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用SCAE堆叠的卷积自动编码器用于对有限数据样本的液压活塞进行故障诊断

Oybek Eraliev¹, Kwang-Hee Lee², Chul-Hee Lee²

¹Department of Future Vehicle Engineering, Inha University, 100 Inharo, Mitchuholgu, Incheon 22212, Republic of Korea.

Sensors (Basel, Switzerland)

|July 27, 2024

概括

一种新的深度学习模型,堆叠卷积自编码器 (SCAE),在诊断液压活塞故障时达到99.5%以上的准确性,即使数据有限且噪音大. 这种先进的模型在特征提取和诊断性能方面超过了传统方法.

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:

深度学习 (DL) 模型擅长故障诊断,但需要大量的数据.
由于传感器问题,有限的数据对可靠的DL模型性能构成重大挑战.
与DL相比,传统的机器学习 (ML) 方法在特征提取和维度减少方面存在局限性.

研究的目的:

开发一种新的深度学习模型,堆叠卷积自编码器 (SCAE),以应对故障诊断中有限数据的挑战.
增强梯度信息流并提取更丰富的层次特征,以提高诊断准确度.
用有限和噪音数据评估SCAE模型在液压活塞故障诊断方面的性能.

主要方法:

开发一种新的堆叠卷积自编码器 (SCAE) 模型.
时间频率视觉模式识别用于故障诊断的应用.
从液压活塞的有限数据样本上对SCAE模型的评估.
与DNN,SSAE和CNN等传统DL模型进行比较分析.

主要成果:

拟议的SCAE模型实现了优异的诊断性能,准确度超过99.5%.
与DNN,SSAE和CNN相比,SCAE模型表现出更高的性能.
该模型表现出强大的诊断能力,即使在杂的数据条件下.

结论:

新的SCAE模型有效地解决了故障诊断系统中有限数据的挑战.
SCAE模型提供了增强的特征提取和梯度流,以获得更高的诊断准确性.
拟议的方法为液压活塞故障诊断提供了可靠和有效的解决方案,优于现有的DL模型.

关键词:

这是分类分类的分类.错误诊断错误诊断错误诊断是一个问题.液压活塞液压活塞有限数据样本有限的数据样本.堆叠的卷积自动编码器

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