Failure Interdependencies Research Articles

Dual Hardware-In-the-Loop (DHIL) platform for testing and validation of WEC subsystems

This paper presents the design process and application of a dual hardware hardware-in-the-loop (DHIL) testing platform targeting components and subsystems of wave energy conversion devices. The DHIL testing methodology combines two HIL-equipped test rigs allowing to simultaneously test components mounted on each rig and connected to the same simulation loop. The platform was therefore developed combining a HIL test rig for drivetrains with a HIL test rig for structural components.The DHIL testing platform has the capability to address multiple types of tests on critical subsystems and components within a WEC: characterization tests, for defining key performances of the equipment under test; accelerated tests, to assess qualitative and quantitative reliability features; and ultimate load testing, for survivability purposes. The overall aim of these tests is to identify weaknesses in an early design phase of device design or as a qualification activity prior the deployment of an ocean prototype. Additionally, HIL and DHIL tests can be used to assess the influence of a design update on the overall WEC model and to track failure interdependencies at a relevant scale. Finally, all of the above-mentioned activities can inform the development of a more accurate global numerical model, potentially at sub-system / critical component level, to be validated based on the test results.The definition of the test rig mechanical and electrical input specifications is dependent on the understanding of the load envelope each subsystem / component will be subject to during its lifetime. To define such envelope, a research activity modelling three different device types, three deployment sites and multiple design situations (e.g. power production, parked, shut-down etc.) led to the creation of a load database that was combined with information from WEC developers.After defining the input specifications, the rigs were designed by identifying the optimal layout, the key components and the setup of the overall test area. Analyses on mechanical, electrical and hydraulic parameters were completed to ensure the required performance of each rig could be achieved, while guaranteeing the safety during their operation.The signal processing of each rig was also analysed, with the aim of defining the minimum rig latency to allow HIL and DHIL tests to be performed. This analysis took into account the possible interface characteristics of the simulator, the sensors and the main features of the models used for real-time tests.An overview of the testing activities to be conducted within the IMPACT project will be presented in this paper. Such activities aim to de-risking the technologies at early stages and increasing their maturity through a structured process. The proposed approach has the final goal of demonstrating that the DHIL testing platform is capable to reduce capital-intensive activities, often associated with the development of large-scale prototypes, which is a critical factor for the successful development and time-to-market of a WEC.

A Combinatorial Reliability Analysis of Generic Service Function Chains in Data Center Networks

In data center networks, the reliability of Service Function Chain (SFC)—an end-to-end service presented by a chain of virtual network functions (VNFs)—is a complex and specific function of placement, configuration, and application requirements, both in hardware and software. Existing approaches to reliability analysis do not jointly consider multiple features of system components, including, (i) heterogeneity, (ii) disjointness, (iii) sharing, (iv) redundancy, and (v) failure interdependency. To this end, we develop a novel analysis of service reliability of the so-called generic SFC, consisting of n = k + r sub-SFCs, whereby k ≥ 1 and r ≥ 0 are the numbers of arbitrary placed primary and backup (redundant) sub-SFCs, respectively. Our analysis is based on combinatorics and a reduced binomial theorem—resulting in a simple approach, which, however, can be utilized to analyze rather complex SFC configurations. The analysis is practically applicable to various VNF placement strategies in arbitrary data center configurations, and topologies and can be effectively used for evaluation and optimization of reliable SFC placements.

On multi-path routing for reliable communications in failure interdependent complex networks

This paper studies several new multiple routing path computation problems in failure-interdependent complex networks such as smart grid communication network, each of which exhibits unique failure interdependency. Despite the difference of the formulation of the problems, we show that each of the problems can be reduced to another within polynomial time, and therefore they are equivalent in terms of hardness. Then, we show that they are not only $$\mathcal {NP}$$ -hard, but also cannot be approximated within a certain bound unless $${\mathcal {P}}=\mathcal {NP}$$ . Besides, we show that their decision versions to determine if there exist two failure independent paths between two given end nodes are still $$\mathcal {NP}$$ -complete. Finally, a series of heuristic algorithms are proposed to deal with the daunting hardness of the problems. Most importantly, this paper opens a new series of research problems with daunting complexity based on important real world applications.

Delineating Infrastructure Failure Interdependencies and Associated Stakeholders through News Mining: The Case of Hong Kong’s Water Pipe Bursts

AbstractThe failure of one infrastructure system could trigger cascading impacts on other interdependent infrastructures. In order to improve the management of diverse infrastructure systems, decis...

Efficient performance monitoring of building central heating system using Bayesian Network method

Central heating system faults affect building energy consumption and indoor thermal comfort significantly. The interdependencies among system components and multiple failure modes present a challenge for system health diagnostics and prognostics. A reliable diagnosis and prognosis can only be ensured when all component conditions are monitored with minimum uncertainty. In this regard, sensors should be selected based on their priority in providing system health information. Currently, most of the research on sensor optimization models optimize sensors position and orientation. However, in this study sensor type is optimized as well. In addition, the proposed method is based on the Bayesian network model and considers failure interdependencies of components, which leads to more realistic results.The objective of the optimization model is to obtain lowest entropy on system health information given financial and practical constraints. The proposed methodology is applied to a central heating system as a case study. The Bayesian Network model of the central heating system is constructed based on knowledge regarding the component interdependencies and conservation laws, as well as the gathered historical data. Results indicate that the Bayesian network model represents component interdependencies, and provides valuable information for sensor combination optimization. Sensors are optimized to obtain the minimum system information uncertainty. In addition, the sensitivity analysis on data generated by sensors demonstrates that the proposed method diagnosis system faults correctly, and predicts system health status effectively.

Critical infrastructure vulnerability—A method for identifying the infrastructure service failure interdependencies

Modern society has become increasingly dependent on the resilience of critical infrastructures (CI), such as electricity distribution, telecommunications, and IT infrastructure. These systems can be interdependent in a variety of ways. The dependencies may lead to a situation where a failure in one system can cause failures in other systems. The comprehension of failure interdependencies is important for the continuous functioning of CI, especially from the viewpoints of preparedness and the response. The sector-specific interdependencies are usually well known but there is a lack of comprehension of the cross-sector interdependencies. This paper presents a qualitative method for identifying and describing the CI service failure interdependencies. The proposed method is a scenario-based approach in which CI stakeholders create a common understanding of the causes and effects of a described threat scenario. The method can be used to identify and describe the potential sector-specific and cross-sector failure interdependencies. Through the method, we have identified and described the interdependencies between the failures in CIs together with domain experts involved in the Finnish regional preparedness committees and experts working in high-level positions in CIs in Finland. The results of the case study can be utilized in the process of developing the preparedness and response activities in Finland. The method presented here can be applied in the process of identifying and describing the failure interdependencies in other countries and contexts.

A Novel Inference-based Approach to Evaluate Failure Interdependency in Access Control Models

Demands for better design and analysis of access controls require system-level evaluation models that can facilitate a quantitative and consistent study of operational capabilities and economics of access control implementations. Previous works on access control models are mainly centered on the access interaction between system subjects and objects with respect to rights, addressing their basic security goals, thus failing to address other dependability attributes. To address this shortcoming, we first propose the abstraction of a computing system into: objects and rights of subjects (called in this paper assets and controls, respectively) to study the unavoidable failure interdependency between these two classes, a perspective that can be a basis for various failure-related assessment methods. We then propose a modeling technique that probabilistically captures the interaction between assets and controls into a graph theoretic paradigm; we specifically show how Bayesian Networks (BNs) can model this dilemma. This paper presents the proposed abstraction, modeling formalism, and associated notation, along with a demonstration example of various useful inferences and further research directions.

Empirical Framework for Characterizing Infrastructure Failure Interdependencies

This paper develops an analytical framework with empirical applications to characterize infrastructure failure interdependencies (IFIs). It uses major electrical power outages as the context for understanding how extreme events (within or external to the power system) lead to failures of other infrastructure systems, given a major electrical power outage. The paper takes an empirical approach by examining the patterns of IFIs that occurred in three kinds of events: the August 2003 northeastern North American blackout, the 1998 Quebec ice storm, and a set of three 2004 Florida hurricanes. Data sources include media reports and official ex post assessments of the events. The results characterize IFIs in terms of the sectors affected, and the consequences for society. We developed scales to characterize the consequences of IFIs in terms of impact and extent indices. A comparison is provided of IFIs arising in all five events discussed in the paper, as a basis for considering priorities for risk mitigation. The most significant IFIs in all five events included effects on HVAC in buildings; effects on water systems; effects on health systems, including hospitals and public health efforts, and effects on road transportation systems.

Infrastructure failure interdependencies in extreme events: power outage consequences in the 1998 Ice Storm

This paper addresses the problem of interdependent failures of critical infrastructures in disasters. Disruptions to critical infrastructure systems such as electric power or transportation frequently cause major social and economic loss in disasters, both directly and through failures in one system leading to or compounding disruptions in another. Strategic approaches regarding infrastructure failures are needed to guide community mitigation and preparedness efforts. This paper defines and provides a conceptual framework for investigating infrastructure failure interdependencies (IFIs) from the standpoint of societal impacts. In order to identify empirical patterns, a unique database has been developed of IFIs observed in major electric power outage events. This paper presents analysis of this data for a major Canadian disaster, the 1998 Ice Storm that affected the northeastern region of the country. The analysis identifies IFIs due to power outage caused by the storm that are of greatest societal concern. These represent potential foci for effective, targeted pre-disaster mitigation and preparedness efforts. The framework and approach are broadly applicable across a range of natural and human-induced hazards.

A synergetic approach for assessing and improving equipment performance in offshore industry based on dependability

The objective of this paper is to present a framework for assessing and improving offshore equipment performance based on dependability. The main idea is to employ principle component analysis (PCA) and importance analysis (IA) to provide insight on the equipment performance. The validity of the model is verified and validated by data envelopment analysis (DEA). Furthermore, a non-parametric correlation method, namely, Spearman correlation experiment shows a high level of correlation between the findings of PCA and DEA. The equipment of offshore industries is considered according to OREDA classification. The approach identifies the critical equipment, which could initiate the major hazards in the system. At first PCA is used for assessing the performance of the equipment and ranking them. IA is then performed for the worst equipment which could have most impact on the overall system effectiveness to classify their components based on the component criticality measures (CCM). The analysis of the classified components can ferret out the leading causes and common-cause events to pave a way toward decreasing failure interdependency and magnitude of incidents which ultimately maximize overall operational effectiveness.

An Integrated Model-Based Approach for Evaluating and Improving Safety Instruments in Continuous Process from the Viewpoints of Maintainability, Reliability and Availability-Case Study: Offshore Industry

The objective of this paper is to present a framework for assessing and improving safety equipment items performance based on maintainability, reliability and availability. The main idea is to employ Principal Component Analysis (PCA) and Importance Analysis (IA) to provide insight on safety equipment items performance. The validity of the model is verified and validated by Data Envelopment Analysis (DEA). Furthermore, a non-parametric correlation method, namely, Spearman correlation experiment shows a high level of correlation between the findings of PCA and DEA. At first PCA is used for assessing the performance of safety equipment items and ranking them. IA is then performed for the worst safety equipment item which could have most impact on the overall system effectiveness to classify their components based on the Component Criticality Measures (CCM). The analysis of the classified components can ferret out the leading causes and common-cause events to pave a way toward decreasing failure interdependency and magnitude of incidents which ultimately maximize overall operational effectiveness.