Albero dei guasti dinamico: metodologie e applicazione a un caso studio

La trattazione riporta lo studio e l'applicazione a un caso studio, della valutazione del rischio tramite la costruzione dell'albero dei guasti con approccio di tipo dinamico, distaccandosi dall'utilizzo tradizionale di tipo statico

Reliability assessment of distribution systems incorporating feeder restoration actions

This paper proposes a computational methodology for the evaluation of the IEEE reliability indices for distribution systems considering distribution system restoration. The goal of the proposed methodology is to move from a reliability assessment based on historical data to a computational approach. The developed tool allows the evaluation of the Service Restoration benefits, in terms of customers interruption duration in case of fault occurrences. Distribution System Restoration (DSR) is aimed at restoring loads after a fault by altering the topological structure of the distribution network while meeting electrical and operational constraints. The Spanning Tree Search algorithm is used to identify a post-outage topology that will restore the maximal amount of load with a minimal number of switching operations. The goal of the proposed tool is to determine the optimal switching sequences for the restoration process. The reliability indices incorporates contributions of all possible faults effects

On Minimal Cut Sets Representation with Binary Decision Diagrams

Since their introduction in form of a canonical representation of logical functions, the Binary Decision Diagrams (BDDs) gained a wide acceptance in numerous industrial applications. This paper summarizes the properties of BDD representation of Minimal Cut Sets (MCS) of Fault Tree (FT) models most typically encountered in nuclear energetics. Cut sets from MCS are defined as paths from the top BDD node to terminal nodes in the BDD, on which a quantitative and qualitative FT analysis (FTA) is performed. The core of the FTA on the BDDs is performed with help of two fundamental algorithms, one for conditional probability evaluation and another for the selection of cut sets. The accuracy of conditional probability evaluation represents an essential feature for an unbiased quantitative analysis, such as the top event probability or the determination of event importance measures. The cut set selection algorithm is shown in a generic version introducing logical predicates for its selection criteria. As it is known, the efficiency of depicted algorithms depends only on the number of BDD nodes used for the FT representation. In order to appraise the compactness of the BDD representation of FT models, their characteristics have herein been evaluated on several real-life models from the Nuclear Power Plant Krško. The extraordinariness of the compactness of the BDD representation reflects in its ability to implement advanced dynamic analysis (i.e. what-if) of FT models. The efficiency of such an approach is recognized by commercial vendors upgrading their FT Tools to new versions by implementing BDD based algorithms

Model-based dependability analysis : state-of-the-art, challenges and future outlook

Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis

Approximate dynamic fault tree calculations for modelling water supply risks

Traditional fault tree analysis is not always sufficient when analysing complex systems. To overcome the limitations dynamic fault tree (DFT) analysis is suggested in the literature as well as different approaches for how to solve DFTs. For added value in fault tree analysis, approximate DFT calculations based on a Markovian approach are presented and evaluated here. The approximate DFT calculations are performed using standard Monte Carlo simulations and do not require simulations of the full Markov models, which simplifies model building and in particular calculations. It is shown how to extend the calculations of the traditional OR- and AND-gates, so that information is available on the failure probability, the failure rate and the mean downtime at all levels in the fault tree. Two additional logic gates are presented that make it possible to model a system’s ability to compensate for failures. This work was initiated to enable correct analyses of water supply risks. Drinking water systems are typically complex with an inherent ability to compensate for failures that is not easily modelled using traditional logic gates. The approximate DFT calculations are compared to results from simulations of theorresponding Markov models for three water supply examples. For the traditional OR- and AND-gates, and one gate modelling compensation, the errors in the results are small. For the other gate modelling compensation, the error increases with the number of compensating components. The errors are, however, in most cases acceptable with respect to uncertainties in input data. The approximate DFT calculations improve the capabilities of fault tree analysis of drinking water systems since they provide additional and important information and are simple and practically applicable

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded

Reliability Evaluation of NPP’s Power Supply System Based on Improved GO-FLOW Method

NPP’s power supply system is repairable and there is common cause failure between the components. The repair rate is introduced and total signaling is considered in the improved GO-FLOW method, aimed at reliability analysis for NPP’s power supply system. Traditional GO-FLOW operators’ algorithms are improved. Comprehensively considering the effect of total signaling flow in the power supply system, the equivalent reliability parameter model and common cause failure probability model of multimodal repairable components are constructed. The improved GO-FLOW model of NPP’s power supply system is set up. Based on the proposed model, components’ reliability parameters are computed. The failure probability time-varying trend in thirty years, respectively, of NPP’s offsite power source and power supply system, is simulated and analyzed. Compared with calculation results of dynamic fault tree analysis method, the validity and the simplicity of the improved GO-FLOW method are verified. The effectiveness and applicability of the improved GO-FLOW model for NPP’s power supply system are proved by simulation examples

Application of artificial neural network and dynamic fault tree analysis to enhance reliability in predictive ship machinery health condition monitoring

The electric power generation system of most ships is powered by a group of diesel generators generally with redundancy to accommodate peak load periods or critical situations. Blackouts onboard ships portents a potential danger to navigation as well as the security and safety of the ship. Thus, understanding the factors affecting the reliability of individual diesel generators and the most critical component to failure is key to ensuring reliable performance of the generators. Therefore, this study was conducted on diesel power generation plant consisting of four Marine Diesel Generators onboard an Offshore Patrol Vessel (OPV). Findings indicates relatively low reliability, of less than 60 per cent within the first 24 months of the 78 operational months data analysed. Similarly, reliability importance measures were adopted to identify Critical components which contribute at least 40 per cent of failures on the sub systems of the diesel generators. The use of dynamic spare gates in the dynamic fault tree analysis has highlighted possible improvements through maintenance action or use of sensors to improve sub-system as well as individual diesel generator’s reliability. Additionally, Artificial Neural Networks classification using unsupervised learning was conducted to identify patterns in the data that signifies the onset of performance degradation in the diesel generators