Operations & maintenance (O&M) of wind turbines (both onshore and offshore) are heavily affected by weather conditions, particularly wind conditions. Current O&M models focused mainly on negative impacts of wind conditions on turbine reliability and maintenance, while ignoring potential maintenance opportunities emerging from dynamic wind velocities. This article addresses this issue by constructing a novel weather-centered O&M framework, integrating wind impacts on: (a) energy production, and (b) maintenance plans. Both the positive (maintenance opportunities) and negative impacts (maintenance delays) of wind conditions are quantified in the framework. Accordingly, a weather-centered opportunistic maintenance policy is developed to enable a flexible maintenance resource allocation. The maintenance model is formulated, and analytical properties regarding optimal maintenance ages are discussed. Furthermore, the net revenue of wind turbines is evaluated using the performance-based contracting (PBC), which captures dynamics of both power generation and operational costs. Experimental results demonstrate the superior performance of our framework in revenue improvement, particularly when facing high failure risks and production losses.

The mission abort is an effective action to reduce the risk of casualties and enhance the survivability of mission-oriented systems, such as aircrafts, submarines, and unmanned aerial vehicles (UAV). A key target of operators in real mission environment is to strive for balance between the success possibility of a mission and system survivability (SS), via elaborate mission abort plans. In this article, we design mission abort policies based on two crucial information: 1) degradation degree of monitored health features, and 2) system age. Accordingly, the operators may abort the mission if the degradation attains a preset control limit at early system ages, or continue the task otherwise. We carry out loss analysis to determine the optimal abort action, by linking MSP and SS. Structural insights regarding the optimality of degradation control limit as well as age threshold are explored. For a comparative purpose, the performances of some heuristic abort policies are analytically evaluated. We make contribution by scheduling mission abort plans harnessing both condition monitoring and age information, which promotes the timeliness and robustness of risk control. A case study on inertial navigation systems of UAV executing line inspection missions is used to illustrate the applicability and superiority of the proposed abort policies.

Recent years have witnessed the prominent advancements of deep learning (DL) in the arsenal of prognostics and health management. However, the prognostic uncertainty problem extensively existed in industrial devices is not addressed by most DL approaches. This article formulates a novel Bayesian Deep Learning (BDL) framework to characterize the prognostic uncertainties. A distinguished advantage of the framework is its capacity of capturing the comprehensive effects of two critical uncertainties: 1) epistemic uncertainty, accounting for the uncertainty in the model, and 2) aleatoric uncertainty, representing the impact of random disturbance, such as measurement errors. The former arises from the variability of the model weights, and the latter is characterized by selected lifetime distributions. We integrate both uncertainties by defining BDL as priors of lifetime parameters. A sequential Bayesian boosting algorithm is executed to improve the estimation accuracy and compress the credible intervals. The superior prediction performance of our framework is validated by a real-world dataset collected from hydraulic mechanisms of circuit breakers.

The mission abort is an effective action to reduce the risk of casualties and enhance the survivability of missionbased systems such as aircrafts, submarines, and unmanned aerial vehicles (UAVs). A main task in real operations is to strive for balance between the mission reliability and the system survivability via elaborate mission abort plans. In this study, we design the optimal mission abort policies based on the information of early-warning signals, which indicates the possible forthcoming fatal malfunction. Depending on the acquisition time of such information, the operator may immediately abort the mission, or ignore the information and continue the task. Within the framework of a constant mission duration, we carry out an economic analysis for the above problem. The optimal abort decision that minimizes the expected total economic loss is investigated. We further extend the proposed model to the scenario of a random mission duration and derive the corresponding optimal abort decisions. A case study on a UAV executing power-grid inspection missions is used to illustrate the applicability of the abort policies.

Condition-based maintenance (CBM), as a key component of asset health management, is crucial to enhance the operational safety and availability of diverse mechatronic systems, such as railway vehicles, wind power equipment, nuclear devices, etc. A common phenomenon observed in CBM is the existence of dispersibility regarding degradation-induced failure threshold, which affects the precision of maintenance decisions. This article addresses such challenges by scheduling a prognosis-centered intelligent CBM policy, which harnesses dynamic lifetime information to support both scheduled and opportunistic maintenance decision-making. The degradation is characterized by a generalized-form stochastic process, and the lifetime distribution is assessed through the fusion of multiple uncertainties. A dynamic reliability criterion is set to determine whether and when to postpone maintenance, whose interval is controlled by the remaining lifetime as well as an optimizable safety coefficient. The postponement interval, in turn, enables the planning of opportunistic maintenance to mitigate system downtime. The operational cost rate is minimized through the joint optimization of the inspection interval, conditional reliability threshold, and safety coefficient. The superiorities of the proposed policy over some conventional/heuristic maintenance policies are demonstrated by a case study on filed maintenance planning of high-speed train bearing.

Intelligent maintenance powered by advanced sensor technology is crucial to ensure the safe and reliable operation of wind turbines. Most maintenance models are scheduled solely based on age/degradation conditions while ignoring the dynamics of wind conditions and residual lifetime that significantly affect maintenance executions. This article addresses such challenges by constructing a dynamic age-state-dependent intelligent opportunistic maintenance framework that is capable of integrating 1) degradation and age state, 2) estimation of remaining lifetime, and 3) both the positive (extra maintenance opportunities) and negative impacts (maintenance delays) of wind conditions. Specially, component-level maintenance is allowed to be postponed to balance lifetime extension and resource allocation, whose implementation interval is controlled by real-time estimations of lifetime and dynamic wind velocities. Moreover, both wind- and health-centered opportunistic maintenance are incorporated to mitigate power generation losses. The applicability and superiority of the proposed framework are validated by a case study on an Ontario wind farm.