Failure Propagation Mechanism Research Articles

Infection-induced cascading failures – impact and mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.

Mission Reliability Modeling of Multistation Manufacturing Systems Considering Cascading Functional Failure

The core function of the manufacturing system is to output high-quality products at a stable production rhythm. However, there are complex interactions between physical and functional failures of machines in the station. Functional failures can be propagated to other stations through material flow, resulting in the increasing risk of system performance degradation, which deserves further study. Therefore, in this article, a mission reliability modeling method considering the cascading functional failure of multistation manufacturing systems is presented. First, from the perspective of functional output, the cascading failure mechanism of a manufacturing system is discussed, and the connotation of functional failure and mission reliability is proposed. Second, the interaction model between machine performance and product quality is established to analyze the production rhythm considering the imperfect inspection. Third, the cascading functional failure model is constructed based on the mechanism of failure propagation. The integrated mission reliability modeling method of a multistation manufacturing system is proposed. Finally, a case study of a ferrite phase-shifting unit manufacturing system is conducted to illustrate the effectiveness and advancement of the proposed method.

Percolation behavior analysis of weighted edge-coupled interdependent networks

The robustness of interdependent networks has received extensive attention from the inter-disciplinary fields. In reality, many interdependent systems have interdependence relationships not only between nodes but also between edges. Different from previous studies focusing on edge-coupled interdependent networks without consideration on coupling strength, in this paper, a weighted edge-coupled interdependent network model with coupling strength is proposed. Then, the cascading failure propagation mechanism caused by attacking a small percentage of edges is studied. Based on the self-consistent probability theory, theoretical framework is established to provide the mathematical analysis of robustness of weighted edge-coupled interdependent networks under edge attacks. The current results can help to understand the vulnerability of interdependent networks and provide useful suggestions for the protection and optimization of complex networked systems.

Interface constitutive modeling and failure propagation mechanisms of integrated polymer-metal hybrid (PMH) structures

Interface constitutive modeling and failure propagation mechanisms of integrated polymer-metal hybrid (PMH) structures

Room- and High-Temperature Fatigue Strength of the T5 and Rapid T6 Heat-Treated AlSi10Mg Alloy Produced by Laser-Based Powder Bed Fusion

The AlSi10Mg alloy produced by laser-based powder bed fusion (L-PBF) is widely used to produce high-value-added structural parts subjected to cyclic mechanical loads at high temperatures. The paper aims to widen the knowledge of the room- and high-temperature (200 °C) fatigue behavior of the L-PBF AlSi10Mg alloy by analyzing the fully reversed rotating bending test results on mechanically polished specimens. Two heat-treated conditions are analyzed: T5 (direct artificial aging: 4 h at 160 °C) and novel T6R (rapid solution: 10 min at 510 °C, artificial aging: 6 h at 160 °C). The study highlights that (i) the T6R alloy is characterized by higher fatigue strength at room (108 MPa) and high temperatures (92 MPa) than the T5 alloy (92 and 78 MPa, respectively); (ii) thermal exposure at 200 °C up to 17 h does not introduce macroscopical microstructural variation; (iii) fracture surfaces of the room- and high-temperature-tested specimens show comparable crack initiation, mostly from sub-superficial gas and keyhole pores, and failure propagation mechanisms. In conclusion, the L-PBF AlSi10Mg alloy offers good cyclic mechanical performances under various operating conditions, especially for the T6R alloy, and could be considered for structural components operating at temperatures up to 200 °C.

Progressive Damage Behaviour Analysis and Comparison with 2D/3D Hashin Failure Models on Carbon Fibre-Reinforced Aluminium Laminates.

It is known that carbon fibre–reinforced aluminium laminate is the third generation of fibre metal materials. This study investigates the response of carbon fibre–reinforced aluminium laminates (CARALL) under tensile loading and three-point bending tests, which evaluate the damage initiation and propagation mechanism. The 2D Hashin and 3D Hashin VUMAT models are used to analyse and compare each composite layer for finite element modelling. A bilinear cohesive contact model is modelled for the interface failure, and the Johnson cook model describes the aluminium layer. The mechanical response and failure analysis of CARALL were evaluated using load versus deflection curves, and the scanning electron microscope was adopted. The results revealed that the failure modes of CARALL were mainly observed in the aluminium layer fracture, fibre pull-out, fracture, and matrix tensile fracture under tensile and flexural loading conditions. The 2D Hashin and 3D Hashin models were similar in predicting tensile properties, flexural properties, mechanical response before peak load points, and final failure modes. It is highlighted that the 3D Hashin model can accurately reveal the failure mechanism and failure propagation mechanism of CARALL.

Study on Invulnerability of Material Emergency Transportation System Based on Three-Layer Interdependent Network

The material transportation capacity under emergency conditions is an important guarantee for the country to deal with war, epidemic outbreak, and other crisis situations. Under emergency conditions, some nodes of the material transportation system may fail to work normally, which may lead to the collapse of the whole system. Based on analyzing the characteristics of the material emergency transportation system, this article builds a three-layer interdependent network model and uses the improved M-L model to describe the failure propagation mechanism of node damage in the three-layer network. Then, the network model is attacked randomly, and the relationship between invulnerability of the three-layer network and the three main indexes of network flow, average degree, and probability of interdependence are studied. Afterwards, the propagation of cascading failure among three subnetworks in the interdependent network is analyzed and compared. This article provides a theoretical basis for building an efficient and robust material emergency transportation system.

On the effect of roughness parameters on adherence using the three-point bending test (ISO 14679:1997)

ABSTRACT In the present work, a specific three-point bending test is applied to evaluate how the roughness can impact the bond strength (adherence) and the mechanism of interfacial failure initiation. The study is conducted using an aluminum alloy 2024-T3 as substrate and the DGEBA (polyepoxide bisphenol A diglycidyl ether) /DETA (diethylenetriamine) as adhesive, considering different abrasive surface treatments. An optimal roughness is reached to maximize the critical force during failure initiation; besides, the roughness impacted the failure propagation mechanism and the failure initiation area for each abrasive treatment. A power-law regression is considered to correlate the critical force and the failure initiation area, considering different average roughness. Local assessment using a mechanical profilometer and Scanning Electron Microscope (SEM) with Focused Ion Beam (FIB) are applied to measure the residual adhesive thickness at failure initiation and failure propagation zones as well as the initiation-to-propagation transitions. It is constated that the residual adhesive thickness is dependent on the average roughness of the substrate. Finally, Weibull’s analysis is undertaken to demonstrate that the mechanism of failure initiation for all substrate groups is essentially the same, although the failure propagation mechanism can be different.

Investigation for the effect of side plates on thermal runaway propagation characteristics in battery modules

• Energy flow of battery modules with and without side plates was clearly quantified. • Effect of side plates on thermal runaway propagation behaviors was fully analyzed. • Mechanisms affecting failure point of a module with side plates were clarified. • Guidelines for selecting parameter of side plates was proposed for a safer module. Side plates have been commonly arranged around lithium-ion batteries to ensure their structural stability when combining modules, which could affect the heat transfer path once thermal runaway propagation occurs in the battery modules, owing to the good thermal conductivity of side plates. However, the effect of side plates on the propagation characteristics is not comprehensively studied. In this paper, a validated three-dimensional thermal runaway propagation model of a battery module is used to investigate the propagation behaviors under different design schemes with side plates. A parametric study is carried out to determine the energy flow distribution of battery modules with and without side plates, and to evaluate the effect of the structural and thermal boundary parameters of the side plates on the failure propagation behaviors. The results indicate that the side plates delay the initial thermal runaway trigger time, considerably affect energy flow distribution, and aggravate the heat transfer. Approximately 40.8% of effluent energy passes through the side plates, dominating the critical point for battery thermal runaway propagation. Furthermore, with side plates surrounding the module, the initial thermal runaway trigger time is delayed by 12.7%, whereas the average propagation time interval between adjacent batteries is shortened by 27.4%, compared to those without side plates. An increase in the thickness, area, and aspect ratio of the side plates can postpone the initial thermal runaway trigger time, while shortening the average propagation time interval. This work adds new knowledge in the failure propagation mechanism within battery modules with side plates, enlightening the thermal safety design of high-energy battery modules.

Application of hypergraph theory in the analysis of the failure propagation and diffusion behaviour of machining centre

AbstractTo improve the rationality of failure diagnosis, this research uses hypergraph theory as basis in proposing a method of analysing failure propagation behaviour to study the failure propagation mechanism from the system perspective. A hierarchical structure model is constructed by failure correlation analysis, matrix transformation and decomposition. The probability of failure is determined by considering the influence of multiple truncation data. On this basis, hypergraph theory is used to calculate the one‐step and cumulative coefficient of failure propagation and diffusion. The influence degree of failure is calculated by integrating the edge betweenness. Using the influence degree of failure as an indicator, failure propagation and diffusion behaviour are analysed, and critical failure nodes and paths are identified. Lastly, a machining centre is used as an example for specific application. The results are compared with the ranking results of critical failure propagation paths determined by other methods, and the effectiveness of the proposed method is verified.

Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

Cascading failure modeling in interdependent critical infrastructure systems (CISs) is an important but challenging work that lays the foundation for disaster impact assessment and recovery planning. However, despite the increasing volume of studies that examine cascading failure across interdependent CISs, the majority of modeling approaches proposed in prior studies do not capture the complex heterogeneous nature and behavior of the CISs under disaster, limiting the ability of these approaches to detailly model the cascading failure process. To address the above limitation, this study proposes a high level architecture (HLA)-based co-simulation approach to integrate heterogeneous domain-specific CIS models and model the cross-domain interdependencies and failure propagation processes. Moreover, the approach provides a novel solution for modeling the dynamic impact of disaster on CISs and analyze its impact on the cascading failure of interdependent CISs. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed approach. The results showed that the model developed using the proposed approach could provide granular data of the state and heterogeneous behaviors of the systems, capture the dynamic evolution of disaster impact on system components, and reveal the paths and mechanisms of failure propagation within and across the systems. The proposed modeling approach can overcome the drawbacks in current interdependent CISs cascading failure models, and provide a foundation for resilience assessment of CISs.

RV-DSS: Towards a resilience and vulnerability- informed decision support system framework for interdependent infrastructure systems

The common challenge currently faced by critical infrastructure (CI) asset owners and operators is the lack of an integrated and robust resilience-informed business planning and management approach in response to interdependent assets’ failures, in particular due to low-probability/high-impact environmental hazards.Interdependencies among CI can cause cascading failures and hence, amplify impacts due to these failures. This can also affect CI’s service restoration rate and consequently, reducing their resilience in coping with these hazardous events. As infrastructures are becoming more interdependent in some sectors, there is an increasing need for better management of the interactions and interdependencies.To reduce these impacts, an integrated resilience and vulnerability- informed Decision Support System (DSS) is required to identify interdependent network’s vulnerable components and introduce adaptive capacities accordingly. This is of particular importance given ever growing investments in asset management across different sectors in order to improve the resilience of the networks in response to extreme environmental hazards.This study presents a novel framework for building a resilience and vulnerability-informed decision support system (RV-DSS). This framework provides potential means of communicating challenges induced due to interdependencies and quantifies benefits of considering interdependencies in streamlining intervention strategies for systems. It also proposes a measure of network resilience in response to hazardous events, in addition to the commonly used measures of vulnerability for assessment of the network performance. The framework can be used in initiating the interdependency-based communications among different CI network owners and managers, leading to shared knowledge and common understanding of their connected assets, hidden failure propagation mechanisms and collective recovery process. The application of the framework is then demonstrated using a case study in North Argyll, Scotland. It is quantitatively demonstrated that although infrastructures with a higher level of interdependency, can impose the network to higher vulnerability, it provides a greater opportunity for an integrated recovery process.

Resilience-Based Optimal Recovery Strategy for Cyber–Physical Power Systems Considering Component Multistate Failures

This article investigates optimal recovery strategy of components for maximizing the resilience of the cyber–physical power system (CPPS), where a component represents a unique node or branch, such as generating stations and communication transmission lines. The proposed optimization model is built as a multimode resource-constrained project scheduling problem to incorporate system resilience, cascading failures of the CPPS, the diversity of recovery resources, execution modes of recovery activities, precedence of damaged components, as well as the availability, cost, and time of recovery resources. The failure propagation mechanisms are characterized by a cascading failure model, which is further embedded in the optimization model to quantify the system real-time performance during the recovery process, and determine whether repaired components can be reconnected to the system. The system resilience is quantified using a proposed time-dependent annual composite resilience metric based on a compound Poisson process. The proposed optimization model is solved using a modified simulated annealing algorithm. The system that couples the IEEE 30-bus model and a small-world communication network is used as a testbed to demonstrate the feasibility and effectiveness of the proposed modeling approach. Comparisons with existing optimization models in the literature show the superiority of the proposed model.

Maximization of Robustness of Interdependent Networks Under Budget Constraints

We consider the problem of interlink optimization in multilayer interdependent networks under cost constraints, with the objective of maximizing the robustness of the network against component (node) failures. Diverting from the popular approaches of branching process based analysis of the failure cascades or using a supra-adjacency matrix representation of the multilayer network and employing classical metrics, in this work, we present a surrogate metric based framework for constructing interlinks to maximize the network robustness. In particular, we focus on three representative mechanisms of failure propagation, namely, connected component based cascading failure, load distribution in interdependent networks, and connectivity in demand-supply networks, and propose metrics to track the network robustness for each of these mechanisms. Owing to their mathematical tractability, these metrics allow us to optimize the interlink structure to enhance robustness. Furthermore, we are able to introduce the cost of construction into the interlink design problem, a practical feature largely ignored in relevant literature. We simulate the failure cascades on real world networks to compare the performance of our interlinking strategies with the state of the art heuristics and demonstrate their effectiveness.

A new method for multivariable nonlinear coupling relations analysis in complex electromechanical system

Coupling relations analysis of monitoring variables in complex electromechanical system is a powerful and useful means for abnormal detection, false monitoring information identification and fault diagnosis. However, due to the characteristics of multivariable, nonlinear and non-stationarity in the actual production process, it is difficult to achieve multivariable coupling modeling of complex electromechanical systems. In this paper, a new coupling modeling method is proposed by combining causality analysis with RBF neural network. First, considering the multivariate and nonlinearity of complex system, the conditional Granger and nonlinear Granger is combined to analyze the causal relations of process variables and obtain the cause variable set of any variable in complex system. Second, the RBF neural network is applied to achieve the nonlinear fitting and the parameter is optimized to ensure the fitting precision. Finally, the effectiveness of the proposed method is verified by an analysis of one case study of real compressor groups data set in chemical production system. This new approach can handle general coupling modeling problems and obtain a quantitative nonlinear coupling model by determine the dependent and independent variables in system and the functional relationship between them. Which dedicated to studying quantitative functional relationship of variable coupling, not just considering the direction or strength of coupling as in the existing, and does not need any prior knowledge about the physical structure. Thus, the proposed method can be effectively used in coupling modeling of complex electromechanical systems and formulate the foundation of anomaly detection, information quality assessment, and failure propagation mechanism, as well as other engineering applications.

Sensitivity Analysis of Material Model Parameters to Reproduce Crushing of Composite Tubes

The ability to predict the behavior of composite structures under impact conditions is an important challenge for engineering analysis since it requires modeling beyond the elasticity and into failure initiation and propagation. Crushing of laminate composites is the result of a combination of several and complex failure mechanisms, which are not always possible to reproduce together in a single finite element analysis (FEA). In this paper, models based on lamina-level failure criteria have been used to predict the onset of damage; once failure initiates, the mechanisms of failure propagation require reducing the material properties using several degradation schemes. Among the composite material models available in LS-DYNA, MAT_ENHANCED_COMPOSITE_DAMAGE (MAT_54) has been chosen for modeling the axial crushing of composite tubes with various resistant sections and wall slope, given the reduced experimental input parameters to insert into the card definition. Comparison with experimental data shows that such material model is able to successfully reproduce the crushing behavior, even if the numerical results are strictly correlated with some model parameters which cannot be measured experimentally or are purely mathematical expedients. Therefore, extensive calibration of these parameters by trial-and-error is necessary to achieve successful simulation results. Once done it for a structure made of the same composite material, it is possible to adopt the same card configuration for all the other cases, obtaining good agreement between numerical and experimental results. After, the attempt to model the same geometries using the MAT_LAMINATED_COMPOSITE_FABRIC (MAT_58) card with the same parameter values previously calibrated has been done obtaining results comparable with the experimental data.

The \u201cweak\u201d interdependence of infrastructure systems produces mixed percolation transitions in multilayer networks

Previous studies of multilayer network robustness model cascading failures via a node-to-node percolation process that assumes “strong” interdependence across layers–once a node in any layer fails, its neighbors in other layers fail immediately and completely with all links removed. This assumption is not true of real interdependent infrastructures that have emergency procedures to buffer against cascades. In this work, we consider a node-to-link failure propagation mechanism and establish “weak” interdependence across layers via a tolerance parameter α which quantifies the likelihood that a node survives when one of its interdependent neighbors fails. Analytical and numerical results show that weak interdependence produces a striking phenomenon: layers at different positions within the multilayer system experience distinct percolation transitions. Especially, layers with high super degree values percolate in an abrupt manner, while those with low super degree values exhibit both continuous and discontinuous transitions. This novel phenomenon we call mixed percolation transitions has significant implications for network robustness. Previous results that do not consider cascade tolerance and layer super degree may be under- or over-estimating the vulnerability of real systems. Moreover, our model reveals how nodal protection activities influence failure dynamics in interdependent, multilayer systems.

Interaction analysis–based information modeling of complex electromechanical systems in the processing industry

Information modeling for complex electromechanical systems in the processing industry is the foundation for system vulnerability analysis, failure propagation mechanism, and fault root cause tracin...

The Determination of Functional Safety Concept coupled with the definition of Logical Architecture: a framework of analysis from the automotive industry

Abstract In the nineties the growing integration of electronic components in mass products led to the introduction of systems engineering methods in the design offices. In the automotive industry this deployment has been accelerated due to the need for the reduction of polluting emissions and for safety concerns. Recently the introduction of safety standards such as ISO 26262 and the design of connected and autonomous vehicles required the development of new system modeling approaches, and particularly, model-based safety analysis methods (MBSA). In this article, we explain how Functional Safety Concept can be determined jointly with the definition of the logical architecture. This will be based on failure propagation mechanisms. This method is applied on an automotive case study.

Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis

Using an exhaustive data set on claims held by trade creditors (suppliers) on failed trade debtors (customers), we quantify the importance of trade credit chains for the propagation of corporate bankruptcy. We show that trade creditors experience significant trade credit losses due to trade debtor failures and that creditors' bankruptcy risks increase in the size of incurred losses. By exploring the roles of financial constraints and creditor-debtor dependences, we infer that the trade credit failure propagation mechanism is driven by both credit losses and demand shrinkage. Finally, we show that the documented propagation mechanism constitutes a significant part of the overall bankruptcy frequency, suggesting that it has measurable implications for the aggregate level