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国外文献 > 1.轴承 > 7. 智能方法与计算 > 机器学习

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标 题： A Comparative Study Between Bayesian Filters and Autoencoder Networks for Bearing RUL Prediction
贝叶斯滤波器与自编码器网络在轴承剩余使用寿命预测中的比较研究
作 者： Haobin Wen
Long Zhang
Jyoti K. Sinha
关键词： Remaining useful life  [剩余使用寿命]
Bearings  [ 轴承]
Predictive maintenance  [ 预测性维护]
Bayesian filters  [ 贝叶斯滤波器]
Deep learning  [ 深度学习]
Uncertainty estimation  [ 不确定性估计]
出版年： 2026
文集名： Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences - UNIfied 2024, Volume 1
联合国大会、国际货币基金组织第八届会议及TEPEN会议联合会议论文集——联合国2024年，第一卷
会议名： 2024 International Conference on Damage Assessment of Structures (DAMAS 2024), 2024 International Conference on Maintenance Engineering (IncoME 2024), 2024 Conference of the Efficiency and Performance Engineering (TEPEN 2024)
2024年结构损伤评估国际会议（DAMAS 2024）、2024年维护工程国际会议（IncoME 2024）、2024年效率与性能工程会议（TEPEN 2024）
届 次：
摘 要： The prediction of remaining useful life (RUL) is crucial in the prognostics and health management (PHM) of rolling-element bearings, which serve a significant role in a wide range of rotating machinery. Effective RUL prediction techniques with uncertainty estimation facilitate predictive maintenance that optimises manufacturing efficiency and prevents unscheduled downtime. To assess the performance of probabilistic RUL prediction models, this paper presents a comparative study among state estimation methods based on Bayesian filters and a direct prediction method using deep neural networks. The first method employs the extended Kalman filter (EKF) for bearing degradation state estimation based on the envelope spectral indicator (ESI) that reflects the vibration intensity of defect frequencies in envelope spectra. The second prediction method compares the particle filter (PF) for bearing RUL prediction using the extracted ESI. In the third method, the nonlinearity between envelope spectra and bearing RUL is explored using probabilistic neural networks, where a supervised convolutional variational autoencoder for regression (CVAER) is used to predict RUL distributions. Using openly available run-to-failure vibration data from a bearing test rig, the model performances are evaluated regarding the accuracy of predicted RUL and the corresponding uncertainty interval across the bearing degradation stage.

剩余使用寿命（RUL）的预测在滚动轴承的预测性健康管理（PHM）中至关重要，而滚动轴承广泛应用于各类旋转机械。有效的RUL预测技术结合不确定性估计，有助于实现预测性维护，从而优化生产效率并避免非计划性停机。为了评估基于概率的RUL预测模型的性能，本文对基于贝叶斯滤波器的状态估计方法与采用深度神经网络的直接预测方法进行了比较研究。第一种方法利用扩展卡尔曼滤波器（EKF），结合包络谱指示器（ESI）进行轴承退化状态估计，其中ESI反映包络频谱中缺陷频率的振动强度。第二种预测方法则使用提取的ESI，对比粒子滤波器（PF）进行轴承RUL预测。第三种方法通过概率神经网络探索包络频谱与轴承RUL之间的非线性关系，采用监督式卷积变分自编码器回归（CVAER）来预测RUL分布。利用轴承试验台公开的运行至失效振动数据，评估了该模型在预测剩余使用寿命（RUL）准确性和对应不确定性区间方面的性能，涵盖轴承退化阶段的各个阶段。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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