LAUSR

To address the challenge of time-varying reliability assessment for double-span rotor systems under misalignment faults and load-sharing conditions, a time-varying reliability modeling method based on neural networks and reliability velocity mapping is proposed in this paper. By establishing a system dynamics model coupled with misalignment fault, a feedforward neural network surrogate model is constructed to efficiently predict stochastic stress responses, overcoming the limitations of high computational cost and difficulty in probabilistic analysis inherent in traditional finite element methods. Furthermore, by introducing the concept of reliability velocity, an intelligent mapping from independent systems to dependent systems is established, significantly enhancing the assessment accuracy of system time-varying reliability under small-sample conditions. Case study validation demonstrates that the proposed method can accurately capture the system degradation behavior under load-sharing and failure dependency mechanisms, providing a theoretical foundation for reliability analysis and intelligent operation and maintenance of rotor systems.