一个可解释的振动增强型BRB模型用于滚动轴承故障诊断
Ziyu Fan1, Kangle Li2, Hailong Zhu1
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China.
PloS one
|March 13, 2026
概括
本研究引入了振动增强信念规则基础 (VE-BRB) 模型,用于可解释轴承故障诊断. 这种新的方法有效地处理连续的振动信号,提高机器的可靠性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 滚动轴承的状况对于工业机械的可靠性至关重要,故障诊断成为一个关键的研究领域.
- 深度学习模型具有潜力,但存在黑子问题,需要大量的数据.
- 基于知识的模型,如信念规则基础 (BRB),提供可解释性和稳定性,特别是在有限的数据.
研究的目的:
- 解决传统的BRB模型在处理连续信号以进行轴承故障诊断方面的局限性.
- 提出一种新的振动增强信念规则基础 (VE-BRB) 模型,以提高可解释性和有效性.
- 通过使用振动数据,使工业机械能够进行可靠的故障诊断.
主要方法:
- 使用窗口特征提取预处理连续振动信号.
- 从低频和高频轴承故障特征构建一个能量矩阵,将信号映射到规则匹配的空间.
- 采用证据推理 (ER) 算法来实现模型可解释性和投影共变矩阵适应演化策略 (P-CMA-ES) 进行优化.
主要成果:
- 该VE-BRB模型有效处理连续振动信号,克服了传统BRB方法的局限性.
- 拟议的方法通过证据推理算法证明了可解释性.
- 对基准数据集的验证证实了 VE-BRB 模型在各种条件下的有效性.
结论:
- 振动增强信念规则基础 (VE-BRB) 模型为轴承故障诊断提供了一个有希望的可解释和强大的解决方案.
- 这种方法提高了基于知识的模型在工业机械监控中的实际应用性.
- 通过有效处理连续传感器数据,VE-BRB模型推进了智能故障诊断领域.
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