Problem Of Failure Research Articles

Numerical investigation study on tensile-shear failure behavior of rock bolts in inclined strata mining tunnels

In response to the problem of severe deformation and failure of the surrounding rock of the mining tunnels in inclined strata, a tensile-shear failure numerical model of rock bolts was adopted to analyze the rock bolts failure behavior and the surrounding rock mechanical response, and explore the influence of different factors on rock bolts failure. The results indicate that: (1) As angle between the rock bolt and the interface decreases, the shear force of increases, and the rock bolt is easier to fracture. The maximum displacement is 250.2 mm of fractured position. A new evaluation index of shear axis ratio was proposed. The larger the shear axis ratio, the rock bolt is easier to fracture. The shear axis ratio at the interface has increased by 95 % compared to the non interface. (2) The plastic zone and the surrounding rock deformation exhibit the same inclination trend as the strata. In final state, the maximum deformation is 297.7 mm at the bottom and top position perpendicular to the rock bolts at the interface. At the moment of rock bolts fracture, the interface displacement of surrounding rock immediately increased by about 10 mm. And the stress in the surrounding rock decreased about 1 MPa. The weakening effect of surrounding rock stress shifts with the change of rock bolt fracture position. The stress at the arch waist decreases by 44 %, the stress at the arch crown decreases by 25 %. When the fracture of the rock bolts at the arch crown and arch waist ends, the stress at the side wall begin to decrease by 40 %. (3) As the inclination angle increases, the deformation of the surrounding rock and the number of rock bolt failures increase. The range of the plastic zone in the surrounding rock gradually increases, and the degree of rock bolts asymmetric fracture more aggravated. As the diameter of the rock bolt increases, the fractured number of the rock bolts and range of plastic zone decreases.

WITHDRAWN: Visualized neural network-based vibration control for pigeon-like flexible flapping wings

This study investigates pigeon-like flexible flapping wings, which are known for their low energy consumption, high flexibility, and lightweight design. However, such flexible flapping wing systems are prone to deformation and vibration during flight, leading to performance degradation. It is thus necessary to design a control method to effectively manage the vibration of flexible wings. This paper proposes an improved rigid finite element method (IRFE) to develop a dynamic visualization model of flexible flapping wings. Subsequently, an adaptive vibration controller was designed based on non-singular terminal sliding mode (NTSM) control and fuzzy neural network (FNN) in order to effectively solve the problems of system uncertainty and actuator failure. With the proposed control, stability of the closed loop system is achieved in the context of Lyapunov’s stability theory. At last, a joint simulation using MapleSim and MATLAB/Simulink was conducted to verify the effectiveness and robustness of the proposed controller in terms of trajectory tracking and vibration suppression.The obtained results have demonstrated great practical value of the proposed method in both military (low-altitude reconnaissance, urban operations, and accurate delivery, etc.) and civil (field research, monitoring, and relief for disasters, etc.) applications.

Structural behaviour of single-deck floating roof tanks subject to seismic loading

During a seismic event, fluid storage tanks are subject to sloshing phenomena; their effect on the structural components of the tanks is one of the main aspects to be considered during the design. The movement in successive modal forms of the roof-fluid system determined by the earthquake can cause severe damage to both the containment shell and the floating roof. The fluid-structure interaction imposes extremely complex out-of-plane deformation fields, mainly in the presence of floating roofs with lower flexible resistance. Such deformations can cause severe structural damage and subsequent catastrophic events such as those experienced in the past in Japan and Turkey. In fact, recently recorded earthquakes in Japan and Turkey, characterized by oscillation periods between 3 and 8 sec (long-period ground motion), compatible with the natural first and second slosh frequencies of the fluid-structure system, led to conspicuous problems of instability failure with the consequent sinking of the floating roof. These events dictated a new starting point for the research into the seismic behaviour of roof-fluid-tank systems.

Highly flexible porous indium-tin-oxide coating with enhanced atomic oxygen and electrostatic discharge resistance

Polyimides (PI) are commonly used in spacecraft in low Earth orbit (LEO) due to their flexibility. However, they are susceptible to erosion caused by atomic oxygen (AO) and electrostatic discharge (ESD) induced by plasma in the harsh LEO environment. Indium-tin-oxide (ITO), owing to its excellent electrostatic and AO resistance, has emerged as the most promising inorganic protective coating for PI in LEO applications. But the inherent rigidity of ITO and the high-temperature annealing process commonly required for synthesis make it challenging to employ ITO as flexible polymer substrates. In this work, we have synthesized the porous ITO layer with a low-temperature (≤150 °C) method and combined it with layer double hydroxides (LDHs) to enhance the flexibility, AO resistance, and ESD protection of the coating. The porous ITO in combination with LDHs layers effectively improve the flexibility (after 20,000 bending cycles without failure problem), AO resistance (erosion yield down to -0.102 × 10−26 cm3/atom under an exposure dose of 5.22 × 1021 atoms/cm2) and ESD resistance properties (sheet resistance around 202 kΩ/sq). The synthesis method proposed avoids the high-temperature annealing and the residual stress effect on the coating's flexibility. This work provides an efficient approach to realizing flexible and protective coating on polymers for long-lasting spacecraft electronic devices in low Earth orbit.

Experimental Study and Creep-Fatigue Life Prediction of Turbine Blade Material DZ125 Considering the Nonholding Effect and Coupling Effect of Stress and High Temperature

Abstract In response to the problem of creep-fatigue interaction damage failure of aero-engine turbine blade material, based on the modified damage evolution model of Kachanov-Rabotnov and Chaboche, a creep-fatigue life prediction model for nickel-based superalloy DZ125 is constructed considering the nonholding effect and coupling effect of stress and high temperature with the nonlinear interaction and superposition of creep damage and fatigue damage according to the continuum damage mechanics theory. Simultaneously, the microfracture morphology of DZ125 was analyzed using a scanning electron microscope, revealing the micromechanism of creep-fatigue interaction. The research results manifest that the creep-fatigue life prediction model has a high life prediction ability within ±2.0 times the dispersion band of the prediction results. Concurrently, a large number of intertwined tearing edges, microcracks, and microvoids appear in the fracture morphology, and creep and fatigue interact with each other in the form of effective stress. The above research can provide theoretical support for predicting the lifespan of mechanical structures in a high-temperature environment.

Study on progressive failure mode of surrounding rock of shallow buried bias tunnel considering strain-softening characteristics

Mountain tunnels portal often have to pass through slope terrain unavoidably, thus forming a shallow buried bias tunnel. During the construction of shallow buried bias tunnel, disasters such as slope sliding and tunnel collapse frequently occur. The failure mode of surrounding rock obtained by current research is based on the limit equilibrium theory, which cannot reflect the progressive failure characteristics of the surrounding rock of shallow buried bias tunnel. In order to reveal the failure mechanism of the gradual instability of surrounding rock of shallow buried bias tunnel, the problem of gradual failure of the surrounding rock is reduced to an elastic–plastic analysis problem for surrounding rock considering the strain-softening characteristics. Based on the elastic–plastic analysis of the failure process of shallow buried bias tunnel, MATLAB was used to compile a program to read the finite-difference calculation result file, extract the effective information such as shear strain and tensile strain at the center point of each unit, and establish the analysis method of the progressive failure mode of shallow buried bias tunnel. The reliability of the method proposed was verified by comparing the failure process of the model test with the development process of shear strain increment. Under the condition of no support, the formation mechanism of failure plane of surrounding rock on both sides of shallow buried bias tunnel is different. The shallow buried side is the shear failure plane formed by the collapse of surrounding rock, while the deep buried side of the tunnel is the shear failure plane formed by the collapse of surrounding rock and slope sliding. Under the conditions of excavation and support, the failure plane of the shallow buried bias tunnel can be divided into three parts according to the formation sequence and reasons. The part I is the failure plane, which is formed by active shear under the influence of tunnel excavation. The part II is the failure plane formed by tensile crack of slope top. The part III is the failure plane formed by passive shear under the push of the soil in the upper part of the slope.

Risk factors contributing to tick-acaricide control failure in communal areas of the Oliver Tambo district eastern cape province, South Africa

Application of chemical acaricides in the control of ticks has led to the problem of tick-acaricide control failure. To obtain an understanding of the possible risk factors involved in this tick-acaricide control failure, this study investigated tick control practices on communal farms in the north-eastern part of the Eastern Cape Province (ECP) of South Africa. A semi-structured questionnaire designed to document specific farm attributes and acaricide usage practices was administered at 94 communal farms from the Oliver Tambo District municipality of the ECP. Data collected indicated that the main acaricide chemicals used at plunge dips of inland and coastal areas were synthetic pyrethroid formulations. Most (75%) farmers claimed not to have noticed a significant reduction in numbers of actively feeding and growing ticks on cattle after several acaricide treatments. Based on the farmers’ perceptions, leading factors that could have led to tick-acaricide control failure included: weak strength of the dip solution (76%); poor structural state of dip tanks (42%); and irregular tick control (21%). The rearing of crossbreeds of local and exotic cattle breeds, perceived weak strength of the dip solution and high frequency of acaricide treatment, were statistically associated with proportions of farms reporting tick-acaricide control failure. Furthermore, approximately 50% of farms reported at least four tick control malpractices, which could have resulted in the emergence and spread of tick-acaricide control failure. Other sub-optimal tick control practices encountered included incorrect acaricide rotation, and failure to treat all cattle in a herd. This data will inform and guide the development of management strategies for tick-acaricide control failure and resistance in communal farming areas.

Revealing the mechanism of pack ceiling failure induced by thermal runaway in NCM batteries: A coupled multiphase fluid-structure interaction model for electric vehicles

Structure failure of lithium-ion battery (LIB) pack ceiling leads to the unintended release of combustible and poisonous substances during thermal runaway (TR), resulting in personnel injuries and financial losses. However, limited research has been conducted on the mechanism behind pack ceiling failures. In this study, we developed a coupled multiphase fluid-structure interaction (FSI) model to simulate the evolution of up-cover baffle under the TR impact of a 52 Ah NCM battery. Our findings reveal several important insights:1) the maximum force and temperature on the baffle are 13.01 N and 598.5 °C in experiment; 2) the simulation shows that particles exert higher temperature and greater force on the baffle compared to the gas phase; 3) the overall equivalent stress in the stainless-steel baffle surpasses the tensile strength that incurs crack on the baffles. According to the validated model, we find that the baffle structure failure is caused by the thermal stress from particle-structure heat conduction. Furthermore, this observation is applicable to the structure failure problems associated to the thermal runaway of high-density battery that involves enormous particles. In addition, the insulation layer is found to be more effective than the gap distance in protecting the pack ceiling. These findings offer a valuable insight into the structure design of LIB pack, and provide the guidance toward future battery integration technologies.

An in-depth and insightful exploration of failure detection in distributed systems

In today’s world, everyone wants a good profit with a tiny investment and distributed computing is a boon for this purpose. Cloud computing, fog computing, and the Internet of Things (IoT) are well-known examples of distributed computing which provide good computing services and performance. However, providing reliable services in a real environment, which is failure-prone, remains a challenge. To address this issue, failure detectors are used in distributed systems, which are abstract modules responsible for detecting and monitoring the activity of nodes in order to determine whether they are faulty or not. In this paper, an approach is presented for the systematic literature review of failure detectors in distributed systems. Further, many existing review and survey papers on failure detectors are critically analyzed along with their key contributions and limitations. The classification of distributed systems is presented on the basis of the nodes’ properties and the components of system models are described in detail. Various issues and challenges related to agreement and failure problems are also explored. The strengths and limitations of various existing failure detectors are discussed along with their comparative evaluation. Finally, fault-tolerance and recovery techniques are discussed and analyzed.

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

Is the glass half full or half empty? Examining the impact of Swatch Bharat interventions on sanitation and hygiene in rural Punjab, India

This paper reports the findings of a cluster-randomized control study to assess the impact of India's flagship sanitation program, as implemented in Punjab, that aimed to eliminate the practice of open defecation and improve the awareness and practice of good hygiene across rural Punjab. The study finds that the program interventions specific to the Gram Panchayats (villages) had a modest effect on improving access to toilets and reducing open defecation among households with children in rural Punjab. However, awareness of the importance of handwashing before eating and after defecation among school-going children improved by 8–14 percentage points in treatment arms relative to control, though no significant impact on handwashing practices was observed. Consistent with the problem of implementation failure, the findings indicate the inherent difficulties of implementing bottom-up interventions through a large-scale government program.

Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.

Efforts of implementing ultra‐flexible thin‐film encapsulation for optoelectronic devices based on atomic layer deposition technology

AbstractFlexible, wearable electronics are the future of electronics. Although organic photovoltaic devices have the advantages of high efficiency, low cost, and flexibility, they face the problem of failure due to the effects of water vapor in the environment. Therefore, the development of encapsulation films with outstanding mechanical and encapsulation properties is the key to realizing wearable devices. This review provides an overview of the development of thin‐film encapsulation (TFE), the application of TFE in the field of optoelectronics, recent advances in the field of flexible encapsulation with TFE using atomic layer deposition technology, and an outlook on future trends in the field of flexible encapsulation with TFE using atomic layer deposition technology.

A fast and high precision multi-robot environment modeling based on M-BFSI: Bidirectional filtering and scene identification method

This article designs and implements a fast and high-precision multi-robot environment modeling method based on bidirectional filtering and scene identification. To solve the problem of feature tracking failure caused by large angle rotation, a bidirectional filtering mechanism is introduced to improve the error-matching elimination algorithm. A global key frame database for multiple robots is proposed based on a pretraining dictionary to convert images into a bag of words vectors. The images captured by different sub-robots are compared with the database for similarity score calculation, so as to realize fast identification and search of similar scenes. The coordinate transformation from local map to global map and the cooperative SLAM exploration of multiple robots is completed by the best matching image and the transformation matrix. The experimental results show that the proposed algorithm can effectively close the predicted trajectory of the sub-robot, thus achieving high-precision collaborative environment modeling.

Cloud computing-based framework for heart disease classification using quantum machine learning approach

Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.

Parent‐child interaction is related to emotional and behavioral problems in pediatric intestinal failure

AbstractObjectivesParent‐child interaction in pediatric intestinal failure (IF) and in parenteral nutrition (PN) graduates can be disturbed due to parental responsibilities related to the condition, and could affect the child's emotional and behavioral functioning (EBF). The aim was to assess parent‐child interaction, to examine associations with clinical features, and to relate parent‐child interaction to EBF.MethodsA cross‐sectional study of IF patients and PN graduates aged 1.5−18 years and one primary caregiver was performed in two Dutch multidisciplinary pediatric IF teams. Parent‐child interaction was observed using the Emotional Availability Scales (EAS), reported with the Parent Child Interaction Questionnaire (PACHIQ) and compared to normvalues when available. Associations between parent‐child interaction and clinical factors and EBF were examined. EBF was assessed using the Child Behavior Checklist. All measures are validated.ResultsA total of 41 parent‐child dyads were included, 17 (41.4%) received PN at time of assessment and median age was 8.9 [interquartile range 5.8−11.9] years. No significant differences in parent‐reported interaction compared to normative values or between PN‐receiving and weaned children were found. Exploratory multivariable regression indicated that only the child's female sex was significantly positively related to parent‐child interaction. Parent‐child interaction was negatively related to EBF for nonhostility, conflict resolution, and acceptance.ConclusionOur findings suggest that (self‐reported) parent‐child interaction in IF patients and PN graduates is not significantly different as compared to healthy peers, and unrelated to clinical characteristics. However, aspects of parent‐child interaction were correlated to EBF, potentially being a target for prevention or intervention in children with diminished EBF.

Edge AI-Based Smart Intersection and Its Application for Traffic Signal Coordination: A Case Study in Pyeongtaek City, South Korea

Recently, smart intersections have emerged as a novel intelligent transportation system (ITS) solution that integrates traffic monitoring, optimal signal control, and even traffic safety. Although smart intersections have been prevalent in many cities, there are a few drawbacks in their practical operations. First, there are inevitable delays in transmitting and processing the video data. Second, there is still a need to develop a real-time signal control method leveraging the acquired data from smart intersections. Thus, this study aims to construct edge AI-based smart intersections and to provide their application for traffic signal coordination. To this end, we install smart intersections on three consecutive intersections of Route 45 in Pyeongtaek city, South Korea. The real-time traffic data are collected by an edge AI video analysis model which is compressed and optimized for its operation in on-site edge devices. The optimized model maintains a similar level of accuracy (93.64%), even if the size is reduced by 97.8% compared to the original. Next, we utilize the LT2 model to treat the coordination failure problem in nonpeak hours occurring unnecessary delays of the side-streets with relatively high demands. We complement some constraint conditions in order to consider the compatibility with the current legacy system. The experiment is conducted on a virtual environment of which geometry and traffic demand are configured based on the features of the study site. The numerical results conclude that the optimal offsets calculated by the LT2 model effectively manage bandwidths for multidirectional flows based on the real-time traffic demands collected from the edge AI-based smart intersections. This study contributes to serve high-resolution real-time traffic data using edge AI on smart intersections and to provide a case study for signal coordination.

Nonlinear error elimination using the fusion of PGC-DCM, geometric fitting, and Atan algorithms.

A phase generated carrier (PGC) demodulation scheme is always accompanied by nonlinear errors. We propose a fusion of PGC differential and cross multiplying (PGC-DCM), geometric fitting, and arctangent (Atan) algorithms for fiber optic interferometric sensors to eliminate nonlinear errors. The output amplitude of the PGC-DCM algorithm is used to judge whether the Lissajous figure of quadrature signals is larger than 1/2 ellipse arc. When the Lissajous figure exceeds 1/2 ellipse arc, the contaminated quadrature signals are corrected by the ellipse correction parameters calculated from the geometric fitting; otherwise, the previous fitting parameters are employed for correction. Geometric fitting is realized by minimizing the Sampson error, and its failure problem under small signals is solved by using the temporary stability of fitting results. Finally, desired signals are extracted from the corrected quadrature signals by the Atan algorithm. Experimental results show that the fusion combines the merits of the three algorithms and expands the application of the geometric fitting in PGC demodulation schemes.

State dysfunctionality as a premise for the functioning of a terrorist organization in its territory

This article discusses the phenomenon of dysfunctionality of states as a premise for the operation of terrorist organizations on their territory. It analyses historical and contemporary causes of state failure, such as the legacy of colonialism, corruption, poverty, and internal ethnic and religious conflicts. It presents various definitions and classifications of dysfunctional states, including the terms "failed state" and "fragile state." The role of terrorist organizations in such states and their ability to create alternative power structures in areas beyond the control of legitimate governments are also discussed. A key factor in the success of these organizations is radical ideology, which enables the takeover and maintenance of control in the territory of a dysfunctional state. The conclusions indicate the necessity of a comprehensive approach to the problem of state failure in the context of international security.

Characterization of hydrogen refueling temperature rise of the on-board hydrogen system under different failure modes

Fuel cell vehicles (FCVs) have the advantages of pollution-free, and excellent fuel economy. However, rapid hydrogen filling will cause the temperature inside the hydrogen cylinder to rise rapidly. The failure of some components of the on-board hydrogen system (OBHS) will increase the accident rate of FCVs during rapid refueling. Valves and pipelines are ignored during the hydrogen filling mode establishment, thereinto the automatic tank valve and hydrogen dispenser are prone to failure due to loose parts, sensor failure, and other problems. In this paper, a two-dimensional (2D) planar computational fluid dynamics (CFD) model is established to simulate and analyze three kinds of failures, such as hydrogen filling blockage, excessive hydrogen filling, and excessive hydrogen flow, to study the temperature rise characteristics of the OBHS. The results show that the failures of excessive hydrogen filling and excessive hydrogen flow are more likely to cause the final temperature of the hydrogen cylinders to exceed 358 K. The maximum temperature of the OBHS caused by excessive hydrogen flow rate is 5.7 % (21 K) and 0.8 % (3 K) higher than hydrogen filling blockage and excessive hydrogen filling, respectively. This indicates that, contrary to what other studies have not found, the hydrogen filling flow has a greater impact on the temperature rise of the hydrogen cylinder than the hydrogen filling pressure and inlet pipe diameter.