面 向 机 械 振 动 信 号 的 自 主 信 号 处 理 大 语 言 模 型 智 能 体

Vibration signal analysis is a cornerstone of machine condition monitoring and fault diagnosis，yet it faces a central dilemma. Traditional expert systems have rigid workflows，while end-to-end deep models，despite their adaptive learning abilities，suffer from being ‘black boxes’ with insufficient reproducibility. This paper introduces a neuro-symbolic multi-agent framework for autonomous signal processing. The framework utilizes a large language model (LLM) as a central decision-maker， coordinating a toolbox of interpretable，symbolic signal processing operators to enable autonomous vibration signal analysis and diagnosis. The framework adopts a Plan-Execute-Review multi-agent architecture to iteratively optimize the signal processing decision chain. To ensure the logical consistency of the planning and prevent incorrect operator calls，all operators are formally regulated based on their dimensional and semantic transformation properties. Specifically，they are constrained by semantic information for the LLM to comprehend. Validation on bearing fault diagnosis datasets shows that this framework can autonomously generate signal processing decision chains with clear physical meaning and has successfully reproduced expert-level， interpretable diagnostic algorithms such as ‘envelope spectrum-kurtosis’. In single-domain tests on the Tsinghua University bearing dataset，the Gemini-2.5-pro version reached an accuracy of 97.8%. In cross-domain tests on the University of Ottawa variable-speed dataset， the framework， trained solely on ‘acceleration’ and ‘deceleration’ conditions， achieved 99.3% accuracy on unseen conditions，proving its generalization ability. This research provides a promising new paradigm for building trustworthy，reproducible，and scalable next-generation intelligent diagnostic systems.