Advanced methods for loss-of-flow accident precursors identification in a superconducting magnet cryogenic cooling circuit

In nuclear fusion systems, such as ITER, Superconducting Magnets (SMs) will be employed to magnetically confine the plasma. A Superconducting Magnet Cryogenic Cooling Circuit (SMCCC) must keep the SMs at cryogenic temperature to preserve their superconductive properties. Thus, a Loss-Of-Flow Accident (LOFA) in the SMCCC is to be avoided. In this work, a three-step methodology for the prompt identification of LOFA precursors (i.e., those component failures leading to a LOFA) is developed. First, accident scenarios are randomly generated by Monte Carlo sampling of the SMCCC components failures and the corresponding transient system response is simulated by a deterministic thermal-hydraulic code. In this phase, fast-running Proper Orthogonal Decomposition (POD)based Kriging metamodels, adaptively trained to mimic the behavior of the detailed long-running code, are employed to reduce the associated computational burden. Second, the scenarios generated are grouped by a Spectral Clustering (SC) embedding the Fuzzy C-Means (FCM), in order to characterize the principal patterns of system evolution towards abnormal conditions (e.g., a LOFA). Third, an On-line Supervised Spectral Clustering (OSSC) approach is developed to assign signals measured during plant operation to one of the prototypical clusters identified, which may reveal the corresponding LOFA precursors (in terms of combinations of failed SMCCC components). The devised method is applied to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid. Results show that the approach developed timely identifies 95% of LOFA events and approximately 80% of the corresponding precursors

Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

Maintenance optimization in industry 4.0

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience

A sensitivity analysis for the adequacy assessment of a multi-state physics modeling approach for reliability analysis

In this work, a moment-independent Sensitivity Analysis (SA) based on Hellinger distance and Kullback-Leibler divergence is proposed to identify the component of a system most affecting its reliability (Diaconis et al., 1982; Gibbs et al., 2002; Di Maio et al., 2014). This result is used to adequately allocate modeling efforts on the most important component that, therefore, deserves a component-level Multi-State Physics Modeling (MSPM) to be integrated into a system-level model, to estimate the system failure probability

Robust multi-objective optimization of safety barriers performance parameters for NaTech scenarios risk assessment and management

Safety barriers are to be designed to bring the largest benefit in terms of accidental scenarios consequences mitigation at the most reasonable cost. In this paper, we formulate the problem of the identification of the optimal performance parameters of the barriers that can at the same time allow for the consequences mitigation of Natural Technological (NaTech) accidental scenarios at reasonable cost as a Multi-Objective Optimization (MOO) problem. The MOO is solved for a case study of literature, consisting in a chemical facility composed by three tanks filled with flammable substances and equipped with six safety barriers (active, passive and procedural), exposed to NaTech scenarios triggered by either severe floods or earthquakes. The performance of the barriers is evaluated by a phenomenological dynamic model that mimics the realistic response of the system. The uncertainty of the relevant parameters of the model (i.e., the response time of active and procedural barriers and the effectiveness of the barriers) is accounted for in the optimization, to provide robust solutions. Results for this case study suggest that the NaTech risk is optimally managed by improving the performances of four-out-of-six barriers (three active and one passive). Practical guidelines are provided to retrofit the safety barriers design

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED)

Seismic resilience assessment of Small Modular Reactors by a Three-loop Monte Carlo Simulation

We develop a three-loop Monte Carlo Simulation (MCS) framework for the seismic resilience assessment of Small Modular Reactors (SMRs), embedding Probabilistic Seismic Hazard Analysis (PSHA), seismic fragility evaluation and multiple SMR units accident sequence analysis. A set of metrics are computed to capture different aspects of SMR resilience to earthquakes, specifically the ability to withstand seismic disruption, mitigate consequences and restore normal operation. The MCS framework allows accounting for the aleatory and epistemic uncertainties of the PSHA and fragility parameters. An application is given with regards to an advanced Nuclear Power Plant (aNPP) consisting of four reactor units of NuScale SMR design. A comparison is made to a conventional NPP (cNPP), i.e., a typical large reactor of equivalent generation capacity. Both plants are fictitiously located on the Garigliano nuclear site (southern Italy). The results show that resilient features of SMRs overcome cNPPs in terms of post-accident scenario mitigation and restoration capabilities

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies

Ensemble of Artificial Neural Networks for Approximating the Survival Signature of Critical Infrastructures

Survival signature can be useful for the reliability assessment of critical infrastructures. However, analytical calculation and Monte Carlo Simulation (MCS) are not feasible for approximating the survival signature of large infrastructures, because of the complexity and computational demand due to the large number of components. In this case, efficient and accurate approximations are sought. In this paper we formulate the survival signature approximation problem as a missing data problem. An ensemble of artificial neural networks (ANNs) is trained on a set of survival signatures obtained by MCS. The ensemble of trained ANNs is, then, used to retrieve the missing values of the survival signature. A numerical example is worked out and recommendations are given to design the ensemble of ANNs for large-scale, real-world infrastructures. The electricity grid of Great Britain, the New England power grid (IEEE 39-Bus Case), the reduced Berlin metro system and the approximated American Power System (IEEE 118-Bus Case) are, then, eventually, analyzed as particular case studies

A fuzzy expectation maximization based method for estimating the parameters of a multi-state degradation model from imprecise maintenance outcomes

Multi-State (MS) reliability models are used in practice to describe the evolution of degradation in industrial components and systems. To estimate the MS model parameters, we propose a method based on the Fuzzy Expectation-Maximization (FEM) algorithm, which integrates the evidence of the field inspection outcomes with information taken from the maintenance operators about the transition times from one state to another. Possibility distributions are used to describe the imprecision in the expert statements. A procedure for estimating the Remaining Useful Life (RUL) based on the MS model and conditional on such imprecise evidence is, then, developed. The proposed method is applied to a case study concerning the degradation of pipe welds in the coolant system of a Nuclear Power Plant (NPP). The obtained results show that the combination of field data with expert knowledge can allow reducing the uncertainty in degradation estimation and RUL prediction