滚动轴承的剩余使用寿命预测基于多尺度转换和ISSA-LSTM
Hongju Wang1, Xi Zhang1, Mingming Ren1
1School of Mechanical and Electrical Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China.
Entropy (Basel, Switzerland)
|November 24, 2023
概括
这项研究预测了滚动轴承的剩余使用寿命 (RUL),使用最大相关性曲折解卷 (MCKD),多尺度转换 (MPE) 和长短期记忆 (LSTM) 网络. 综合方法实现了0.007的低根平均平方误差,提高了机器的可靠性.
科学领域:
- 机械工程 机械工程
- 数据科学数据科学数据科学
- 可靠性工程可靠性工程
背景情况:
- 轴承性能对于旋转机械的可靠性和安全性至关重要.
- 准确的剩余使用寿命 (RUL) 预测对于积极的维护和可靠性策略至关重要.
- 操作轴承数据中的故障标记通常被噪声所掩盖,这使RUL预测复杂化.
研究的目的:
- 调查最大相关性曲解解卷变 (MCKD),多尺度转换 (MPE) 和长短期记忆 (LSTM) 对于滚动轴承RUL预测的应用.
- 为了解决LSTM网络中参数配置的挑战,使用改进的Sparrow搜索算法 (ISSA).
- 评估融合这些先进技术的有效性,以提高RUL预测准确度.
主要方法:
- 最大相关性曲解解卷变 (MCKD) 用于增强故障特征.
- 用于捕捉数据复杂性的多尺度转换 (MPE).
- 长期短期记忆 (LSTM) 循环神经网络,通过改进的子搜索算法 (ISSA) 优化,用于时间模式建模.
- 融合MCKD,MPE和LSTM,用于全面的RUL预测.
主要成果:
- MCKD有效地增强了轴承故障标志.
- MPE成功地捕捉了承载运营数据中的复杂性.
- 用ISSA优化参数的LSTM证明了对RUL预测的时间模式建模的熟练程度.
- 综合方法在RUL预测中实现了0.007的根平均平方误差 (RMSE).
结论:
- MCKD,MPE和LSTM的融合提供了一个强大的,准确的方法来预测滚动轴承的剩余使用寿命.
- 这种综合方法利用了每个技术的优势,改善了旋转机械的故障检测和可靠性评估.
- 该研究强调了先进的信号处理和机器学习在增强预测性维护策略方面的潜力.
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