基于退化检测和优化粒子滤波的轴承寿命预测方法

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许雨晨,李宏坤,马跃,黄刚劲,张明亮.基于退化检测和优化粒子滤波的轴承寿命预测方法[J].,2021,61(3):227-236

基于退化检测和优化粒子滤波的轴承寿命预测方法

Bearing life prediction method based on degradation detection and optimized particle filter

DOI：10.7511/dllgxb202103002

中文关键词:退化指标寿命预测粒子滤波轴承

英文关键词:degradation index life prediction particle filter bearing

基金项目:辽宁省科学技术计划项目(2019JH/10100019)；国家自然科学基金资助项目(U1808214)；大连理工大学基本科研业务费专项资金资助项目(DUT20LAB125).

作者	单位
许雨晨,李宏坤,马跃,黄刚劲,张明亮

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中文摘要:

轴承广泛应用于各种机械设备中，为避免轴承突然损坏而导致设备故障，应有效预测其剩余使用寿命，因此提出一种基于自适应退化检测和粒子群优化粒子滤波(particle swarm optimization particle filter，PSO PF)算法的轴承寿命预测方法．首先，从轴承振动信号中提取候选特征，并对候选特征进行筛选，将优选的特征进行加权融合构建退化指标．然后引入自适应退化检测法确定首次预测时刻．最后引入粒子群优化算法对粒子滤波的重要性采样过程进行改进，使用优化粒子滤波算法从检测到的首次预测时刻开始对轴承进行剩余寿命预测．轴承全寿命实验验证，该方法能够有效预测轴承剩余寿命，并且与常规粒子滤波算法相比具有更高的预测精度．

英文摘要:

Bearings are widely used in various mechanical equipment. In order to avoid sudden damage to the bearing and cause equipment failure, it is of great significance to effectively predict its remaining service life. So a bearing life prediction method based on adaptive degradation detection and particle swarm optimization particle filter (PSO PF) algorithm is proposed. First, the candidate features are extracted from the bearing vibration signal and screened, the preferred features are weighted and fused to construct a degradation index. Then the adaptive degradation detection method is introduced to determine the first prediction time. Finally, the particle swarm optimization algorithm is introduced to optimize the importance sampling process of the particle filter. The optimized particle filter algorithm is used to predict the remaining life of the bearing from the first prediction time detected. Through the full life experiment of the bearing, the method effectively predicts the remaining life of the bearing, and compared with the conventional particle filter algorithm, the method has higher prediction accuracy.

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基于退化检测和优化粒子滤波的轴承寿命预测方法

本站小编 Free考研考试/2024-01-16

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