以数据驱动初始化为基础的柴油发动机可靠性故障诊断方法
Huimin Guan1, Guanyu Hu2, Hongyao Du1
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
本研究引入了基于数据的柴油发动机故障诊断方法,改进了有限专家知识的信念规则基础 (BRB) 模型. 在复杂的系统中,DI-BRB-R方法提高了可靠性和诊断准确性.
科学领域:
- 工程
- 计算机科学
- 人工智能
背景情况:
- 柴油发动机对于运输和工业来说至关重要,但由于数据的局限性,它们的故障诊断具有挑战性.
- 信念规则基础 (BRB) 模型具有潜力,但严重依赖专家知识,限制了它们的应用.
- 现有的方法难以处理复杂的系统,数据不足以准确诊断柴油发动机故障.
研究的目的:
- 为柴油发动机提出一种新的可靠性故障诊断方法.
- 在有限的专家知识条件下增强信念规则基础 (BRB) 的建模能力.
- 开发用于柴油发动机故障诊断的BRB模型的数据驱动初始化方法.
主要方法:
- 建议对BRB模型采用数据驱动的初始化方法 (DI-BRB-R).
- 使用戴维斯-博尔丁指数 (DBI-FCM) 的模糊c-means集群初始化属性参考值.
- 用拉普拉斯光滑 (LS-GMF) 的高斯成员函数初始化规则信任度,以可靠性准则为指导.
主要成果:
- 拟议的DI-BRB-R方法有效地使用数据初始化BRB模型,减少对专家知识的依赖.
- 该方法证明了柴油发动机的改进建模能力和诊断准确性.
- 对WD615柴油发动机的验证证实了可靠性故障诊断方法的有效性.
结论:
- 对于柴油发动机故障诊断,DI-BRB-R方法提供了一个强大的解决方案,尤其是在有限的专家投入的情况下.
- 数据驱动初始化显著提高了BRB模型在复杂工程诊断中的实用性.
- 该研究为确保柴油发动机的运行安全和系统可靠性提供了可靠和有效的方法.
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