The real-time and reliable transmission of data packets is a critical foundation for ensuring Internet of Things applications. Therefore, how to ensure the timely reliability of wireless sensor networks has become a hotspot. However, existing timely reliability models often overlook the impacts of energy depletion and channel capacity randomness on wireless transmission. Additionally, most evaluations focus on single-hop, single-path scenarios, while practical data transmission typically requires multi-hop and multi-path strategies. To overcome the above shortcomings, this study conducts the timely reliability evaluation and optimization of wireless sensor networks considering channel capacity randomness and energy depletion. First, focusing on data transmission delay modeling, this study emphasizes the effects of energy depletion and channel capacity randomness on wireless data transmission, and further proposes a timely reliability evaluation model based on the G/G/1 queuing model. Secondly, to tackle the computational challenges of multi-hop and multi-path data transmission, this study proposes a timely reliability solving algorithm that integrates the binary decision diagrams with Monte Carlo simulation.. Building on these foundations, this study develops a periodic optimization model for signal transmission power, balancing sensor lifetime and network transmission performance. Finally, taking the military Internet as an example, the effectiveness of the proposed method is verified.

Condition-based maintenance (CBM) powered by inspection/monitoring technology is crucial to guarantee safety and economical operations of various industrial assets. The implementation of prevailing CBM procedures for large-scale heterogeneous systems, however, is increasingly challenged by model intractability and computational cost stemming from the synergistic effect of information completeness and structure complexity. In this article, we innovatively devises a tractable CBM model for multicomponent continuously degrading systems under nonperfect inspection information, which is applicable to heterogeneous system structure and arbitrary hierarchical maintenance actions. The maintenance optimization problem of interest constitutes a continuous-state partially observable Markov-decision process applicable to heterogeneous system structures. A series of structure properties associated with systematic conditional reliability and accessibility of optimal solution are established, following which a multiagent reinforcement learning model governed by partial-independent parameter-sharing mechanism is employed to allow for solution search under continuous state–action space. A customized proximal policy algorithm is then leveraged to facilitate efficient agent training by diminishing the cure of dimension. Comparative experiments conducted on train wheel treads verify the superior model performance over cost control and computational efficiency improvement.

Inspection errors are extensively reported in equipment health management due to multisource noises and technical limitations, particularly in hidden defect diagnosis of the multistage failure process. This article proposes a state-age-dependent maintenance and spare control strategy to compensate inspection-error-induced risk (attributed to both false positive and false negative) during defect identification. Specifically, a dual-phase adaptive inspection accommodating health variation is scheduled, following which both spare ordering and replacement are postponed to compensate implication of false-positive error. In addition, age-based replacement supported by preponed standard ordering is implemented promptly to alleviate false-negative error impact. To mitigate downtime losses, a dynamic selection mechanism upon failure occurrence between urgent and standard orderings is executed. The long-run operational cost rate is minimized by the joint optimization of postponed intervals of ordering and replacement, as well as the second-phase inspection interval. The model applicability is demonstrated through numerical experiments conducted on high-speed train bogie bearings.

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.

Load sharing is a common mechanism in redundant systems, which possesses significant impacts on operational safety. Although failure analyses of such systems are abundant, the risk mitigation methodology through elaborate maintenance scheduling is still insufficient. To address such deficiency, we innovatively designed a two-threshold group maintenance policy for$k$-out-of-$n$load-sharing systems. Such policy aims to save costs by mitigating the failure risks and minimizing the disturbance of maintenance activities to ensure continuous operation. Compared with existing studies, the proposed policy and modeling approach have two prominent superiorities. First, they are not limited to basic two-component systems which are addressed most. Second, arbitrary lifetime distribution is allowed in maintenance decision-making, which effectively expands the realistic application scope. To relieve the computation burden arising therefrom, a surrogate-aided approach is proposed to enhance the practicability for large-scale systems. We demonstrate the generality and superior performance of the approach through numerical experiments.

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.

Industrial assets exposed to random environment often exhibit complex deterioration mechanisms with health status variations. In actual field operation, hidden defect signals are usually crucial indicators of upcoming malfunctions and also reminders of proactive maintenance executions. Despite the extensive applications of defect-centered maintenance, in the literature, little attempt has: a) captured the impact of random environments on health variation and restoration, and b) explored the differentiated functions of maintenance windows in separate states. This article addresses these challenges by introducing a state-based maintenance policy with multifunctional maintenance windows. The impact of environmental disturbance on both defect initialization and propagation is characterized by random increment of the state transition rate as well as probabilistic malfunction risk. Three types of maintenance windows (regular, opportunistic, and postponed) are scheduled to ensure a flexible scheduling of inspection and spare part resources. Importantly, the function of opportunistic window is state-based, defect identification when normal and removal when defective. The objective is to minimize the cost rate via the joint optimization of inspection interval, postponed interval, and opportunistic threshold. Experimental studies demonstrate the superior performance of this policy over some conventional policies.

The mission abort is an effective action to reduce the risk of casualties and enhance the survivability of mission-based 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 paper, 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.