When the operating conditions of a hydraulic turbine bearing are dynamically adjusted under varying loads, it often leads to aggravated shaft vibrations and seal failures, which can result in unplanned downtime and substantial economic losses. To address this issue, a fault diagnosis and maintenance system, named signal large language model (LLM), has been developed. In terms of enhancing diagnostic reliability, a dual-channel fusion strategy, integrating both long-term and short-term time windows, is employed to simultaneously capture global and instantaneous features, improving diagnostic accuracy by 5% compared to previous methods. To improve maintenance efficiency, this study proposes a trusted maintenance planning strategy that combines LLMs with neural network-based communication, creating a closed-loop process from diagnosis to maintenance execution. Comparative experiments confirm the system’s superior performance in terms of maintenance efficiency.
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