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Predictive maintenance (PdM) strategies constitute an essential component of prognostics and health management (PHM), enhancing device safety and availability while concurrently reducing repair costs. The implementation of PdM typically involves two primary steps: remaining useful life (RUL) prediction and the development of maintenance strategies. However, the limited accuracy of RUL prediction due to the absence of historical degradation information coupled with maintenance decisions normally made independently of the RUL prediction process present significant challenges in developing effective PdM strategies. In this article, a PdM strategy for a single device is presented. First, this article proposes an RUL prediction method based on data augmentation and similarity measures. In data augmentation, Gaussian process regression (GPR) and uncertain simulation are used to generate additional simulated degradation trajectories for RUL prediction. Subsequently, RUL prediction information is obtained using similarity matching. Following that, building on RUL prediction information, we propose a multiobjective optimization PdM strategy. This strategy considers both device availability and maintenance cost to guide maintenance tasks under actual working conditions. Finally, the effectiveness of the proposed PdM strategy is verified by taking a railway gyroscope as an example. The experimental results demonstrate that the proposed RUL prediction method exhibits an average absolute error improvement of at least 10.89% and an average probability value enhancement of at least 7.96% compared with other prediction methods, such as uncertain process simulation. Furthermore, the optimal maintenance times identified by the proposed PdM strategy are all located before the true RUL, indicating that the strategy is effective in preventing device failures.

Heterogeneous traffic agents consisting of human-driven vehicle (HDV) and connected-automated vehicle (CAV) appear on the road and form a mixed traffic environment. When combining the characteristics of CAVs and HDVs, it's still an open issue of investigating how heterogeneous traffic agents will execute lane-changing. With respect to the coupling process in the lane-changing behaviour of vehicles, this paper derives the lane changing optimization method for CAVs under different penetration rates. This method captures not only the full spectrum of CAV penetration rates but also all types of vehicle platoon. Firstly, the different car-following and lane-changing models are used to capture the characteristics of different types of vehicles. Moreover, For CAV, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Secondly, the framework for the coupling analysis of CAV lane-changing behaviour and mixed traffic stability is derived. Then, this study proposes and examines optimal control strategies based on numerical simulation. These consist of a longitudinal phase, in which the proposed strategies improve the operational efficiency of the overall mixed traffic flow, and a lateral phase, in which they safely change lanes. The results show that they improve the comfort, efficiency, safety, and stability of the heterogeneous traffic agents compared to the maneuvers performed by the baseline. This method can serve as a useful and simple decision tool for future CAV lane management.