System vibration characteristics and fault evolution evaluation based on multimodal data fusion

Abstract

To address the problems of one-sided modal information, unclear fault evolution, and insufficient support for early fault diagnosis warning of complex systems, a method for evaluating system vibration characteristics and fault evolution based on multimodal data fusion is proposed. With multimodal data fusion, factor space mapping, fault evolution network modeling, and probabilistic evaluation as the core, the unified characterization of heterogeneous data to fault-influencing factors is realized through feature extraction and hierarchical mapping of multi-source data, including vibration, acoustic emission, and oil analysis. Based on the Space Fault Network (SFN), the topological relationship and probability transfer model of fault events are constructed. Combined with evolutionary entropy analysis, an evaluation system of failure probability-evolutionary entropy and a hierarchical early warning mechanism are formed. Taking the axle box bearing as an example, with thresholds determined by full-life cycle fault data fitting and engineering experience, the system fault is identified as the attention state at 80 hours and the high-risk state at 100 hours, which is consistent with the law of gradual fault evolution and engineering practicability. The proposed method forms a failure probability–evolutionary entropy dual-index evaluation system and a hierarchical early warning mechanism with strong physical interpretability, providing reliable technical support for reliability evaluation and predictive maintenance of complex mechanical systems.