通过可靠的预测和风险意识强化学习来优化工业设备的预测性维护
Zifei Xu1,2, Qiang Zhang1
1School of Power and Energy Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Complex & intelligent systems
|November 14, 2025
概括
本研究介绍了一个智能预测性维护 (PdM) 框架,使用剩余使用寿命 (RUL) 预测来优化设备维护. 该系统通过管理不确定性和故障风险来平衡安全性和成本,以提高可靠性.
科学领域:
- 工业工程 工业工程 工业工程
- 机器学习 机器学习
- 可靠性工程可靠性工程
背景情况:
- 预测性维护 (PdM) 对于工业设备的性能和降低成本至关重要.
- 剩余使用寿命 (RUL) 预测是有效的PdM策略的关键组成部分.
- 现有的方法往往难以充分解决维护决策中的不确定性和风险.
研究的目的:
- 开发一个智能PdM框架,整合RUL预测和分布式强化学习.
- 提高RUL预测的准确性,量化相关的不确定性.
- 通过考虑长期回报,风险和成本效益来优化维护计划.
主要方法:
- 一个概率神经网络被用于准确的RUL预测和不确定性量化.
- 一个量子回归深度Q网络 (QR-DQN) 代理被用于分布式强化学习.
- 将风险敏感的决策规则纳入,以管理不确定性和失败概率.
主要成果:
- 拟议的框架在复杂的系统退化场景中,与传统基线相比,表现优越.
- 实现了灾难性故障的显著减少,并优化了维护计划.
- 通过有效的风险管理和及时干预,提高整体系统可靠性.
结论:
- 智能PdM框架为管理工业设备维护提供了一个强大的方法.
- 将RUL预测与分布式强化学习相结合,有效地解决了不确定性和风险.
- 这种方法可以提高运营效率,节省成本,提高设备的寿命.
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