1,721,098 research outputs found
Agent-based modeling for energy supply chain resilience analysis
No full textEnergy Supply Chains (ESCs) are complex systems made up of numerous suppliers interacting with each other and with the environment. From the complexity of these interdependences, risk scenarios can originate in an unpredictable way, threatening the normal operation of the entire ESC and endangering its supply capability. New methodologies are, then, being developed for carrying out ESC risk and resilience analyses. In this study, we rely on agent-based modeling (ABM) to build a multi-layer ESC model for analyzing its resilience. Every element in the ESC is simulated as an agent implementing basic functions like sending and receiving orders, and production. We simulate different disruption scenarios and recovery strategies to investigate the essential factors influencing the resilience of the overall ESC
Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0
No full textThe Industry 4.0 paradigm is boosting the relevance of predictive maintenance (PdM) for manufacturing and production industries. PdM strongly relies on Internet of Things (IoT), which digitalizes the physical actions allowing human-to-human, human-to-machine, and machine-to-machine connections for intelligent perception. Several issues still need to be addressed for reaching the maturity stage for the widespread application of PdM. To do this, IoT needs to be empowered with data science capabilities, to reach the ultimate objective of digitalization, which is supporting decision making to optimally act on the physical systems. In this article, we present a comprehensive outlook of the current PdM issues, with the final aim of providing a deeper understanding of the limitations and strengths, challenges and opportunities of this dynamic maintenance paradigm. This is done through extensive research and analysis of the scientific and technical literature. On this basis, this article outlines some main research issues to be addressed for the successful development and deployment of IoT-enabled PdM in industry
Deep reinforcement learning for optimizing operation and maintenance of energy systems equipped with phm capabilities
Full text linkThe Life Cycle Cost (LCC) of energy systems including Renewable Energy Sources (RES) strongly depends on the Operation and Maintenance (O&M) costs. Nowadays, many components of these energy systems are equipped with Prognostics & Health Management (PHM) capabilities, for estimating their current and future health states. This information is intended to be used for the optimization of O&M. It is an ambitious and challenging objective as the uncertain information brought by PHM must be combined with other factors influencing O&M, such as the limited availability of maintenance crews, the variability of energy demand and production, the long-time horizons of energy systems. In this work, we formalize the O&M optimization of RES-based energy systems equipped with PHM as a sequential decision problem over a long-time horizon and we solve it by Deep Reinforcement Learning (DRL). The proposed methodology is applied to a small wind farm. Strengths and weaknesses are analyzed by means of a comparison with state-of-the-art O&M policies
Gaussian Fields for Predicting Drift of Oil and Gas Pipes
No full textIn 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
Pipe drift estimation based on the measurements of geometrical parameters from a single pipe
Full text linkPipe drift is among the most relevant quality factors of pipes for deep water applications. This is estimated through empirical models encoding geometrical parameters. In a previous work, we relied on Gaussian fields to map these parameters onto the pipe drift conformance probability. The field kernel was estimated from a set of measurements gathered from pipes of the same lot, produced at the same mill. However, in practice it is very difficult to find this homogeneous dataset. The objective of this paper is to extend the previously developed framework to estimate the drift based on the geometrical data relevant to a single pipe, only. For this, we consider known analytical kernels and infer their parameters from the single pipe measurements. Then, we estimate the actual error on the drift conformance probability estimations, considering the best fitting kernel. This error turns out to be negligible
Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning
Full text linkEquipment of renewable energy systems are being supported by Prognostics & Health Management (PHM) capabilities to estimate their current health state and predict their Remaining Useful Life (RUL). The PHM health state estimates and RUL predictions can be used for the optimization of the systems Operation and Maintenance (O&M). This is an ambitious and challenging task, which requires to consider many factors, including the availability of maintenance crews, the variability of energy demand and production, the influence of the operating conditions on equipment performance and degradation and the long time horizons of renewable energy systems usage. We develop a novel formulation of the O&M optimization as a sequential decision problem and we resort to Deep Reinforcement Learning (DRL) to solve it. The proposed solution approach combines proximal policy optimization, imitation learning, for pre-training the learning agent, and a model of the environment which describes the renewable energy system behavior. The solution approach is tested by its application to a wind farm O&M problem. The optimal solution found is shown to outperform those provided by other DRL algorithms. Also, the approach does not require to select a-priori a maintenance strategy, but, rather, it discovers the best performing policy by itself
Deep reinforcement learning based on proximal policy optimization for the maintenance of a wind farm with multiple crews
Full text linkThe 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
An interdisciplinary approach for investigating an accident originating from leakage in a gasketed bolted joint
Full text linkWe investigate an accident originating from a leak of hydrochloric acid through a gasketed bolted joint of a measuring equipment operating in aWasteWater Treatment System (WWTS). In spite of the simplicity of the failed component and of the fact that the WWTS is not classified as safety critical, the investigation requires corroborating the traditional techniques of risk analysis such as FMECA, Bow-Tie, etc., with non-standard, interdisciplinary techniques such as structural reliability and task analysis. Moreover, the identification of safety barriers for risk reduction requires applying refined modeling approaches, such as atmospheric dispersion modeling and human reliability modeling
The aramis data challenge: Prognostics and health management in evolving environments
Full text linkThe objective of the Aramis Data Challenge is the creation of a public benchmark dataset for the problem of fault detection in evolving environments. A multi-component system in which the degradation of one component accelerates the degradation processes of the other components, thus modifying their lifetime distributions and the statistical properties of the monitored signals over time is considered. Here, we provide details with respect to the Challenge definition, the data collection and the evaluation metric
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