一个可解释的混合框架,将卷积潜向量与基于变压器的注意力机制相结合,用于滚动元件故障检测和分类
Ali Saeed1, M Usman Akram2, Muazzam Khattak1
1Quaid-i-Azam University, Islamabad, 45320, Pakistan.
Heliyon
|November 11, 2024
概括
结合卷积神经网络 (CNN) 和视觉变压器 (ViT) 的新混合框架可以增强工业资产状况监测. 这种人工智能方法在故障检测和预测性维护的分类方面实现了高精度.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 工业资产破产带来了重大的财务,运营和安全风险.
- 有效的状态监测对于维持工业运营至关重要.
- 大量的传感器数据需要先进的分析技术来实时监控.
研究的目的:
- 为监测工业资产状况开发一种新的混合框架.
- 利用机器学习,特别是CNN和ViT,进行准确的故障检测和分类.
- 通过先进的数据分析,实现及时的预测性维护.
主要方法:
- 一个混合框架,集成CNN用于本地特征提取和ViT用于全球上下文理解.
- 利用数据增强技术,以提高模型通用化和计算效率.
- 关于Case Western Reserve University (CWRU) 和MFPT故障数据集的培训和验证.
主要成果:
- 在CWRU数据集上的三个故障类别中达到99.62%的平均故障分类准确度.
- 显示了平均到故障检测时间为38.4毫秒.
- 在MFPT数据集上以99.17%的准确度验证外部赛道和99.26%的内部赛道故障.
结论:
- 拟议的混合CNN-ViT框架为工业资产故障检测和分类提供了一个强大的解决方案.
- 该方法显示高精度和效率,适用于各种数据集大小.
- 该框架的适应性允许与替代卷积模型集成,以增强预测性维护.
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