Weak fault feature extraction and system fault analysis under strong noise

To tackle the intractable problems including weak fault feature extraction and evolution uncertainty quantification for complex systems in strong noise environments, a novel method for weak fault diagnosis and evolution analysis is proposed. This method integrates the fuzzy structured element (FSE), cloud model (CM), and Space Fault Network (SFN). The method centers on adaptive wavelet denoising, fault feature cloudification, and SFN probability propagation. The fault signal under strong noise is reconstructed by optimizing the wavelet threshold with the FSE. The uncertainty encapsulation of the peak factor of fault features is realized based on the CM to establish the feature CM. The fault event topology is constructed relying on the SFN. The quantitative transfer of uncertainty in the fault evolution is achieved combined with cloud algebra. Verified by the inner ring pitting fault of axle box bearings, the results demonstrate that the proposed method can extract the fault characteristic frequency of 250.5 Hz. The derived fault probability CM (0.680, 0.059, 0.023) accurately quantifies the system risk level. This result is consistent with the actual fault evolution law in engineering practice. This method provides technical support for early fault warning and maintenance of complex industrial system. Furthermore, comparative experiments confirm its superiority over traditional methods in noise suppression and feature retention. Parameter analysis is also discussed to improve engineering generalization.