工矿自动化2025，Vol.51Issue(12)：1-9,9.DOI:10.13272/j.issn.1671-251x.2025100035
基于多尺度特征迁移学习的提升机关键部件故障诊断技术
Fault diagnosis technology for critical components of hoisting machines based on multi-scale feature transfer learning
摘要
Abstract
To address the degradation in diagnostic performance caused by missing sample labels of key components-such as bearings and gearboxes-in mine hoisting machines under complex operating conditions,a fault diagnosis technology for critical hoisting machine components based on multi-scale feature transfer learning was proposed.A Lightweight Fault Diagnosis Model Based on Domain Adversarial and Multi-Scale Time-Frequency Feature Extraction(DAMSF-LFDM)was constructed.A Serpentiform Wavelet Coefficient Matrices(SWCMs)representation was proposed.By combining wavelet packet transform,piecewise aggregate approximation,and serpentiform reorganization,a multi-scale time-frequency feature matrix was constructed to fully capture the internal correlation characteristics of vibration signals across different frequency bands.A Multi-Scale Residual Ghost Convolution Block(MRGCB)was proposed.It employed multiple parallel convolutional layers to effectively extract deep features of the input data at different scales,thereby strengthening the model's ability to capture multi-scale information.To extract personalized fault features from SWCMs and perform adaptive fusion,a Fused Multi-Scale Fault Feature Extraction Module(FMFFEM)was introduced.Feature fusion was carried out via summation,and an adaptive feature-weight allocation mechanism was incorporated to complete the fused extraction of features from different frequency bands.By integrating multi-level maximum mean discrepancy loss with a domain adversarial mechanism,a deep transfer diagnosis network based on the domain adversarial mechanism was established,improving the model's adaptability across operating conditions.Experimental results demonstrated that the DAMSF-LFDM model significantly outperformed the comparative models overall,achieving the highest fault diagnosis accuracy across different transfer tasks.The average cross-condition accuracies on the SEU dataset and the MFS-RDS dataset reached 98.67％and 99.80％,respectively.关键词
提升机/故障诊断/多尺度时频特征提取/域对抗网络/深度迁移学习/蛇形小波系数矩阵组Key words
hoisting machine/fault diagnosis/multi-scale time-frequency feature extraction/domain adversarial network/deep transfer learning/serpentiform wavelet coefficient matrices分类
矿业与冶金引用本文复制引用
雷少华,卓帅,徐鸿洋,李冲,汤争争,董飞,俞啸..基于多尺度特征迁移学习的提升机关键部件故障诊断技术[J].工矿自动化,2025,51(12):1-9,9.基金项目
国家自然科学基金项目(52304181) （52304181）
江苏省自然科学基金项目(BK20231060). （BK20231060）
