在可变操作条件下进行轴承故障诊断的注意力增强的间隔信念规则基础方法
Bing Chen1, Jingying Li1, Hongyu Li1
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了带有注意力机制 (IBRB-a) 的间隔信念规则基础,用于在动态条件下诊断轴承故障. 该模型在复杂的工业环境中提高了准确性和适应性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 轴承是具有复杂操作条件的关键部件.
- 传统的故障诊断方法与动态复杂性作斗争,导致不准确.
研究的目的:
- 提出一个带有注意力机制的间隔信念规则基础 (IBRB-a),用于在变化的操作条件下进行故障诊断.
- 解决传统方法在解释性,适应性和误判率方面的局限性.
主要方法:
- 将一个新的间隔规则构建方法与内核密度估计 (KDE) 结合起来.
- 一个两阶段的融合策略,使用证据推理 (ER) 来进行渐进的信息融合.
- 使用受约束的预测协差矩阵自适应进化策略 (P-CMA-ES) 的优化.
主要成果:
- 在可变的操作条件下,IBRB-a模型有效地减轻了间隔不确定性.
- 使用轴承数据集的实验验证证明了该模型的有效性.
- 拟议的方法显示出强大的通用性和改进的诊断准确性.
结论:
- IBRB-a模型提供了一个强大的解决方案,用于在复杂,动态的工业环境中进行轴承故障诊断.
- 专家知识的整合和数据驱动的适应提高了诊断性能.
- 这种方法为传统故障诊断技术提供了更可靠,更准确的替代方案.
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