国外文献 > 1.轴承 > 5. 监测与诊断 > 寿命与维护
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| 标 题： |
A novel approach for bearing remaining useful life estimation under neither failure nor suspension histories condition
一种在既无失效也无停机历史条件下估算轴承剩余使用寿命的新方法 |
| 作 者： |
Xiao, Lei
Chen, Xiaohui
Zhang, Xinghui
Liu, Min |
| 关键词： |
Degradation tendency [降解倾向]
Remaining useful life [ 剩余使用寿命]
Adaptive time window [ 自适应时间窗口]
Increasing rate [ 增长速度]
Back-propagation neural network [ 反向传播神经网络] |
| 出版年： |
2017 |
| 期刊名： |
Journal of Intelligent Manufacturing
智能制造杂志 |
| 年卷期： |
2017, vol.28, no.8 |
| 摘 要： |
Remaining useful life prediction methods are extensively researched based on failure or suspension histories. However, for some applications, failure or suspension histories are hard to obtain due to high reliability requirement or expensive experiment cost. In addition, some systems' work condition cannot be simulated. According to current research, remaining useful life prediction without failure or suspension histories is challenging. To solve this problem, an individual-based inference method is developed using recorded condition monitoring data to date. Features extracted from condition data are divided by adaptive time windows. The time window size is adjusted according to increasing rate. Features in two adjacent selected windows are regarded as the inputs and outputs to train an artificial neural network. Multi-step ahead rolling prediction is employed, predicted features are post-processed and regarded as inputs in the next prediction iteration. Rolling prediction is stopped until a prediction value exceeds failure threshold. The proposed method is validated by simulation bearing data and PHM-2012 Competition data. Results demonstrate that the proposed method is a promising intelligent prognostics approach.
目前，基于故障或停机历史的剩余使用寿命预测方法已得到广泛研究。然而，由于对可靠性要求高或实验成本昂贵，某些应用中很难获取故障或停机历史。此外，部分系统的运行工况无法进行仿真。根据现有研究，缺乏故障或停机历史的剩余寿命预测仍面临挑战。为解决这一问题，本文提出了一种基于个体的推断方法，利用当前记录的健康监测数据进行建模。从状态数据中提取的特征被划分为自适应时间窗口，窗口大小随增长率动态调整。相邻两个选定窗口中的特征作为输入和输出，用于训练人工神经网络。采用多步滚动预测，预测后的特征经后处理后作为下一次预测迭代的输入。当预测值超过故障阈值时停止滚动预测。该方法通过模拟轴承数据及PHM-2012竞赛数据进行了验证。结果表明，所提出的方法是一种有前景的智能预测方法。 |
| 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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