DTMPI-DIVR: A digital twins for multi-margin physical information via dynamic interaction of virtual and real sound-vibration signals for bearing fault diagnosis without real fault samples

Abstract

Traditional methods for bearing fault diagnosis have limitations, such as relying on unimodal modelling and requiring a large amount of labelled fault data. To address these issues, a multi-margin physical information digital twin framework (DTMPI-DIVR) is proposed. This framework is based on the dynamic interaction of real and virtual signals and can realize bearing fault diagnosis without real fault samples. A 15-degree-of-freedom nonlinear sound-vibration coupled dynamics model is constructed to simulate the complex behaviour of rotating machinery, and a signal decoupling algorithm is introduced to extract independent fault-related margin information from multimodal signals. Gaussian process regression (GPR) is used to construct a reduced-order agent model, and six significance parameters are screened by sensitivity analysis to achieve efficient evaluation and optimization of physical hyperparameters. Moreover, virtual fault signals are generated based on the initial hyperparameters and compared with the actual signals, and the hyperparameters are optimized using the Firefly algorithm with the time–frequency domain relevance error threshold as the objective function. The time–frequency domain relevance error is continuously calculated through real-time simulation, and the saliency parameters are dynamically updated to ensure that the physical and actual working conditions are consistent. The experiments show that the diagnosis accuracy under fault-free data learning is up to 94.5 %, and 92.38 % is maintained under −2dB noise, which comprehensively surpasses the existing methods and verifies the advancement of the sound-vibration signal fusion strategy and digital twinning of multi-marginal physical information.