基于多尺度小波分解和特征融合的旋转机械多层次类不平衡故障诊断

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Zhen Guo , Wenliao Du , Chuan Li , Yannan Yu , Tao Hu , Shuai Wang , Zhiping Liu
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引用次数: 0

摘要

旋转机械作为工业系统的核心设备,其健康状态监测对保证生产安全高效至关重要。然而,在实际工作条件下,故障样本稀缺且分布不均匀,导致传统诊断模型对少数类型故障的识别能力明显下降。针对这一问题,提出了一种基于多尺度小波分解和特征融合网络(MWDFN)的故障诊断模型。首先,通过三电平离散小波变换(DWT)提取信号的低频近似系数,得到信号的统计特征;在此基础上,小波包变换(WPT)对统计显著的高频系数进行二次分解,生成多尺度能量熵联合特征。其次,通过特征拼接和标准化处理构建低维融合特征向量;最后,采用分层自适应采样策略,结合随机森林分类器的类权值对分类边界进行调整和优化。实验结果表明,在4个数据集上的平均准确率达到了88.84%。MWDFN的源代码可从http://github.com/MR-ach/MWDFN获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-scale wavelet decomposition and feature fusion for rotating machinery fault diagnosis under multi-level class imbalance
As the core equipment of industrial systems, rotating machinery, the health status monitoring of which is of vital importance to ensuring production safety and efficiency. However, in actual working conditions, fault samples are scarce and unevenly distributed, resulting in a significant decline in the ability of traditional diagnostic models to identify a few types of faults. To address this issue, this paper proposes a fault diagnosis model based on multi-scale wavelet decomposition and a feature fusion network (MWDFN). Firstly, the low-frequency approximate coefficients of the signal are extracted through the three-level discrete wavelet transform (DWT) to obtain the statistical characteristics. Based on this, the wavelet packet transform (WPT) conducts a secondary decomposition on the statistically significant high-frequency coefficients, generating multi-scale energy-entropy joint features. Secondly, low-dimensional fusion feature vectors are constructed through feature stitching and standardized processing. Finally, a hierarchical adaptive sampling strategy is adopted, combined with the class weights of the random forest (RF) classifier, to adjust and optimize the classification boundaries. The experimental results show that the average accuracy rate on the four datasets reached 88.84 %. The source code of MWDFN is available at http://github.com/MR-ach/MWDFN.
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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