Edge Failure Research Articles

An Experimental Study of Polynomial Time Algorithms for Minimum 2-Edge Connected Subgraph Problem

In this paper, we focus on the problem of finding a minimum-sized directed 2-edge-connected subgraph, a problem classified as NP-Complete [8], which plays a critical role in various practical applications. We present approximation algorithms aimed at finding efficient, high-quality solutions within polynomial time. These algorithms are based on a comprehensive analysis of the problem of finding a directed 2-edge-connected subgraph, with performance evaluated in terms of the number of edges. The results of our experiments demonstrate that the proposed algorithms effectively reduce the number of remaining edges across different graph scenarios, particularly in high-density graphs. Moreover, they maintain strong connectivity even in the event of edge failures, ensuring the continuity of network operations in the face of faults and disasters.

The Reliability of a Class of Two-Layer Networks with Unreliable Edges

It is well known that networks are dynamic graphs, and the topology of a network can be described by a graph. Thus, the reliability of a network under edge failure is defined as the probability that its corresponding topological graph remains connected under the condition that the edges fail with independent probabilities. In this paper, the reliability of a class of two-layer networks is considered, where each layer is a complete graph and the edges joining different layers are one-to-one correspondingly connected. The edge failure probability is uniform in the same layer and distinct in different layers and between layers. The recursive formula for the reliability (reliability polynomial) of the two-layer network is obtained, and the corresponding algorithm is also given. Furthermore, the reliability of several networks is computed by Python, which verifies the correctness of the algorithm.

Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective

Complex and changing environments often cause resource failures in smart manufacturing systems (SMSs), significantly affecting their robustness. This paper introduces a novel methodology to assess the robustness of SMSs facing resource failures, using a complex network approach. It divides SMSs into social and technical layers, analyzes resources and relationships within and between these layers, and establishes a two-layered network model. It also categorizes various types of failures and proposes three robustness metrics to evaluate system performance at individual, local, and global levels. Simulations visually demonstrate the methodology and key findings: (1) the robust-but-fragile trait of SMSs only reacts to node failures and keeps significant in terms of the gradient of robustness; (2) there exists no edge failure that keeps damaging system robustness to the maximal or minimal degrees, and edge failures cause less damage to system robustness than node failures; (3) when failures occur, SMS robustness at all levels changes with inconsistent paces, and the optimal link mode varies by network structures and failure strategies. Finally, managerial implications are presented to guide practical robustness control at different stages of SMS lifecycles.

Enabling high reliability via matroidal connectivity and conditional matroidal connectivity on arrangement graph networks

Connectivity indicators are commonly used to evaluate system fault tolerance and reliability. However, with the high demand for multi-processor systems in high-performance computing and data center networks, the number of processors is getting larger, and the network is getting more complex. Thus, traditional connectivity and other indicators are hardly competent in assessing the reliability of complex networks. The matroidal connectivity and conditional matroidal connectivity are novel connectivity metrics that measure the actual fault-tolerant capability based on the constraints of each network dimension. In this paper, we study matroidal connectivity and conditional matroidal connectivity of the (n,k)-arrangement graph network An,k from the natural perspective of the partition of edge dimension and obtain their theoretically accurate values. Moreover, we conduct numerical analysis to compare matroidal connectivity with other conditional edge connectivities in An,k. Additionally, we explore the distribution pattern of edge failure through simulation experiments in An,k and attain the relation of conditional matroidal connectivity related to network scales. Our investigations include two famous network classes: alternating group graphs and star graphs.

Dynamic performance of a 5-story full-scale prestressed precast space frame structure under edge and corner column failure scenarios

Progressive collapse resistance is more critical for precast concrete than cast-in-place structures. Most existing studies utilized scaled-down and quasi-static pushdown tests on substructures with rigid constraints under key member failure scenarios. However, the connection details, the spatial action of floors and the constraint stiffness of the remaining structure significantly affect the structure's progressive collapse resistance. Full-scale structural testing is the most direct method to evaluate the progressive collapse resistance of a new prefabricated structure, although it is difficult and costly to load and measure. A 2 × 2 bay 5-story full-scale frame structure was constructed according to the minimum requirements of the Code for Seismic Design of Buildings to investigate the progressive collapse resistance of a new precast, prestressed, efficiently fabricated frame (PPEFF) system. The structure's dynamic performance was analyzed by removing the 4th-floor edge column and bottom corner column. The slider and double micro-friction surface configuration proved suitable for the rapid removal of large axial force columns in a full-scale progressive collapse experiment. The layout of the full-scale structural test and the structure's dynamic responses are described. The effect of the lateral constraint stiffness and the duration of column removal on the dynamic response of the test structure were analyzed using finite element analysis. The progressive collapse resistance mechanism of the PPEFF structure was discussed. The PPEFF system remained in an elastic state and exhibited good progressive collapse resistance.

Robustness of higher‐order network pertinent to first‐ and higher‐order dependency

AbstractRobustness of higher‐order network (HON) with dependency is different from first‐order network (FON), as the attack orders and the number of iterations are totally different. To measure the robustness of HON with dependency, this study proposed new attack strategies on node and edge failure through measuring the size of the giant component and global efficiency. And two real‐world systems of hazardous materials transportation accident causation system and Chinese high‐speed railway system with different network size were empirically studied. Results show that HON is more fragile to robustness attacks than FON. The number of attack iterations is uncertain in HON, which depends on the attack strategies and the network structure. Meanwhile, nodes and edges with high dependency have greater impacts on network robustness, providing valuable insights into capturing critical information. This study may be helpful to design the robust system, improve the system's reliability and serve as a valuable reference for future exploration of HONs robustness.

Node and edge centrality based failures in multi-layer complex networks

Multi-layer complex networks (MLCN) appears in various domains, such as, transportation, supply chains, etc. Failures in MLCN can lead to major disruptions in systems. Several research have focussed on different kinds of failures, such as, cascades, their reasons and ways to avoid them. This paper considers failures in a specific type of MLCN where the lower layer provides services to the higher layer without cross layer interaction, typical of a computer network. A three layer MLCN is constructed with the same set of nodes where each layer has different characteristics, the bottom most layer is Erdos–Renyi (ER) random graph with shortest path hop count among the nodes as gaussian, the middle layer is ER graph with higher number of edges from the previous, and the top most layer is preferential attachment graph with even higher number of edges. Both edge and node failures are considered. Failures happen with decreasing order of centralities of edges and nodes in static batch mode and when the centralities change dynamically with progressive failures. Emergent pattern of three key parameters, namely, average shortest path length (ASPL), total shortest path count (TSPC) and total number of edges (TNE) for all the three layers after node or edge failures are studied. Extensive simulations show that all but one parameters show definite degrading patterns. Surprising, ASPL for the middle layer starts showing a chaotic behaviour beyond a certain point for all types of failures.

Sampling and active learning methods for network reliability estimation using K-terminal spanning tree

Network reliability analysis remains a challenge due to the increasing size and complexity of networks. This paper presents a novel sampling method and an active learning method for efficient and accurate network reliability estimation under node failure and edge failure scenarios. The proposed sampling method adopts Monte Carlo technique to sample component lifetimes and the K-terminal spanning tree algorithm to accelerate structure function computation. Unlike existing methods that compute only one structure function value per sample, our method generates multiple component state vectors and corresponding structure function values from each sample. Network reliability is estimated based on survival signatures derived from these values. A transformation technique extends this method to handle both node failure and edge failure. To enhance efficiency of proposed sampling method and achieve adaptability to network topology changes, we introduce an active learning method utilizing a random forest (RF) classifier. This classifier directly predicts structure function values, integrates network behaviors across diverse topologies, and undergoes iterative refinement to enhance predictive accuracy. Importantly, the trained RF classifier can directly predict reliability for variant networks, a capability beyond the sampling method alone. Through investigating several network examples and two practical applications, the effectiveness of both proposed methods is demonstrated.

Droplet Bottom Expansion and Its Wettability Control Mechanism Based on Macroscopic Defects.

Biomimetic surfaces with special wettability have received much attention due to their promising prospects in droplet manipulation. Although some progress has been made, the manipulation of droplets by macroscopic defects of the millimeter structure and the wetting-state transition mechanism have rarely been reported. Herein, inspired by lotus leaves and desert beetles, biomimetic surfaces with macroscopic defects are prepared by laser processing and chemical modification. Various functions of droplet manipulation are achieved by controlling the millimeter-scale macroscopic defects, such as droplet capture, motion trajectory changing, and liquid well. And a droplet bottom expansion phenomenon is proposed: wetting-state transition in superhydrophobic regions around defects. The "edge failure effect" is proposed to explain the force analysis of droplet capture and the droplet bottom expansion to distinguish it from the adhesion phenomenon presented by the droplet sliding. 53.28° is defined as the expanded saturated angle of the as-prepared surface, which is used to distinguish whether the defect could cause the droplet bottom expansion. An enhanced edge failure effect experiment is designed to make the droplet bottom expansion more intuitive. This work provides a mechanistic explanation of the surfaces that utilize macroscopic defects for droplet manipulation. It can be applied to the monitoring of droplet storage limits, providing a perspective on the design and optimization of superhydrophobic surfaces with droplet manipulation.

Network design with vulnerability constraints and probabilistic edge reliability

AbstractThe network design problem with vulnerability constraints and probabilistic edge reliability (NDPVC‐PER) is an extension of the NDPVC obtained by additionally considering edge reliability. We consider the design of a telecommunication network in which every origin‐destination pair is connected by a hop‐constrained primal path, and by a hop‐constrained backup path when certain edges in the network fail. The edge failures occur with respect to their reliability, and the network is designed by considering a minimum reliability level. Therefore, a hop‐constrained backup path must be built by considering all simultaneous edge failures that have a certain probability of realization. While there exist models to solve the NDPVC without enumerating all edge subsets, edge reliability cannot be dealt with by applying the techniques applied to the NDPVC. Therefore, we develop models based on a new concept of resilient length‐bounded cuts, and solve the NDPVC‐PER without edge set enumerations. We perform extensive testing of the model to determine the best performing settings, and demonstrate the computational efficiency of the developed model. Our findings on these instances show that, in the dataset considered in this study, increasing the reliability level from 90% to 95% increases the average cost only by 12.4%, while increasing it from 95% to 99% level yields a cost increase of 93.9%.

Robustness of open source community multi-project knowledge collaboration network based on structural hole theory.

Nodes in the structural hole position play a key role in the multi-project network of the open source community (OSC). This paper studies the robustness of this network based on structural hole theory. First, a semantic-based multi-project KCN is constructed, and four node types are identified: knowledge contribution nodes, knowledge dissemination nodes, structural hole nodes (SHNs) and opinion leader nodes. Second, a robustness analysis model of the edge failures of these four key nodes is constructed. Third, a simulation test is conducted on the proposed model using empirical data from the Local Motors multi-project OSC. The results show that the KCN has the lowest robustness when facing the edge failure of opinion leader nodes, followed by knowledge dissemination nodes, knowledge contribution nodes, SHNs and random nodes. The edge failure of opinion leader nodes causes the lowest network robustness because of the propagation effect of these nodes. Additionally, SHN failure has only a small initial impact on connectivity, whereas knowledge collaboration efficiency decreases rapidly (i.e., the edge failure of SHNs causes the network to enter a state of high connectivity and low efficiency). The proposed model can be used to provide comprehensive and targeted management guidance for OSC development.

Performance Evaluation of Electrode Fabricated by using FDM in Die-Sinking EDM

An electrode is a vital transmission tool of electrical charges that erodes a workpiece surface in die-sinking electrical discharge machining (EDM). However, the demanding requirements of the geometrical complexity and accuracy of an electrode significantly affected its manufacturing cost and time. Therefore, rapid tooling (RT) was attempted to improve electrode manufacturing. This research aims to verify the application of the FDM electrode in die-sinking EDM. Furthermore, the metallization and the machining performance of the FDM electrode were also studied. Fused Deposition Modelling (FDM) was utilized to fabricate a cylindrical electrode core made of Polyethylene Terephthalate Glycol (PETG). In primary metallization, the electrode core was immersed in copper paint. Next, the coated PETG substrate was electroplated in secondary metallization at a current density of 0.023 A cm-2 for 168 hours (7 days). The electrolyte consists of 80 g/ℓ copper (II) sulphate pentahydrate and 20 ml/ℓ sulphuric acid. The machining performance of FDM electrode such as material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) was benchmarked with a copper electrode. Copper coating with an average thickness of 334 µm was successfully electroplated on the surface of the FDM electrode. Additionally, the FDM electrode was able to machine the mild steel workpiece with 1 mm infit at a peak current of 16 A and pulse-on time of 50 µs without suffering premature electrode failures such as edge failure, delamination, distortion and rupturing. Lastly, the machining performance of the FDM electrode was comparable to the copper electrode in terms of MRR, EWR and SR. The potential of the FDM electrode in EDM machining is proven successful and can be investigated further to enhance the performance of this electrode.

Durability and Damage Tolerance Analysis Approaches for Wind Turbine Blade Trailing Edge Life Prediction: A Technical Review

The size of wind turbine blades is increasing rapidly, and they are being installed in remote offshore locations. Consequently, it is essential to focus on improving the design and maintenance procedures in the blade industry to meet the growing demand. Of particular concern is the long-term operational performance of the wind turbine blade trailing edge. In this paper, we discuss the application of durability and damage tolerance analysis (DADTA) approaches to trailing edge service life prediction. DADTA is mandated in the aerospace sector to support airworthiness certification and to provide an updated life prediction of the structure based on the different stages of their service life. The DADTA framework has two main parts: durability and damage tolerance analysis. The durability part uses a structural fatigue approach based on a damage accumulation method during the initial design phase to predict the lifespan of a structure without defects. On the other hand, the damage tolerance analysis part uses a fracture mechanics approach and a damage growth method to update the lifespan prediction of a structure during the operation stages. This is achieved by utilizing sensors and inspection data as inputs while the structure is in service. Both these methods are comprehensive and have merits; however, their broad adoption in the wind turbine blade industry is still lacking. The current paper provides an extensive review of these methods and shows how these can be applied to the wind turbine blade industry, specifically for predicting the structural design life of the trailing edge of composite wind turbine blades. The review includes (a) defining wind turbine trailing edge failure modes, (b) trailing edge design procedures, and (c) a detailed discussion of the application of durability and damage tolerance analysis for trailing edge life prediction. Overall, this review paper would be of special interest to blade designers and would guide researchers and engineers interested in life prediction methodologies based on DADTA approaches for wind turbine blades.

Controllability evaluation of complex networks in cyber–physical power systems via critical nodes and edges

Certain edge failures in cyber–physical power systems (CPPSs) can cause cascading failures in the power system. Protecting these critical edges and essential equipment in the supporting communication network can avoid these failures. Here, based on complex network controllability regarding kinetic equations and considering the actual operating conditions of CPPSs, we filter out a class of transmission lines and a class of nodes in the power system communication network of the power system that show a significant impact on network controllability. We further validate the results of the filtering by examining the resulting network robustness. Through simulations, we observe that the power edges and communication nodes screened based on network controllability have the most significant impacts on system controllability. It is therefore crucial to protect these edges and nodes to achieve more stable power grid operation and prevent cascading failures. Finally, we validate our results using a case study on a city in Henan Province, China. Our simulations demonstrate that the addition of a communication system can enhance the controllability of the power system.

Rewiring or adding links: A real-world case study of network vulnerability

We show that the robustness of complex networks issued from the real world can be significantly improved against deliberate attacks to high degree edges by using network modification techniques. Two methods are compared respectively based on edge rewiring and on adding edges according to an heuristic based on network bridges. We show by numerical simulation that both techniques are generally useful to decrease vulnerability of the network examples used which are of the infrastructure type. Edge rewiring in general leads to less vulnerable networks but in practice it is more difficult to carry out on real-world networks because of engineering and cost reasons. Edge addition, on the other hand, seems easier to apply for most infrastructure networks, is effective against random edge failures, and is also useful in protecting the networks from attacks targeting bridge edges.

A New Concept of Sustainable Wind Turbine Blades: Bio-Inspired Design with Engineered Adhesives.

In this paper, a new concept of extra-durable and sustainable wind turbine blades is presented. The two critical materials science challenges of the development of wind energy now are the necessity to prevent the degradation of wind turbine blades for several decades, and, on the other side, to provide a solution for the recyclability and sustainability of blades. In preliminary studies by DTU Wind, it was demonstrated that practically all typical wind turbine blade degradation mechanisms (e.g., coating detachment, buckling, spar cap/shell adhesive joint degradation, trailing edge failure, etc.) have their roots in interface degradation. The concept presented in this work includes the development of bio-inspired dual-mechanism-based interface adhesives (combining mechanical interlocking of fibers and chemical adhesion), which ensures, on the one side, extra-strong attachment during the operation time, and on the other side, possible adhesive joint separation for re-use of the blade parts. The general approach and physical mechanisms of adhesive strengthening and separation are described.

Experimental assessment of self-tapping screws for the reinforcement of multiple holes in laminated veneer lumber beams

In recent times, there is increased focus in the built environment on the use of low carbon materials. In tall timber buildings, service ducting needs to be facilitated. Multiple holes in elements may be required in buildings where a high demand of services exist. Limited investigations have been conducted on laminated veneer lumber (LVL) beams in comparison to the literature on timber and glued laminated timber beams and no experimental testing has been conducted where multiple holes in LVL have been reinforced. Such openings are of particular concern when positioned in high shear zones. Therefore, an experimental programme in which seventy-five LVL beams were monotonically tested in three-point bending was conducted. The programme included standard beams, beams with a single round hole, and beams with double round holes for three different hole diameters and two holes spacings. The openings were concentrically positioned and were reinforced using internal screw reinforcement in which three configurations were examined. The programme was designed to evaluate the guidance that is currently available which has been developed based on testing with glued laminated timber. Throughout, it was seen that the self-tapping screws orientated at 60° were the most effective in comparison to when the reinforcement was orientated at 45° or positioned vertically. For LVL beams with single holes and up to hole diameters of 0.45 diameter (d) which far exceeded the current guidance of 0.3d maximum hole size, the reinforcement when positioned at 60°, achieved midspan failures like standard beams. The failure loads of such reinforced beams were in the same range as the standard beams. The screw reinforcement when orientated at 60o, was most effective for the double holed beams when a hole size of 0.35 d for both spacings (0.5 d and 0.75 d) was studied. The load carrying capacity was also in the same range as the standard beams but the failure mode was hole edge failure. Strain profile readings indicated the effectiveness of the reinforcement such that strains at the soffit of the beam where two holes were reinforced with screws exceeded the strains at the soffit of the standard beams.

An improved graph convolutional networks for fault diagnosis of rolling bearing with limited labeled data

Abstract Rolling bearings are essential parts of rotating equipment. Due to their unique operating environment, bearings are vulnerable to failure. Graph neural network (GNN) provides an effective way of mining relationships between data samples. However, various existing GNN models suffer from issues like poor graph-structured data quality and high computational consumption. Moreover, the available fault samples are typically insufficient in real practice. Therefore, an improved graph convolutional network (GCN) is proposed for bearing fault diagnosis with limited labeled data. This method consists of two steps: graph structure data acquisition and improved graph convolution network building. Defining edge failure thresholds simplifies the generated weighted graph-structured data, thereby enhancing data quality and reducing training computation costs. Improvements to standard GCNs can effectively aggregate data features of different receptive field sizes without noticeably raising the computational complexity of the model. Experiments with limited labeled data are conducted on two public datasets and an actual experimental platform dataset to verify the superiority of the proposed method. In addition, experiments on imbalanced datasets also fully demonstrate the robustness of the proposed method.

Decay behaviour of ultrathin reinforced membranes in PEMFCs subjected to the combination of mechanical/chemical accelerated stress testing

To improve the performance of proton exchange membrane fuel cells (PEMFCs), proton exchange membranes (PEMs) have been made much thinner. However, the thinner the PEM, the more challenging its durability. In this paper, the degradation of ultrathin reinforced PEMs with two thicknesses, 8 μm and 12 μm, under the combination of mechanical/chemical accelerated stress testing (AST) is researched. The results show that the cycle numbers of the two ultrathin reinforced PEMs are equivalent and meet the target proposed by the United States Department of Energy (DOE). They present very different trends from those of changes in the durability of traditional homogeneous PEMs with thickness. The growth of the hydrogen crossover current follows an exponential function, which is the same as that of traditional homogeneous PEMs but with an order of magnitude smaller characteristic parameter k, which might be the key reason that the cycle numbers of ultrathin reinforced PEMs far exceed those of other PEMs. The failure analysis shows that the fracture of the two ultrathin reinforced PEMs is located at the edge of the membrane electrode assembly (MEA) frame. These failures could be attributed to the stress concentration at the edge of the frame and contribute to the similar lifetime and decay regulations. More details are observed that the edge failure points of the two PEMs at the gas inlet or outlet region. In addition, the effects of thinner PEMs at the gas inlet and outlet are more obvious than that at other parts. It is concluded that the durability of ultrathin reinforced PEMs is almost independent of thickness, while the stress concentration caused by the MEA structure and the condition control at the location of the gas inlet and outlet are mainly responsible for the failure of the PEM. The results in this work will provide a new perspective for the development of ultrathin reinforced PEMs.

Defense and Attack Game Strategies of Dual-Network Coupled CPPS with Communication Edge Failures

With the development of power technology and communication technology, the power grid and power communication network have become interdependent and closely coupled. The load shedding operation of the power grid is an important means to reduce the occurrence of chain faults and ensure the safe and stable operation of the power grid. Based on the transmission of load control services in the communication network, this paper establishes a model for a dual-network coupled cyber physical power system (CPPS). Considering communication edge faults, the associated load capacity of the communication edge and the expected load loss of the power grid are defined. On this basis, the paper proposes a complete information zero-sum game mechanism called “defense attack defense” for communication edge failures, which takes the expected loss of load from the power grid as the benefit. The paper studies the optimal attack and defense game strategies and provides the algorithm implementation process for the three stages of the game. Considering the bandwidth capacity of the communication edge, this paper uses the Dijkstra algorithm or k shortest paths (KSP) algorithm with the cost factor of the communication edge as the weight to plan the main and backup communication channels for multiple load control services. The simulation results show that the game mechanism proposed in this paper can effectively reduce the expected load loss from the power grid and improve the stability of the CPPS.