The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.

In the oil and gas industry, empirical models are used to estimate the drift of pipes. These models encode pipe geometrical features, measured along the pipe length. If the estimated drift does not meet the operability requirements, then the pipe is rejected. This improves the quality of the purchased pipes, but strongly affects their production costs. We rely on the Gaussian fields theoretical framework to address two issues: the a priori estimation of the probability of pipes rejection and the a posteriori estimation of the drift conformance probability, given the actual measured parameters. These are fundamental pieces of information for purchasing decisions. A case study is considered to show the application of the theoretical framework. The proposed methodology is applied to real pipe measurement data, which have been opportunely rescaled to avoid the disclosure of relevant information.