结构耦合下机械工程设备制造中数控机电系统的故障诊断技术

IF 3.1 Q1 Mathematics
Xueqing Bai
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引用次数: 0

摘要

本研究针对机械工程装备制造中数控机电系统的故障诊断技术,探讨了结构耦合影响下的故障检测方法,以提高故障诊断的准确性和效率。研究首先分析了故障诊断的时域和频域特征,包括用于识别不同类型故障的定量和无量纲特征。随后,研究探讨了特征降维方法,包括 PCA、LLE 和 t-SNE 等算法,并比较了它们在故障诊断中的应用效果。研究重点提出了一种名为 LTCN-BLS 的轻量级深度学习故障诊断框架,该框架结合了 2-DLTCN 和 1-DLTCN 分支,以及基于 ILAEN 的 BLS 分类器,可有效提取和融合数据的时域和时频域特征。实验结果表明,LTCN-BLS 框架在故障诊断中具有准确度高、网络复杂度低的特点,与传统方法相比,在早期故障监测、退化评估和鲁棒性方面具有明显优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault diagnosis technology of CNC electromechanical system in mechanical engineering equipment manufacturing under structural coupling
This study addresses the fault diagnosis technology of CNC electromechanical systems in mechanical engineering equipment manufacturing, and explores the fault detection methods under the influence of structural coupling to improve the accuracy and efficiency of fault diagnosis. The study first analyzes the time-domain and frequency-domain features for fault diagnosis, including quantitative and dimensionless features used to identify different types of faults. Subsequently, the study explores feature dimensionality reduction methods, including algorithms such as PCA, LLE and t-SNE, and compares the effectiveness of their application in fault diagnosis. The research focuses on proposing a lightweight deep learning fault diagnosis framework called LTCN-BLS, which combines 2-DLTCN and 1-DLTCN branches, and an ILAEN-based BLS classifier to effectively extract and fuse time-domain and time-frequency-domain features of the data. The experimental results show that the LTCN-BLS framework has high accuracy and low network complexity in fault diagnosis, and has obvious advantages in early fault monitoring, degradation assessment, and robustness compared with traditional methods.
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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