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使用增强的Savitzky-Golay过器和改进的深度学习框架预测剩余使用寿命的方法.

Xiangyang Li¹, Lijun Wang², Chengguang Wang¹

¹School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou, 450045, China.

Scientific reports

|October 14, 2024

概括

本研究引入了一种深度学习方法,用于预测设备健康状况和剩余使用寿命 (RUL). 新的框架提高了故障预测的准确性,超过了传统方法.

关键词:

深度学习是一种深度学习.神经网络的神经网络的神经网络预测和健康管理 (PHM)剩余使用寿命 (RUL) 预测

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科学领域:

工程工程师工程师工程师
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

大型设备的运行完整性依赖于有效的故障预测和健康管理.
预报和健康管理 (PHM) 努力从多变量传感器数据中准确预测剩余使用寿命 (RUL).
传统的PHM方法往往需要广泛的特征工程预先知识.

研究的目的:

提出一种新的多道,多规模的深度学习方法,用于增强故障预测和RUL估计.
通过利用复杂的操作数据集的深度学习来解决传统方法的局限性.
提高PHM系统的准确性和稳定性.

主要方法:

改进的萨维茨基戈莱过器 (ISG) 用于高效预处理大,动态传感器体积的数据.
开发了一个混合深度学习框架,将卷积神经网络 (CNN) 集成为空间特征提取和长短期记忆 (LSTM) 网络用于时间依赖模型.
结合CNN和LSTM输出被用来增强综合预测能力.

主要成果:

对C-MAPSS数据集的实验验证证明了框架的有希望的性能,特别是在动态的操作条件下.
对比分析证实了拟议的深度学习方法优于单个故障类型预测的经典算法.
该研究确定了最佳参数,并通过各种融合方法和CNN深度评估了过效率.

结论:

开发的多道,多规模的深度学习框架为设备故障预测和RUL估计提供了强大而准确的解决方案.
虽然该方法并未优化用于多故障预测,但在单故障场景中显著优于传统方法.
这种深度学习策略推进了用于工业应用的预测和健康管理 (PHM) 能力.