一个深度学习模型与信号分解和告知器网络用于设备振动趋势预测
Huiyun Wang1, Maozu Guo1, Le Tian1
1School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究引入了一种新的方法,用于使用信号分解和Informer模型预测设备运行趋势. 该方法通过减少振动信号中的噪音来提高预测准确性,有助于防止设备故障.
科学领域:
- 工程 工程师 工程师 工程师
- 数据科学数据科学数据科学
- 预测性维护是指预测性维护.
背景情况:
- 设备运行趋势预测对于安全和降低成本至关重要.
- 振动监测和时间序列预测是防止设备故障的关键.
- 原始振动信号中的高噪声使准确的趋势预测变得复杂.
研究的目的:
- 提出一个准确的设备运行趋势预测方法.
- 解决振动信号中的噪声挑战,以改善预测.
- 通过准确的预测,提高设备维护的可靠性.
主要方法:
- 使用变化模式分解 (VMD) 优化通过改进的搜索算法 (ISSA) 来分解原始信号到内在模式函数 (IMF).
- 计算了模糊,以识别用于进一步分解的重要组件,使用改进的适应性白噪声完整合体实证模式分解 (ICEEMDAN).
- 在分解后序列上使用Informer模型进行时间序列预测,并重建了结果.
主要成果:
- 拟议的方法有效地将噪音振动信号分解为有意义的组件.
- 适用于这些组件的Informer模型实现了准确的设备运行趋势预测.
- 与现有的预测模型相比,实验结果显示出更高的准确性.
结论:
- 结合的VMD-ICEEMDAN和Informer模型为设备运行趋势预测提供了一个强大的解决方案.
- 这种方法通过有效处理噪音振动数据,显著提高了预测准确度.
- 该方法通过可靠的故障预测,有助于提高设备安全性和降低维护成本.
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