基于FMEA的规范模型,用于设备维修指南.
Domingos F Oliveira1,2, Miguel A Brito2, Duarte J Brandão2
1Department of Informatics and Computing, Mandume Ya Ndemufayo University, Lubango, Angola.
Frontiers in artificial intelligence
|November 17, 2025
概括
机器学习使用时间序列数据准确预测维修动作,增强制造维护. 这支持最佳的维修选择,最大限度地减少停机时间并提高生产效率.
科学领域:
- 制造业 工程 制造工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 尽量减少制造业停机时间和提高生产效率至关重要.
- 准确预测机器维修需求对于主动维护至关重要.
研究的目的:
- 展示机器学习如何支持维护团队选择最佳维修方法.
- 利用机器故障数据和故障模式和影响分析进行预测性维护.
主要方法:
- 采用设计科学研究范式.
- 整合CRISP-DM和PDCA方法论用于数据科学质量保证.
- 用多变量时间序列数据制定修复动作预测作为分类问题.
主要成果:
- 评估两个建模方法:合并时间序列与保存它们为3D数组.
- 多层感知器 (MLP),卷积神经网络 (CNN) 和完全卷积网络 (FCN) 模型的应用.
- 基于分类准确性和时间序列处理和架构的影响评估模型性能.
结论:
- 突出预测机器维修的有效策略.
- 通过准确的预测,在制造环境中推进规范性维护.
- 展示机器学习在优化维护操作中的价值.
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