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在旋转机械中的轴承的AI驱动弹性故障诊断

Syed Muhammad Wasi Ul Hassan Naqvi¹, Arsalan Arif¹, Asif Khan²

¹Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23640, Pakistan.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

深度学习模型,特别是卷积神经网络 (CNN),在诊断旋转机械的轴承故障方面明显优于传统的机器学习 (ML). CNNs实现了100%的准确性,突出了它们在复杂故障诊断中的有效性.

科学领域:

工程工程师工程师工程师
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

预测性维护对于旋转机械至关重要,以防止故障并提高运营效率.
轴承故障是常见的问题,可能导致大量停机时间和成本.

研究的目的:

为了比较传统的机器学习 (ML) 和深度学习 (DL) 技术在轴承故障的诊断性能.
在各种负载和速度条件下评估这些方法.

主要方法:

执行了两个分类任务:一个三类 (健康,内赛故障,外赛故障) 和一个九类 (包括故障严重程度).
评估了传统的ML算法,如袋式树,神经网络,SVM和KNN.
深度学习模型,特别是卷积神经网络 (CNN),在使用连续波纹转换 (CWT) 生成的缩影图上进行训练.

主要成果:

包装树木在ML方法中获得了最高的准确性 (93.04%为三类,87.13%为九类).
在这两项分类任务中,CNN模型的准确性高达100%.
在识别复杂的轴承故障模式方面,CNNs表现出卓越的性能.

结论:

深度学习,特别是在CWT扫描图上训练的CNN,在进行故障诊断方面明显优于传统的ML.
在处理复杂,非线性和动态诊断场景时,CNN非常有效,这证明了它们更长的培训时间.
该研究证实了DL在旋转机械的先进预测性维护方面的潜力.

关键词:

人工智能的人工智能是人工智能.轴承故障诊断轴承故障诊断轴承故障诊断连续波波变换 (CWT) 是一种连续波波变换.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.机器学习是机器学习.扫描图,扫描图,扫描图,扫描图

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