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Cross-condition rolling bearing multistage degradation recognition based on health indicator matrix and transition sample recognition enhancement network with multi-branch encoding
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DOI：10.1088/1361-6501/ae5df1.png)
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Abstract
En 中文
Accurate recognition of multistage degradation (MD) in rolling bearings is of great significance for equipment condition monitoring. However, health indicator (HI) constructed using existing single-method approaches often fail to simultaneously achieve degradation sensitivity and monotonicity under varying operating conditions. In addition, mainstream MD recognition methods exhibit limited performance in recognizing transitional samples across degradation stages under cross-condition scenarios. To address these challenges, this study proposes a cross-condition MD recognition method for bearings based on a HI matrix (HIM) and a transition sample recognition enhancement network with multi-branch encoding (TSREN-MBE). Firstly, a degradation sensitive HI (DSHI) is constructed by integrating the least absolute shrinkage and selection operator (LASSO) with the grey wolf optimization algorithm, using comprehensive fault frequency energy as the LASSO regression target, thereby capturing the intrinsic degradation process of bearings. Meanwhile, a time-weighted Wasserstein distance (TWWD) metric is introduced into unsupervised HI construction by incorporating temporal degradation information into the WD, thereby improving the monotonicity of unsupervised HIs. Secondly, DSHI and TWWD are concatenated to form the HIM, which drives the Gath–Geva fuzzy clustering algorithm to adaptively generate MD labels for bearings under different operating conditions. Finally, a TSREN-MBE model is constructed, where multi-branch transformer encoders and multi-head attention are employed to encode and fuse heterogeneous features. By jointly leveraging transition sample recognition enhancement loss, local maximum mean discrepancy, and cross-entropy loss in a joint loss function, the model enhances recognition of transitional samples and improves cross-condition MD recognition accuracy. Experimental results on the XJTU-SY dataset demonstrate the effectiveness and superiority of the proposed method.
Keywords:
health indicator matrix
multistage degradation recognition
transition sample enhancement
cross-condition monitoring
multi-branch encoding
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