使用人工智能对液压机械冷却系统的故障分类
Haseeb Ahmed Khan1, Uzair Bhatti1, Khurram Kamal1
1Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种基于人工智能的方法,用于使用ResNet-18.8.对液压系统冷却故障进行分类. 这种新的方法实现了近95%的准确性,增强了运营可持续性并防止了故障.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 液压系统在制造业和机器人技术等各种行业中至关重要.
- 液压系统的故障可能导致大量的运行停机时间.
- 人工智能 (AI) 为故障预测和分类提供了潜力.
研究的目的:
- 提出一种基于人工智能的新方法,用于对液压测试装置中的冷却系统行为进行分类.
- 调查深度学习模型在液压故障诊断中的有效性.
主要方法:
- 在三种不同的故障条件下,从液压测试装置的时间序列数据生成的光谱图.
- 采用卷积神经网络 (CNN) 变体,特别是残余网络 (ResNet-18),用于故障分类.
- 使用混矩阵提取性能指标,如F-score,精度,准确性和回忆.
主要成果:
- 在经过严格的测试,验证和培训后,ResNet-18模型表现出高分类准确度,接近95%.
- 该模型成功地在液压系统的冷却行为中分类了三个不同的故障条件.
- 性能评估证实了该模型在区分正常和故障运行状态方面的有效性.
结论:
- 开发的基于人工智能的方法,利用ResNet-18,对于分类液压系统冷却故障非常有效.
- 这种方法可以大大帮助防止运行停机时间,并确保液压系统的可持续运行.
- 该研究强调了深度学习在工业液压设备中用于先进故障诊断的潜力.
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