一种开发和管理数据驱动模型的方法,用于海洋发动机长期健康预测
Jaehan Jeon1, Gerasimos Theotokatos1
1Maritime Safety Research Centre, Department of Naval Architecture, Ocean, and Marine Engineering, University of Strathclyde, Glasgow, G4 0LZ, United Kingdom.
ISA transactions
|October 9, 2025
概括
本研究引入了一种用于开发和管理船舶发动机的预测和健康管理 (PHM) 模型的新方法. 它通过数据驱动的管理提高了模型的准确性,改善了海事行业的采用.
科学领域:
- 海洋工程 海洋工程
- 数据驱动建模数据驱动建模
- 预测和健康管理 (PHM)
背景情况:
- 船舶机械需要强大的预测和健康管理 (PHM) 来实现操作可靠性.
- 现有的PHM方法往往缺乏全面的数据驱动型模型管理策略.
- 排气的磨损是船舶发动机的关键降解因素.
研究的目的:
- 提出一种新的方法来开发和管理船舶机械PHM的数据驱动模型.
- 为了研究四冲程船舶发动机中排气磨损的降解.
- 引入一种综合方法,将基于物理的数字双胞胎与数据驱动的模型管理相结合.
主要方法:
- 使用基于物理的数字双胞胎与随机降解模型集成生成的模拟数据集.
- 开发了使用多层感知器和贝叶斯神经网络的健康指标 (HI) 构建和预测子模型.
- 实施数据驱动模型管理,使用错误和不确定性指标进行子模型再培训.
主要成果:
- 在预测准确度方面取得了显著的改善,R平方值从0.24增加到0.89 (案例1) 和0.26增加到0.94 (案例2).
- 证明了用于船舶发动机数字双胞胎的热力学和随机降解模型集成的有效性.
- 验证了数据驱动模型管理对提高PHM系统性能的贡献.
结论:
- 拟议的方法提供了一个强大的框架,用于开发和管理用于船舶发动机的数据驱动的PHM模型.
- 数字双胞胎与随机降解模型和先进的数据驱动技术的整合对于准确的机械健康预测至关重要.
- 这项工作有助于在海事行业更广泛地采用先进的PHM系统.
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