一种基于故障机制和ANN支持的Wiener流程组合的可靠性估计方法
Di Liu1,2,3,4, Yajing Qiao1, Shaoping Wang1,3
1School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
Heliyon
|February 23, 2024
概括
通过将故障机制与人工神经网络 (ANN) 和历史数据相结合,提高了工程可靠性的估计. 这种融合方法提高了准确性,而不是依赖单个数据源的方法.
科学领域:
- 工程 工程师 工程师 工程师
- 可靠性工程可靠性工程
- 数据科学数据科学数据科学
背景情况:
- 由于历史数据有限和故障机制理论不完整,在工程中精确的可靠性估计具有挑战性.
- 现有的方法通常仅依赖于历史数据或故障机制,导致准确度低于最佳.
- 整合这两种数据类型对于改善复杂工程应用中的可靠性预测至关重要.
研究的目的:
- 开发一种新的可靠性估计方法,将工程故障机制与人工神经网络 (ANN) 支持的维纳过程融合在一起.
- 为了提高可靠性评估,利用历史数据和故障机制知识.
- 通过使用现实世界的工程数据,验证拟议方法在传统方法上的优越性.
主要方法:
- 使用一个包含随机效应的维纳过程模型,并集成了ANN和故障机制.
- 贝叶斯模型平均化 (BMA) 方法用于结合ANN支持的维纳过程和故障机制.
- 模型参数通过BMA框架的数据融合来更新.
主要成果:
- 与仅依赖ANN或故障机制的模型相比,拟议的融合方法显示出更高的可靠性估计准确性.
- 使用航空液压数据集的验证证实了综合方法的实际优势.
- 失效机制和ANN支持的维纳过程的融合显著改善了预测性能.
结论:
- 开发的方法有效地整合了历史数据和故障机制,以准确的工程可靠性估计.
- 这种方法比使用孤立数据源的现有方法提供了显著的进步.
- 这些发现对于提高工程系统的可靠性和安全性具有实际意义.
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