶段，建立基于Wiener过程的性能退化表征模型。为了克服传统卡尔曼滤波方法忽略相邻时刻参数的波动性问题，建立基于增量卡尔曼滤波（IKF）算法的状态空间方程；同时为了充分开发利用历史数据和在线监测数据，以便准确确定状态空间方程初始参数，提出基于EM-IKF协同算法的参数自适应更新方法，从而实现轴承剩余寿命自适应在线预测。通过滚动轴承工程实例验证与分析，结果表明：与传统方法相比，本文方法预测精度至少可以提高24.64%。
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关键词:
- 滚动轴承 /
- 剩余寿命预测 /
- 西沃兹信息准则（SIC） /
- 增量卡尔曼滤波（IKF） /
- EM算法
Abstract:In view of the characteristics reflecting the two-stage performance degradation process of rolling bearings, a remaining life prediction method of rolling bearings was proposed based on an EM-IKF algorithm. In the stationary stage, to determine the initial degradation point of the bearing, the Schwarz information criterion (SIC) was used to identify the change point of bearing health state. In the degradation stage, a performance degradation characterization model was established based on Wiener process. To overcome the problem that the traditional Kalman filtering method ignored the parameter’s volatility between the adjacent times, the state space equation was established based on an incremental Kalman filter (IKF) algorithm. Meanwhile, to fully develop and utilize the historical data and the online monitoring data, and accurately determine the initial parameters of the state-space equation, a parameter adaptive updating method was proposed based on an EM-IKF collaborative algorithm. Then, the adaptive online prediction of bearing remaining life was realized. Finally, the effectiveness of the proposed method was verified and analyzed by an engineering example involving the rolling bearings. The results showed that compared with the traditional method, the prediction accuracy of the proposed method can be improved by at least 24.64%.
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表 1 轴承加速寿命试验工况
Table 1. Accelerated test condition of bearing life
工况编号 转速/（r/min） 径向力/kN 1 2100 12 2 2250 11 3 2400 10 
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表 2 轴承的变点识别
Table 2. Changes point identification’s results of bearings
滚动轴承编号 初始退化点编号 2-1 453 2-5 165
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表 3 滚动轴承预测平均均方误差对比
Table 3. Comparison of average square errors in antifriction bearings’ predictions
轴承编号 平均均方误差 M0 M1 2-1 0.0544 0.1037 2-5 0.2067 0.2743
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