| Rolling bearings are common and important components in rotating machinery,playing a vital role in the normal operation of mechanical systems.In practical industrial environments,compound faults of rolling bearing components often occur,but traditional single-fault diagnosis models cannot achieve decoupling diagnosis of compound faults.Transformer is an emerging deep learning model that has achieved superior performance in multiple tasks across various fields.It is a highly generalized model with stronger feature extraction ability.Therefore,this paper focuses on the decoupling diagnosis of rolling bearing compound faults based on the Transformer model,and mainly completes the following work:(1)To address the problem of insufficient compound fault categories in existing research datasets for rolling bearing compound faults,this paper designs the local fault processing form and experimental scheme of rolling cylindrical bearings,and obtains a bearing fault dataset that includes multiple single faults and compound faults under three speed conditions.This provides a data foundation for fully verifying the performance of the subsequent decoupling diagnosis model.(2)To address the decoupling diagnosis problem of compound faults in rolling bearings,this paper proposes a decoupling Transformer encoder model(DTE)and its corresponding multi-channel signal fusion model with convolutional fusion(DTECF).These two models achieve high accuracy decoupling diagnosis of 96.96%/99.32% and 100%/99.89% on the rolling bearing compound fault dataset and the gear-rolling bearing compound fault dataset respectively,effectively solving the problem that traditional single-fault diagnosis models cannot decouple compound faults.(3)To address the problem of poor generalization stability and long training time of the DTE and DTECF models,this paper proposes a decoupling Transformer model(DT)and its corresponding multi-channel signal fusion model with convolutional fusion(DTCF).These two models exhibit high accuracy decoupling diagnosis of 99.49%/99.16 and99.89/99.52% on the rolling bearing compound fault dataset and the gear-rolling bearing compound fault dataset respectively.Compared with compound fault decoupling diagnosis models based on CNN and MLP,the DT and DTCF models have better noise robustness and generalization performance under fewer samples.Moreover,they belong to generative diagnosis models and do not require manually setting the empirical threshold,making them more intelligent and automated.This paper also visualizes the self-attention weights and cross-attention weights of the model to explore its interpretability.(4)To address the problem of decoupling diagnosis of compound faults under variable operating conditions,this paper proposes a decoupling transfer Transformer model with a working condition recognizer(DTTWCR).We use bearing compound fault data under three different speeds and design six transfer tasks for experimental verification.The results show that the proposed model can effectively separate the features related to working conditions and fault features,achieving good transfer diagnosis performance. |
