Fault diagnosis of mining rolling bearings based on low-rank modal fusion and adversarial metric
ZHANG Huakai
JIAN Mingjian
XU Yiran
DING Beidou
WANG Qiqi
SONG Jie
Abstract：To address the problems of weak fault features,scarce high-quality samples,and cross-condition distribution shifts in mining rolling bearings,which lead to insufficient generalization performance of traditional deep learning models,a Mine Rolling Bearing Fault Diagnosis Model Based on Low-Rank Multimodal Fusion and Adversarial Metrics(MTSFCL)is proposed.The superlet transform was used to construct dual-modal input data composed of time-series signals and time-frequency images,which enhanced the multidimensional representation of rolling bearing faults.A lightweight dual-branch feature extraction layer was designed.The temporal branch adopted a Bidirectional Gated Recurrent Unit(BiGRU)enhanced by the Efficient Channel Attention(ECA)mechanism,which captured long-term dependencies in time-series signals while effectively suppressing interference from redundant information.The spatial branch was built on an improved StarNet architecture.Multi-scale convolution and a selective kernel fusion mechanism were used to extract multi-scale fault features from time-frequency images.Element-wise multiplication was used to achieve high-dimensional spatial feature mapping without increasing network depth.A Low-Rank Multimodal Fusion(LMF)module was designed,in which low-rank factors projected temporal and spatial features into a common subspace,and nonlinear fusion was performed through element-wise multiplication,enabling deep interaction between dual-modal features with low computational cost.To improve model generalization performance,a domain adaptation module based on an adversarial metric was constructed by combining the Conditional Domain Adversarial Network(CDAN)with Local Maximum Mean Discrepancy(LMMD)as a metric constraint,thereby reducing marginal and conditional distribution differences between the source domain and the target domain.Experimental results showed that:① the number of parameters of MTSFCL was only 0.322 1 × 106,and the inference time for a single sample was 2.76 ms.② The average diagnostic accuracy under a single operating condition reached 99.94％.Under the small-sample condition with only five fault samples for each class,the average diagnostic accuracy reached 94.12％,which was significantly higher than that of high-parameter models such as ViT and VGG16.③ Under cross-condition scenarios,the average diagnostic accuracy reached 99.28％.Compared with the CDAN domain adaptation method without the LMMD metric constraint,the accuracy increased by 4.27％.High accuracy was also maintained under strong noise interference,demonstrating strong generalization performance and robustness.
Keywords：mining rolling bearingfault diagnosissuperlet transformlow-rank modal fusionadversarial metricconditional domain adversarial Networkdomain adaptation
Publication Date：2026-02-28
Online Publishing Date：2026-04-07(First online date of this platform, not the publication date of the document)
Pages：12( 125-136 )