可解释的人工智能用于监管执行器的临时和伪临时预测性维护
Jun Tang1, Yang Liu1, Xiaolu Huang2
1Department of Mechanical Engineering, Yanshan University, Qinhuangdao, 066004, China.
Scientific reports
|November 11, 2025
概括
本研究介绍了一种使用机器学习和XAI.的总督执行器 (GVA) 的预测性维护框架. 该方法增强了故障诊断和预警系统,提高了蒸汽轮机运行的可靠性和实时性能.
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
- 人工智能的人工智能
背景情况:
- 总督执行器 (GVA) 对蒸汽轮机的安全性和效率至关重要.
- 高维数据和复杂的故障挑战了传统的诊断算法.
- 现有的方法缺乏实时性能,可靠性和通用性.
研究的目的:
- 为GVA开发一个先进的预测维护框架.
- 为了增强故障诊断,定位和预警能力.
- 为改善维护工作流程提供可解释的归因分析.
主要方法:
- 提出了一个后期和伪后期预测性维护 (PPPM) 框架.
- 借助先进的机器学习和夏普利添加式扩展 (XAI).
- 使用传感器网络优化和归因分析优化故障诊断和预警模型.
主要成果:
- PPPM优化了50%的传感器网络测量点用于故障诊断.
- 归因分析改善了诊断模型的实时性,概括性和可靠性.
- 优化的伪监督预警模型在W-F1得分上取得了5.997%的改善,在AUC上取得了6.942%的改善.
结论:
- 该PPPM框架有效地解决了GVA故障诊断和早期预警方面的挑战.
- XAI集成为预测性维护提供可解释的见解.
- 拟议的方法显著提高了GVA监控系统的性能和可靠性.
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