关于故障模式识别和预测退化过程的联合学习
Di Wang1, Xiaochen Xian2, Changyue Song3
1department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society
|April 10, 2024
概括
本研究介绍了一种联合学习模型,用于识别故障模式,并预测制造系统的剩余有用寿命 (RUL). 该模型整合了故障模式信息,以便更准确地预测RUL,改善预后健康管理 (PHM).
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:
- 准确的预后健康管理 (PHM) 对于防止制造系统的意外故障至关重要.
- 现有的方法经常独立处理故障模式识别和剩余有用寿命 (RUL) 预测,限制准确性.
- 由于不同的传感器信号退化模式,RUL预测高度依赖于特定的故障模式.
研究的目的:
- 提出一种新的联合学习模型,用于同时识别故障模式和预测RUL.
- 利用从多个传感器信号中提取的可解释的降解特征来改进PHM.
- 通过结合故障模式信息来提高RUL预测的准确性.
主要方法:
- 开发了一个联合学习模型,将多个传感器信号集成到降解过程中.
- 提取了可解释的特征,将降解机制视为深度神经网络的输入.
- 使用历史单元数据训练模型,包括传感器信号,故障时间和故障模式.
主要成果:
- 联合学习模型成功地同时执行故障模式识别和RUL预测.
- 该模型有效地描述了特征,RUL和故障模式之间的复杂关系.
- 通过对飞机燃气轮机发动机退化的案例研究证明了有效性.
结论:
- 联合学习失败模式识别和RUL预测显著提高了PHM的准确性.
- 拟议的数据驱动神经网络方法灵活,适用于复杂的制造系统.
- 该方法为预测单元RUL和识别现实应用中的故障模式提供了强大的解决方案.
相关概念视频
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00:59Propagation of Uncertainty from Random Error
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01:22Multi-input and Multi-variable systems
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01:26Classification of Systems-I
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01:31Classification of Systems-II
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01:21Fatigue
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