在使用科尔莫戈罗夫-阿诺德网络的旋转机器中解释性故障分类和严重性诊断
Spyros Rigas1, Michalis Papachristou2, Ioannis Sotiropoulos3
1Department of Digital Industry Technologies, School of Science, National and Kapodistrian University of Athens, 34400 Psachna, Greece.
Entropy (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一种使用科尔莫戈罗夫-阿诺德网络的新深度学习方法,用于诊断机械轴承故障. 该方法提供准确,可解释和高效的实时故障检测和分类.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 滚动元件轴承对于旋转机械至关重要,但轴承故障会导致重大工业故障和停机时间.
- 有效的机器监控对于防止故障和确保运营效率至关重要.
研究的目的:
- 开发一种自动化和可解释的深度学习方法来进行轴承故障诊断.
- 在统一的方法中利用Kolmogorov-Arnold网络进行特征选择和超参数优化.
主要方法:
- 这项研究采用了Kolmogorov-Arnold网络,一种新的深度学习技术,作为多层感知器的替代品.
- 开发了一个统一的框架,用于自动选择功能和超参数优化.
- 浅的网络架构和减少的功能集是优先考虑的模型效率和可解释性.
主要成果:
- 该框架在轴承故障检测和在基准数据集上的故障和严重程度分类方面取得了完美的F1分数.
- 模型证明了在同一数据集内识别各种故障类型的适应性,包括不平衡和不对齐.
- 符号表示和特征归属提供了模型可解释性和对最佳特征选择的洞察力.
结论:
- 拟议的基于科尔莫戈罗夫-阿诺德网络的框架为实时机械监控和轴承故障诊断提供了实用和高效的解决方案.
- 该方法的可解释性和适应性使其适用于实际应用和科学研究.
- 这种方法促进了用于工业诊断的轻量级,可解释的AI模型的开发.
关键词:
科尔摩戈罗夫阿诺尔德网络公司轴承的故障发生在轴承上可以解释的人工智能AI错误分类 错误分类 错误分类 错误分类检测故障的检测故障检测.严重性分类的严重性分类.象征性表示 象征性表示 象征性表示更多相关视频
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