基于转移学习的电梯门系统故障预测方法的研究
Jun Pan1, Changxu Shao1, Yuefang Dai2
1Zhejiang Province's Key Laboratory of Reliability Technology for Mechanical and Electronic Product, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种新的深度学习模型,用于电梯门故障预测. 该系统分析操作声音,准确预测剩余使用寿命 (RUL),提高电梯安全.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 声学 声学 在声学方面
背景情况:
- 电梯门系统的安全性对于预防事故至关重要.
- 电梯系统的故障预测对于主动维护至关重要.
- 操作声音分析提供了一种非侵入性的故障检测方法.
研究的目的:
- 开发一个深度学习模型来预测电梯门系统的剩余使用寿命 (RUL).
- 为了应对电梯操作环境的变化和健全的采集方法.
- 为了利用历史的声音数据来准确预测目标电梯系统的故障.
主要方法:
- 收集了来自各种电梯的开关声音.
- 提取的声学特征:A级加权的声压水平,声音强度,清晰度和粗度.
- 采用了一个图形神经网络 (GNN) -长短期记忆 (LSTM) -Bhattacharyya 距离域对抗神经网络 (BDANN) 模型与转移学习.
主要成果:
- 该GNN-LSTM-BDANN模型有效地从转换的图形数据中提取了深度特征.
- 转移学习使知识从历史数据转移到预测目标系统的RUL.
- 实验结果验证了模型在预测潜在故障时间框架方面的能力.
结论:
- 拟议的深度学习方法准确预测电梯门系统故障.
- 这种方法通过可靠的剩余使用寿命预测提高了电梯安全性.
- 该模型对不同环境和采集方法的适应性使其成为预测性维护的宝贵工具.
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