A condition-based maintenance decision-making framework for multi-component systems is proposed in this work by integrating dynamic Bayesian network (DBN) with proportional hazards model (PHM). The framework is designed to address the challenge of handling mixed failure types and complex failure dependencies, which often lead to inaccurate maintenance decisions in existing methods. In this integrated model, the DBN captures the failure evolution and both dynamic and static dependencies among components, while the PHM enhances the capability to characterize mixed failure interactions, thereby enabling the coverage of three common types of failure dependencies in multi-component systems. The model is formulated and solved using a finite-horizon Markov decision process (MDP), with the optimal maintenance strategy obtained by maximizing the total expected reward. Numerical case studies demonstrate the framework’s flexibility in handling mixed failures and complex dependencies, showing its potential to effectively support condition-based maintenance decision-making for complex multi-component systems.

Three kinds of phase modulation methods adopted in the rotation rate sensor based on microsphere resonator are investigated. The performance of these modulation methods were compared and analyzed. As a result, sinusoidal wave modulation was employed and the sidebands demodulation has been realized in the sensing system which provide a valuable reference for signal processing of rotation rate sensors.