A multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery

Intelligent Fault Diagnosis (IFD) models are all trained in one-time learning way, lacking the incremental learning capability for continually incoming samples and newly occurring faults. Although Online Fault Diagnosis (OFD) models with incremental learning capability have begun to attract sustaining attention and extensive research, they are just suitable for a single-sensor signal, limiting their diagnostic performance on machinery operating under complex environment. To solve these problems, this paper proposes a multi-sensor fused incremental broad learning with D-S theory for online fault diagnosis of rotating machinery. Firstly, a feature fusion method based on mutual attention mechanism is designed to sufficiently explore the similarity relationship of the multi-sensor data after fast Fourier transform (FFT). After that, the fused feature matrices are inputted to broad learning system for training and the outputs are fused based on D-S evidence theory, achieving multi-level information fusion to ensure adequate fusion of the multi-sensor signals. Additionally, the sample and class incremental learning are developed to rapidly update subsequent models without retraining. Finally, two experiment datasets concerning the key components of rotating machinery are employed to verify the effectiveness and superiority of the proposed method. Benefiting from the advantages of sample incremental learning, the model can be updated in approximately 6 s and 0.15 s, respectively, which is less than the initial model. Meanwhile, the testing accuracy of subsequent models is still maintained at 100% as more and more samples are employed for model updates. Besides, it can be updated in 13 s and 1 s respectively to accommodate new faults, while still has excellent accuracy after fusion. Consequently, the proposed method is an effective online fault diagnosis method because it represents significant advantages in reducing time consumption and improving the accuracy and reliability of diagnostic results.