基于Transformer与多模态异构特征融合的水轮机组故障诊断方法OA

[Objectives]The multimodal fault signals such as vibration anomalies with nonlinear and multi-scale characteristics may be generated during the operation of hydroturbine units.It is difficult for traditional time-series models to capture long-range dependencies,and the single-modal analysis method cannot integrate heterogeneous data features effectively.In response to the above issues,this study proposes a multimodal heterogeneous graph hybrid feature extraction model consisting of Transformer-Gramian angular summation fields(GASF)-recurrence plot(RP)-two-dimensional(2D)-gated recurrent unit(GRU),aiming to improve the reliability and generalization ability of fault diagnosis.[Methods]Firstly,the time-series data of hydroturbine units are converted into 2D images,GASF and RP methods are used to extract spatial features of time-series data,and Transformer model is constructed.Meanwhile,GRU is used to capture dynamic time-series features,and multi-modal feature fusion is used to combine temporal features,image spatial features,and heterogeneous image features.Thus,the accuracy and robustness of fault identification are significantly improved.[Results]The proposed method shows higher accuracy and stronger generalization ability in the fault diagnosis task of hydroturbine units,and the diagnosis accuracy reaches 100%on multiple test sets.[Conclusions]The proposed method can effectively fuse time-series data and image features,significantly enhance the model's ability to recognize nonlinear fault modes,and accurately capture the abnormal state of the device.