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We propose an analytic, time-variant model that conservatively evaluates the increase in reliability achievable when a component is equipped with a Prognostics and Health Management system of known performance metrics. The reliability model builds on metrics of literature and is applicable to different industrial contexts. A simulated case study concerning crack propagation in a mechanical component is considered to validate the proposed model.

We introduce a new perspective to improve the reliability of a network, which aims at finding cost-effective portfolios of Prognostics and Health Management (PHM) systems to be installed throughout the network. To do this, we estimate the reliability of the single network element equipped with a PHM system, whose prognostic performance is measured in terms of the − α λ performance, false positive and false negative metrics. Then, we apply genetic algorithms for finding the portfolios of PHM systems to be installed on the network elements, which are optimal with respect to cost and a global reliability efficiency index of the network. The workbench case study of the IEEE 14 bus network is considered as application.

We consider the maintenance process of gas turbines used in the Oil and Gas industry: the capital parts are first removed from the gas turbines and replaced by parts of the same type taken from the warehouse; then, they are repaired at the workshop and returned to the warehouse for use in future maintenance events. Experience-based rules are used to manage the flow of the parts for a profitable gas turbine operation. In this article, we formalize the part flow management as a sequential decision problem and propose reinforcement learning for its solution. An application to a scaled-down case study derived from real industrial practice shows that reinforcement learning can find policies outperforming those based on experience-based rules.

For maintenance of gas turbines (GTs) in oil and gas applications, capital parts are removed and replaced by parts of the same type taken from the warehouse. When the removed parts are found not broken, they are repaired at the workshop and returned to the warehouse, ready to be used in future maintenance. The management of this flow is of great importance for the profitability of a GT plant. In this paper, we adopt a previously developed formalized framework of the part flow and reinforcement learning (RL) to optimize part flow management. The formal framework and RL algorithm are extended to account for the stochastic failure process of the involved parts. An application to a scaled-down case study derived from an industrial application is illustrated.

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Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synthetic case study and a real case study concerning the monitoring of the tank pressure of an aero derivative gas turbine lube oil system. The CM model provides satisfactory performances in terms of classification accuracy, while remarkably reducing the expert efforts for data labeling and model (periodic) updating.

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