The atmospheric corrosion of metallic materials causes great economic loss every year worldwide. Thus, it is meaningful to predict the corrosion loss in different field environments. Generally, the corrosion prediction method includes three parts of work: the modelling of the corrosive environment, the calibration of the corrosion effects, and the establishment of the corrosion kinetics. This paper gives an overview of the existing methods as well as promising tools and technologies which can be used in corrosion prediction. The basic corrosion kinetic model is the power function model and it is accurate for short-term corrosion process. As for the long-term corrosion process, the general linear models are more appropriate as they consider the protective effect of the corrosion products. Most corrosion effect models correlate the environmental variables, which are characterized by the annual average value in most cases, with corrosion parameters by linear equations which is known as the dose-response function. Apart from these conventional methods, some mathematical and numerical methods are also appropriate for corrosion prediction. The corrosive environment can be described by statistical distributions, time-varying functions and even geographic information system (GIS), while the corrosion effect can be captured via response surface models and statistical learning methods.

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.

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.