机器学习用于旋转机械故障分析:全面审查
Oguzhan Das1, Duygu Bagci Das2, Derya Birant3
1National Defence University, Air NCO Higher Vocational School, Department of Aeronautics Sciences, Izmir, Turkey.
Heliyon
|July 6, 2023
概括
本综述探讨了基于人工智能的旋转机械智能故障诊断和预测 (IFDP) 的挑战. 它全面检查了IFDP程序,数据和算法,为工业4.0应用提供了洞察力.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 工业4.0需要对旋转机械进行先进的故障分析.
- 智能故障诊断和预测 (IFDP) 模型对于机械健康至关重要.
- 现有的IFDP模型在现实世界部署时面临挑战.
研究的目的:
- 为旋转机械提供IFDP程序的全面审查.
- 识别和分析开发和应用IFDP模型的挑战.
- 探索IFDP的当前文献和未来方向.
主要方法:
- 对IFDP方法的系统审查.
- 对故障分析策略,数据源和数据类型的分析.
- 检查机器学习技术和复合故障考虑.
主要成果:
- 确定了关键挑战:模型评估,现实世界的适用性,域名适应性,数据问题和算法选择.
- 对各种旋转机械部件 (轴承,轮,旋转机等) 的IFDP方法进行了审查. ) 的情况.
- 强调了特定组件问题对机器健康指标的影响.
结论:
- 解决已识别的挑战对于旋转机械有效的IFDP至关重要.
- 未来的研究应该专注于提高模型稳定性,数据融合和域名适应性.
- 该审查为推进IFDP在工业4.0.0中的基础理解提供了基础理解.
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