轴承故障的多源诊断使用可解释的增强树
Miguel Fernández-Temprano1, Manuel Astorgano-Antón1, Óscar Duque-Pérez2
1Department of Statistics and Operational Research, Escuela de Ingenierías Industriales, Universidad de Valladolid, 47011 Valladolid, Spain.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
早期检测感应电机故障,特别是轴承问题,对工业至关重要. 这项研究使用可解释的人工智能来识别最好的传感器数据和领域,用于准确的故障诊断,提高工业可靠性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 工业维护 工业维护 工业维护
背景情况:
- 感应电机在工业应用中至关重要,早期故障检测可以防止重大财务损失.
- 轴承故障是感应电机中最常见的问题,需要可靠的诊断技术.
- 传统的诊断依赖于振动分析,但供应电流和声音也被探索,在时间和频率领域.
研究的目的:
- 采用可解释的人工智能 (XAI) 来识别最佳传感器数据和用于感应电机故障诊断的领域.
- 量化通过多传感器数据融合实现的诊断改进.
- 通过严格的模型选择过程,客观地比较不同的诊断方法.
主要方法:
- 利用可解释的人工智能 (XAI) 技术,包括SHAP值,用于解释诊断模型.
- 应用增强技术用于精确的故障诊断.
- 实施了结构化模型选择程序,用于对诊断变量和领域进行客观比较.
主要成果:
- 确定了特定的传感器变量和域,这些变量和域对准确的感应电机故障诊断有重大贡献.
- 证明了整合来自多个传感器的数据 (多传感器数据融合) 的诊断好处.
- 通过应用增强技术和XAI解释,实现了高度精确的诊断.
结论:
- 可解释的人工智能为诱导电机故障诊断提供了对最有效的数据源和领域的宝贵见解.
- 多传感器数据融合提高了诊断准确度,从而提高了工业可靠性.
- 推进模型的SHAP值解释提供了一种透明和有效的方法来理解复杂的诊断决策.
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