增强轴承健康指标提取使用斜率自适应信号分解进行预测性维护
Dev Bhanushali1, Pooja Kamat1, Harsh Dhiman2
1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India.
MethodsX
|April 28, 2025
概括
新的斜率自适应信号分解 (SASD) 算法通过改进振动数据分析以进行预测性维护来增强机器健康监测. 基于SASD的指标导致更准确的剩余使用寿命 (RUL) 预测,特别是在GRU模型中.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 有效的机器健康监测对于预测性维护至关重要.
- 传统方法在噪声和振动数据中的趋势保存方面扎.
- 准确的剩余使用寿命 (RUL) 估计需要强大的特征提取.
研究的目的:
- 介绍了新的斜率自适应信号分解 (SASD) 算法.
- 提高从振动信号中提取机器内在健康指标的效果.
- 提高轴承的RUL估计的准确性.
主要方法:
- 开发了SASD算法,包括动态Savitzky-Golay过,细分和基于趋势的重新校准.
- 将SASD应用于PRONOSTIA,用于特征提取的数据集.
- 使用深度学习 (GRU,LSTM) 和回归模型评估SASD衍生指标.
主要成果:
- SASD有效地减轻噪声,同时保持关键信号趋势.
- 与传统方法相比,基于SASD的健康指标显著提高了预测准确度.
- 在各种操作条件下,GRU模型在RUL预测中获得了0.96以上的R2得分.
结论:
- 在不同的条件下,SASD提供了一种可靠的方法来进行健康监测.
- 该算法桥梁信号处理和数据驱动的预测,以提高预测性维护.
- 未来的工作包括将SASD扩展到各种工业数据集,并探索边缘计算应用.
关键词:
带有健康监测的健康监测机器健康指标 机器健康指标预测性维护是一种预测性维护.剩余的使用寿命 (RUL)信号处理 信号处理倾斜适应信号分解用于轴承寿命估计.斜率自适应信号分解 (SASD) 方法更多相关视频
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