针对伺服机构不同故障局部观测响应相似、机理差异隐含于多源变量关联和动态演化过程，导致单一判别路径难以稳定区分复杂故障模式的问题，提出一种混合专家信号—语义桥接伺服机构故障诊断方法。该方法首先对位移、电流、电压和温升等多源观测信号进行统一表征，并通过语义原型对齐建立信号特征与故障类型、故障机理、故障部位等诊断语义之间的关联；然后引入故障机理约束的混合专家诊断模型，结合局部时序上下文增强和门控路由形成差异化判别路径；最后将层级诊断结果组织为结构化输出。仿真实验结果表明，该方法能够提高伺服机构复杂故障模式的识别性能和层级诊断一致性，为智能故障诊断与结构化诊断表达提供了新的实现思路。

To address the difficulty that local observation responses of different faults in servo mechanisms are similar, while mechanism differences are embedded in multi-source variable correlations and dynamic evolution processes, a servo mechanism fault diagnosis method with Mixture-of-Experts signal-semantic bridging is proposed. First, multi-source observation signals, including displacement, current, voltage and temperature rise, are uniformly represented, and semantic prototype alignment is used to establish the association between signal features and diagnostic semantics such as fault type, fault mechanism and fault location. Then, a fault-mechanism-constrained Mixture-of-Experts diagnosis model is introduced, in which local temporal context enhancement and gated routing are combined to form differentiated discriminative paths. Finally, hierarchical diagnosis results are organized into structured outputs. Simulation results show that the proposed method improves the recognition performance and hierarchical diagnosis consistency of complex fault modes in servo mechanisms, providing a new implementation approach for intelligent fault diagnosis and structured diagnostic expression.