研究过器寿命和性能预测,整合注意力机制和优化算法
Xibao Wu1, Da Sun1, Huixiang Liu1
1Beijing Information Science and Technology University, Beijing, China.
The Review of scientific instruments
|February 25, 2026
概括
预测过介质的情况.
科学领域:
- 环境工程 环境工程
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 清洁室空气质量对于系统效率和产品质量至关重要.
- 气体过介质的降解需要先进的预测性维护.
- 传统的替代策略对于复杂的污染场景是不够的.
研究的目的:
- 为气体过介质开发一个准确的剩余使用寿命 (RUL) 预测模型.
- 整合多种机器学习技术,以提高预测能力.
- 为联合预测过器性能和RUL提供统一的框架.
主要方法:
- 开发了一个PSO-RF-BiLSTM-Attention模型,整合了随机森林,BiLSTM,自我注意力和PSO.
- 设计了一个实验平台,用于模拟在SO2下活性炭过器的降解.
- 收集多维操作数据用于模型培训和验证.
主要成果:
- 该PSO-RF-BiLSTM-Attention模型的表现明显优于基线模型.
- 实现了~58.7%的平均绝对误差 (MAE) 减少.
- 增加了约5.6%的确定系数 (R2).
结论:
- 拟议的模型有效预测气体过介质性能和复杂降解场景中的RUL.
- 证明了联合预测的统一框架的可行性.
- 在清洁室环境中为智能维护策略提供有效支持.
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