LLM-Based Reliability Evaluation and Predictive Maintenance over Complex Product Lifecycle

Complex products such as aero-engines are characterized by long manufacturing chains, strong cross-stage coupling, and multi-source heterogeneous data, which often lead to inconsistent product quality, degraded reliability, and high operation and maintenance costs. To address these challenges, this paper proposes a full lifecycle reliability evaluation and predictive maintenance method for complex products using large language models (LLMs). In contrast to existing methods that rely on isolated stage-wise analysis or single-source data, the proposed method integrates multi-source information from the design, production, assembly, and operation stages into a unified dataspace by exploiting the semantic understanding and text-processing capabilities of LLMs. Based on stage-specific failure characteristics, adaptive statistical models are employed to construct component-level reliability models, enabling dynamic reliability evaluation throughout the entire lifecycle. Furthermore, a safety-economic multi-objective optimization model is established, considering reliability and maintenance costs. The optimal predictive maintenance strategy is obtained using multi-objective particle swarm optimization. A case study based on a Chinese aero-engine manufacturer demonstrates that the proposed method effectively improves system reliability while reducing maintenance costs and failure frequency. This work enables lifecycle reliability management and maintenance decision-making for high-precision complex products.