一个用于复杂故障诊断的插即用数据处理模块

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Updated: Sep 10, 2025

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Runfang Hao¹, Chaoqian He¹, Yongqiang Cheng²

¹Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan 030024, China; Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education, Taiyuan 030024, China.

ISA transactions

|August 26, 2025

概括

这项研究引入了一个数据处理模块,通过扩大训练样本和提高模型稳定性来增强智能故障诊断. 这些新方法显著提高了检测复杂设备故障的准确性.

科学领域:

工程
人工智能
机器学习

背景情况:

智能故障诊断对于现代设备至关重要,但由于数据集较小和故障复杂而受到限制.
现有的方法因数据稀缺和复杂的故障模式而难以广泛的工业应用.

研究的目的:

开发一个可插入和使用的数据处理模块,以克服智能故障诊断的局限性.
通过扩大样本规模和改进数据关系来提高深度学习模型的性能.

主要方法:

使用离散里埃转换来融合故障类型并生成新样本的幅度相复合数据增强 (APCUP) 方法.
处理标签的相邻标签平滑 (ALS) 策略,防止过度分类并提高模型的稳定性.
将这些方法集成到一个新的数据处理模块中.

主要成果:

在工业机器人臂平台和两个公共数据集上证明了有效性和可行性.
显著增强了模型诊断复杂故障的能力.
在模块内置后,诊断准确度显著提高,错误减少.

结论:

开发的数据处理模块有效地扩展了培训数据集,并改善了内部数据关系.
通过APCUP和ALS策略,提高智能故障诊断的深度学习模型,特别是复杂的工业设备.
该模块为提高故障诊断系统的准确性和稳定性提供了实用解决方案.

关键词:

数据增强数据处理错误诊断标签的光滑

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