一种可解释的空气发动机轴承故障诊断方法,基于基于动态功率设置的信念规则
Jinyuan Li1, Wei He1, Hailong Zhu2
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, China.
Scientific reports
|December 29, 2024
概括
本研究引入了一个可解释的信念规则基础,具有动态功率集 (D-HBRBP-I),用于航空发动机轴承故障诊断. 这种新的方法通过解决数据不平衡和当地无知,提高了准确性和可信度,达到99%的准确性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的航空发动机轴承故障诊断对于运营安全和降低成本至关重要.
- 当前的黑子模型,就像深度神经网络一样,缺乏可解释性,阻碍了对其预测的信任.
- 现实世界的工业数据往往会带来诸如不平衡的样本类别和本地无知等挑战,从而降低模型的准确性.
研究的目的:
- 为航空发动机轴承提出一种新的故障诊断方法,以提高可解释性和准确性.
- 解决现有模型的局限性,包括缺乏透明度,数据不平衡和当地无知.
- 开发一个诊断模型,提供可信和可靠的故障识别.
主要方法:
- 开发了一个基于可解释的信念规则基础的诊断模型,具有动态功率集 (D-HBRBP-I).
- 采用P-CMAES算法来优化模型,并结合了可解释性约束.
- 拟议的方法使用航空发动机轴承数据集进行了评估.
主要成果:
- D-HBRBP-I模型有效地解决了样本类别不平衡和当地无知的问题.
- 使用P-CMAES算法的优化确保了诊断模型的可解释性.
- 拟议的方法在航空发动机轴承故障诊断方面实现了99%的高精度.
结论:
- 开发的可解释的信念规则基础与动态功率集 (D-HBRBP-I) 为航空发动机轴承故障诊断提供了可靠的解决方案.
- 该方法在工业应用中成功克服了传统黑子模型的局限性.
- 这项研究表明了可解释的AI模型在关键工程系统中的潜力.
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