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在古典和贝叶斯推理框架下,在热电厂的轮发电机组的随机建模和参数估计
Ashish Kumar1, Ravi Chaudhary1, Kapil Kumar2
1Department of Mathematics & Statistics, Manipal University Jaipur, Jaipur, India.
PloS one
|October 20, 2023
概括
这项研究开发了一种用于热电厂轮发电机可靠性的新随机模型. 它比较了经典和贝叶斯的方法来评估关键性能指标,如系统故障的平均时间 (MTSF).
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
- 随机模型建模 随机模型建模
背景情况:
- 热电厂依靠轮发电机组来发电.
- 评估和提高轮发电机的可靠性对于工厂的效率和产量至关重要.
- 现有的可靠性模型可能无法完全捕捉复杂的系统动态.
研究的目的:
- 开发一种用于分析轮发电机可靠性的新型随机模型.
- 在经典和贝叶斯框架内估计模型参数.
- 评估和比较可靠性特征,如MTSF,可用性和利函数.
主要方法:
- 开发一个包含冷待机冗余和韦布尔分布式随机变量的随机模型.
- 应用再生点技术和半马尔科夫方法来评估可靠性.
- 使用蒙特卡洛模拟用于模型验证和比较.
主要成果:
- 该研究对可靠性指标的真实,经典和贝叶斯估计进行了比较分析.
- 参数估计和模型性能根据完美的维修和可用的维修设施的假设进行评估.
- 拟议的模型有效地评估了五组件轮发电机组的可靠性特征.
结论:
- 开发的随机模型为轮发电机可靠性评估提供了一个强大的框架.
- 经典和贝叶斯的方法都为系统可靠性提供了有价值的见解.
- 这些发现支持热电厂的增强维护策略和运营规划.
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