Failure Behavior Of System Research Articles

Failure risk management: adaptive performance control and mission abort decisions.

The failure behavior of safety-critical systems typically depends on the system performance level, which offers opportunities to control system failure risk through dynamic performance adjustment. Moreover, mission abort serves as an intuitive way to mitigate safety hazards during mission execution. Our study focuses on systems that execute successive missions with random durations. To balance mission completion probability and system failure risk, we examine two decision problems: when to abort missions and how to select the performance level prior to mission abort. Our objective is to maximize the expected revenue through dynamic performance control and mission abort (PCMA) decisions. We consider condition-based PCMA decisions and formulate the joint optimization problem into a Markov decision process. We establish the monotonicity and concavity of the value function. Based on this insight, we show that optimizing the mission abort policy requires a series of control limits. In addition, we provide conditions under which the performance control policies are monotone. For comparative purposes, we analytically evaluate the performances of some heuristic policies. Finally, we present a case study involving unmanned aerial vehicles executing power line inspections. The results indicate the superiority of our proposed risk control policies in enhancing operational performance for safety-critical systems. Dynamic performance adjustment and mission abort decisions provide opportunities to reduce the failure risk and increase operational rewards of safety-criticalsystems.

A New Rank Estimator for Least Squares Estimation of Weibull Modulus

The Weibull distribution is widely used in reliability analysis to evaluate the failure behavior and lifetime characteristics of various systems and components. One of the most commonly used methods for estimating the parameters of the Weibull distribution is the ordinary least squares (OLS) technique, which is based on fitting a linear regression model to the transformed data. This paper proposes a new rank estimator for ordinary least squares estimation of Weibull modulus, a key parameter used as a measure of variability in the data. The new rank estimator is a quadratic function of the order statistic number, with three parameters that are optimized by Monte Carlo simulations. Using relative efficiency as a criterion, the performance of the new rank estimator is compared with three commonly used rank estimators, mean, median and hazen rank estimators, which are linear functions of the order statistic number. The results show that the new rank estimator has a significant advantage over the other rank estimators for any sample size between 3 and 150. The findings also imply that other nonlinear functions, such as cubic polynomials, could be applied to further improve the efficiency of the parameter estimators of the ordinary least squares method.

Generalized variables approach to generalized inverted exponential distribution reliability analyses with progressively type II censored data

Reliability analysis plays a crucial role in various fields, including engineering, manufacturing, and qualitycontrol. It provides valuable insights into the failure behavior of systems and products. One commonly used distribution in reliability modeling is the generalized inverted exponential distribution (GIED). The GIED distribution is known for its flexibility and adaptability to a wide range of failure data. This paper presents a new method for estimating confidence intervals and testing hypotheses for GIED distribution reliability functions based on a generalized value approach. By transforming the reliability function into the generalized value domain and calculating the generalized lower confidence limit, the proposed method offers enhanced accuracy and precision. Furthermore, the generalized p-value approach for hypothesis testing provides a robust and computationally efficient method for analyzing reliability data. The results from a real data set and Monte Carlo simulations confirm the superiority of the proposed approach over classical methods. The proposed method offers improved accuracy and computational efficiency, making it a valuable tool for reliability analysis using GIED distributions.

Effect of sintering on stress distribution of thermal barrier coatings for high temperature blade

In the process of long-term high temperature service, thermal barrier coatings (TBCs) will inevitably be sintered, leading to pores healing and porosity decline, which will affect the microstructure and the stability of mechanical properties of TBCs. To understand the relationship between the microstructural changes of sintered TBCs and the mechanical properties, in this work, the cross-sectional microstructure of TBCs at different sintering times were investigated by scanning electron microscopy (SEM). After calculating the porosity and pore size distribution, two-dimensional (2D) numerical models were developed for five different porosities (16 %–12 %) by an improved reconstruction method with layer-by-layer labeling. Based on the results of finite element (FE) analysis after thermal cycling, it was found that the coating's irregular pore structures lead to stress concentration, which affects the mechanical properties of the coating to a great extent. For the whole TC layer, the level of stresses increases and the absolute value of maximum stress augments significantly by 46 % associated with the doubled increase of stress concentration areas with the densification of coating. For the local areas of TC layer, the changes in pore morphology led to a more complex variation in local stress fields. At the TC/BC interface, the Mises stress level tends to increase with decreasing porosity during sintering. At the TC surface, coatings with lower porosities cause higher stresses near this region. The insights gained from the numerical results contribute to a better understanding of the failure behavior of real TBCs systems and help to further remedy the deficiencies and difficulties of the experimental work.

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation.

Dominant causal factors of failure performance of cross-jointed segmental lining

To understand the failure behavior of segmental linings with inclined bolts, this study proposes a group of interrelated contribution-measuring scales (CMS) to evaluate the contributions and relative importance of causal factors. In the field of failure behavior research, parametric analysis is typically used to weight the sensitivity of a system to hypothetical changes in specific parameters; however, the true contribution of influencing factors to the system cannot be assessed via this sensitivity analysis. Identifying and ranking the contributions of causal factors to system effects is important for understanding as to why an incident occurs. To this end, this study first analyzes the member force breakdown structure of a crossed-jointed segmental lining, followed by the evaluation principle, evaluation indices and criteria, and analytical calculation method of the CMS. Finally, the CMS is used to analyze the failure behavior of a cross-jointed segmental lining. The results indicate that: (1) Based on the moment search method and moment separation algorithm, the CMS can be calculated analytically, and the maximum error between the analytical method and numerical solution does not exceed 3 %. (2) For the cross-jointed lining, the magnitude of the joint effect on the lining moment is related to the attenuation of the rotational stiffness of the joint and the system nonlinear characteristics are governed by the dominant joints. (3) Using the CMS, the structure and its variation in the mechanical response of the segmental lining system in the failure history can be measured and represented effectively and the governing variables can be identified quantitatively to simplify the understanding of the system failure behavior.

High-velocity impact failure modeling of Armox 500T steel: Model validation and application to structural design

As an advanced fracture model, the Generalized Incremental Stress State dependent damage MOdel (GISSMO) provides non-linear damage accumulation formulation to better predict ductile fracture initiation associated with a wider range of material stress states. In this study, we simulate the high-velocity impact failure of Armox 500T steel through computational finite element modeling in the LS-DYNA/Explicit solver. Here, the GISSMO is used to describe the ballistic responses of the Armox 500T steel under dynamic impacts. Two sets of the most important parameters in the GISSMO (i.e., the fracture and instability surfaces) are determined for the Armox 500T steel. The GISSMO is calibrated in terms of the fade exponent and mesh regularization functions using the force–displacement curve of tensile tests extracted from the literature. The calibrated GISSMO is then scaled with the strain rate and validated by comparing quantitative measurements from ballistic experiments provided in the literature. Once validated, the model is then used to investigate the impact failure behaviors of Armox 500T steel by simulating bi-layer configurations consisting of an Armox 500T steel front plate and a rolled homogeneous armor (RHA) steel backing plate impact from a 20 mm fragment simulating projectile. The roles of the standoff distance and impact angle of obliquity on impact failure behaviors of bi-layered steel systems are explored. Altogether, results from this study provide new capabilities and insights into the design of armor structures with respect to lightweight and ballistic performance, and evaluation of impact failure behaviors of Armox 500T/RHA bi-layered systems.

SpaceSheep: Satellite Communications for Ovine Smart Grazing.

The application of IoT-based methods to support pastoralism allows the smart optimization of livestock operations and improves the efficiency of the activity. The use of autonomous animal control mechanisms frees the shepherd to carry out other tasks. However, human intervention is still needed in cases such as system failure, the bad or unpredicted behavior of the animals, or even in cases of danger, the welfare of the animal. This study documents the enhancement of an alarm generation system, initially developed within the scope of the SheepIT project, to monitor animal behavior and equipment, which warns the human operator of the occurrence of undesirable events that require intervention. Special attention was given to the use of case scenarios in places without Internet access, such as rural areas. Therefore, the system was integrated with a satellite interface, as a way of guaranteeing the timely delivery of the alarm messages. To ensure an acceptable operating cost, the system was further optimized in terms of message encoding, considering the cost of this type of communication. This study assessed the overall performance of the system, evaluated its scalability, and compared the efficiency gains from the optimization, as well as the performance of the satellite link.

Modeling progressive failure and crack evolution in a randomly distributed fiber system via a coupled phase-field cohesive model

Despite the high microstructural heterogeneity of fiber-reinforced composites, few modeling framework provides a comprehensive and detailed understanding of the failure mechanisms of these materials. The aim of this work is to present a coupled phase-field cohesive-modeling framework that can precisely capture the progressive failure and damage behaviors of multiphasic microstructures and multifiber systems. Here, the phase-field method captures crack evolution in the matrix, and a coupled cohesive-zone model is introduced to characterize interfacial debonding. The novel model framework comprises the following novel aspects. (1) A newly developed scalar indicator that directly extracts inelastic strain from the total strain field and couples the cohesive traction-separation law with the phase-field model to determine the regularized interfacial displacement jump. (2) The periodic boundary conditions in the coupled phase-field cohesive framework are incorporated to characterize crack evolution in random fiber systems. (3) A complete set of failure modes, namely crack initiation, propagation, kinking, and coalescence are characterized in highly heterogeneous solids. Parametric studies of the novel framework yield numerical results that are highly consistent with experimental findings and reveal the effects of fiber distributions, fiber volume fractions, and boundary conditions on the nonlinear mechanical behaviors of fiber-reinforced composites. The results demonstrate the excellent potential of the novel numerical framework to evaluate the mechanical performances of composite materials in engineering applications.

Discontinuous bifurcation of FRCC with zero-thickness interface modeling

In this work, firstly a fracture-based interface constitutive theory, aimed at simulating the cracking mechanisms of Fiber Reinforced Cementitious Composites (FRCCs), is presented. The discontinuous formulation assumes a hyperbolic maximum strength criterion in terms of normal and shear joint stresses. The latter are evaluated on each crack front to simulate the failure behavior of plain and FRCC systems. A non-associated plastic flow rule, in conjunction with a post-cracking softening law, is defined to complete the modeling approach. On the other hand, the use of the most-classical Mixture Theory is followed for taking into account the actions of fibers in concrete matrix. The bridging mechanisms between fibers and active cracks are defined in terms of fiber-to-concrete bond–slip rule and dowel effects. Secondly, a normalized Cracking Indicator (CI) for discrete crack is proposed in the spirit of Hill’s indicator for loss of stability of inelastic continua, to effectively evaluate the most critical direction for further loading in terms of the resulting energy release and crack opening, while accounting for the fiber direction and content. After presenting the constitutive theory and, particularly, the novel concept of the CI, numerical analyses at constitutive level are performed to evaluate the evolution of the fracture energy, post-peak strength, and critical cracking directions under variable fiber contents. Different load scenarios are evaluated, and the numerical predictions are compared with experimental data.

A stochastic time scale based framework for system reliability under a Markovian dynamic environment

AbstractMost engineering systems operate under stochastic dynamic environments. The variability and stochasticity of environmental conditions have a non‐negligible impact on the failure behavior of engineering systems. This article develops a reliability modeling and assessment framework for systems operating under a Markovian dynamic environment. The stochastic dynamic environment is characterized by a continuous‐time Markov chain. Using the cumulative exposure principle, the stochastic time scale, resulting from the cumulative effect of the Markovian dynamic environment, is computed via a Markov reward model. Based on the above settings, the system reliability model under the Markovian dynamic environment is developed. The maximum likelihood estimates and confidence intervals for the model parameters, including the transition rate matrix of the Markov chain, the reward rates of the Markov reward model, and the parameters of the baseline lifetime distribution, are obtained by utilizing the collected environment and lifetime data. The system reliability is then assessed with the estimated parameters. The effectiveness of the proposed framework are validated using simulation and through an application to a long‐term storage system. The results show that the unknown reliability model parameters can be accurately estimated, and the proposed model with the consideration of the cumulative effect of the Markovian dynamic environment can provide a more accurate reliability estimate than that without such a consideration.

Multiscale nano-integration in the scarf-bonded patches for enhancing the performance of the repaired secondary load-bearing aircraft composite structures

This study investigates a novel approach of improving the mechanical performance of scarf-repaired carbon fiber reinforced composites by integrating nanomaterials in the patch constituents. Two distinct types of carbon-based nanomaterials, thermally exfoliated graphene oxide grade-2 (TEGO) and Epocyl™ 128–06 (MWCNT) are integrated into the patch resin matrix and fiber/matrix interface in an upscaled manner. Another group of composites are repaired with pristine patches in accordance with the existing and prevalent composite repair method. Compared to the reference panel, 109.9%, 99.7% and 99.3% stiffness recoveries are achieved for the patches with CNT and TEGO-incorporated resin matrices and TEGO-electrosprayed fibers, respectively. Location-wise analyses of the test data show that the stiffness, strength, Poisson's ratio, and strain values depend on the number of patch plies in each specimen. Fractographic inspections show that the failure sites shift towards the outer areas of the scarf region demonstrating an enhanced stress redistribution due to the nanomaterials. SEM observations show that nanoparticles affect toughening mechanisms based on the type, location, and alignment of the nano-reinforcement, which in turn limits the shear-dominated failures (SDF) in the baseline and pristine patch repair system. In CNT- and TEGO-reinforced resin patches, efficient crack bridging and fracture plane tilting/twisting or crack bifurcations are observed whereas the electrosprayed TEGO particles positioned at the perimeter of fibers operate as a shield for the fibers to prevent SDFs. These findings demonstrate that failure behavior of repair systems and therefore their mechanical performance are governed by the type of nano-reinforcement and its integration process.

Angular Control Charts: A new perspective for monitoring reliability of multi-state systems

Control charts, as had been used traditionally for quality monitoring, were applied alternatively to monitor systems’ reliability. In other words, they can be applied to detect changes in the failure behavior of systems. Such purpose imposed modifying traditional control charts in addition to developing charts that are more compatible with reliability monitoring. The latter developed category is known as probability limits control charts. The existing reliability monitoring control charts were only dedicated to binary-state systems, and they cannot be used to monitor several states simultaneously. Therefore, this paper develops a design of control charts that accommodates multi-state systems, called here as the Angular Control Chart, which represents a new version of the probability limits control charts. This design is able to monitor state transitions simultaneously and individually in addition. Illustrative system examples are implemented to explore the monitoring procedure of the new design and to demonstrate its efficiency, effectiveness, and limitations.

Joint optimization of condition-based performance control and maintenance policies for mission-critical systems

The failure behavior of mission-critical systems typically depends on their performance levels, which provides opportunities to control deterioration by adjusting the performance levels. In addition to performance level selection, preventive maintenance decision is another factor affecting system survivability and mission success probability, providing a new train of thought for joint optimization of preventive maintenance and performance control for effective risk control of mission-critical systems. In existing research, mission-based maintenance and performance control decisions are seldom considered. This paper focuses on mission-critical systems and investigates the joint optimization of maintenance and performance control policies to balance mission success probability and system survivability based on the workload and system state. The problem is formulated as a Markov decision process and structural properties are analyzed to determine the optimal maintenance and performance control policies. The maintenance strategies exhibit a threshold structure, which strongly depends on the relationship between the performance level and the degradation process. For the purpose of comparison, the performance of several heuristic strategies is analytically evaluated. The example of the data transmission satellite network is given to demonstrate the superiority of the proposed strategies in reducing operations costs.

Joint optimization of mission abort and protective device selection policies for multistate systems.

For safety-critical systems such as aircrafts and submarines, mission abort is commonly deployed to enhance system survivability at the cost of reducing mission success probability. In addition to mission abort, protective device can also mitigate the failure risk of safety-critical systems by reducing the magnitude of external shocks. Considering the effect of protective device on system failure behavior, this article proposes a condition-based mission abort policy where a mission is terminated and rescue procedure starts immediately if the state of system is worse than a control limit. Based on the developed mission abort policy, mission reliability and system survivability are evaluated to analyze the risk of mission failure and system failure. The optimal mission abort threshold balancing the tradeoff between mission reliability and system survivability is investigated. Furthermore, the joint optimization of mission abort and protective device selection policies is explored by simultaneously optimizing the defensive factor and abort threshold. A numerical example on a hydraulic system is presented to illustrate the applicability of the proposedpolicies.

Dynamic Response of Bridge-Landslide Parallel System under Earthquake

In order to further understand the instability mechanism and geohazard causation when the main sliding path of the slope body is parallel to the path of the bridge, the corresponding bridge-landslide parallel system is constructed for shaking table tests. This paper summarizes the combination forms of bridge-landslide model under different position and focused on the slope body located above the bridge deck. Firstly, based on the shaking table test results of El Centro (1940), the failure behavior of bridge-landslide parallel system was evaluated, and the changes of acceleration and deformation of bridge pile were subsequently analyzed. Then, the interaction bridge structure and sliding body were explained by the spectral features. The main conclusions are as follows. First, in the model test, the landslide belongs to the thrust-type landslide. Due to the barrier function of the bridge, the main failure site of landslide occurs in the middle and trailing edge of slope body. At the same time, the acceleration value of earthquake waves is 0.3 g, which is the key to this variation. Second, the acceleration response of the measuring points on the bridge pile and landslide increases with the increase of ground elevation. If the slope structure is damaged severely, the deformation response of weak interlayer is inconsistent with the surrounding soil structure. Third, with the increase of excitation power, the dominant frequency of bridge-landslide parallel system gradually transitions from low to high frequency rate, and the interaction of the parallel system weakens the influence of river direction on frequency. Finally, under the same working condition, the dynamic response of the measuring points has obvious regularity with the change of situation. But the response of the same points is not regular due to the different earthquake excitation intensity.

Discussion On Failure Behavior of Piping Systems Under Extremely Large Seismic Loads in BDBE

Abstract To investigate the failure behavior of piping systems under severe seismic loads considering beyond design basis event (BDBE), an experimental approach to use pipes made of simulation materials was applied. "Simulation material" means the substitute material for steel to realize the structural experiment by the existing testing facilities. The simulation materials adopted in this study were pure lead (Pb) or lead-antimony (Pb-Sb) alloy. Using pipe elbows made of simulation materials, static loading tests on elbows and shaking table tests on simple piping system models composed of one or two elbows and an additional mass were conducted. From the static loading tests, the load-deflection relationship of an elbow under monotonic loading was obtained as well as the fatigue failure modes under cyclic loading depending on the several cyclic displacement levels. From the shaking table tests, several failure modes were obtained, namely, "Collapse by self-weight", "Collapse by a few cycles of input", "Ratchet and subsequent collapse", "Overall deformation", and "No failure". It was considered that the occurrence of these failure modes was affected by the ratio of the input frequency to the specimen's natural frequency, the ratio of additional mass weight to the limit mass weight, the configuration of the specimen, and the input acceleration level. The experimental results indicated that it was crucial to understand the structure's ultimate behavior when treating BDBE, and that the research approach using simulation material is effective to investigate the ultimate behavior of piping systems.

Fuzzy-FMEA risk evaluation approach for LHD machine – A case study

Improvement of productivity has become an important goal for mining industries in order to meet the expected targets of production and increased price competitiveness. Productivity can be improved in different ways. The effective utilization of men and machinery is one such way. Equipment is prone to numerous unexpected potential failures during its operation. Failure Mode and Effect Analysis (FMEA) is one of the suitable techniques of reliability modeling used to investigate the failure behavior of a complex system. In conventional FMEA, the risk level of failures, a ranking of failures and prioritization of necessary actions is made on the basis of estimated Risk Priority Number (RPN). While this approach is easy and uncomplicated, there are a few flaws in acquiring the best approximation of the failure. The estimation of RPN is made by multiplying the Severity (S), Occurrence (O) and Detection (D) alone and irrespective of the degree of importance of each input. Hence, a new risk management approach known as the Fuzzy rule base interface system was proposed in this research in order to mitigate the failures. Fuzzy FMEA is designed in order to acquire the highest Fuzzy RPN value which will be used as the focus of enhancements to reduce the probability of occurrence of some kind of failure for a second time. This study focused on the Root Cause Analysis (RCA) of underground mining machinery such as Load-Haul-Dumper (LHD). 16 potential risks of various sub-system breakdowns were identified in Fuzzy FMEA. The highest value of RPN 168 (for potential failure mode-F9) was obtained for the electrical subsystem (SSE), as was the highest FRPN 117 (F9). There is a difference between the RPN and FRPN values. The FRPN value is obtained from Fuzzy field generation with consideration of the degree of importance of the given input data. In addition, the recommendations were made based on the analysis to reduce the uneven occurrence of failures.

다기준 결정모형을 이용한 최적 점검주기 분석연구

Purpose: We developed a multicriteria decision model to assist decision-makers in determining the optimal inspection intervals based on a set of competing criteria, such as cost, downtime, and availability.<BR>Methods: A procedure based on multiattribute utility theory is proposed for developing the model. The proposed approach can simultaneously determine the optimal inspection intervals for monitoring the failure behavior of systems trails to be inspected, maintainability features, and decision-maker preferences for cost, downtime, and availability.<BR>Results: We determined the optimal inspection intervals for periodic condition monitoring based on delay time analysis and a multicriteria framework.<BR>Conclusion: We developed a multicriteria decision model that includes cost, downtime, and availability for consequences that have multiple dimensions to be considered.

Failure behavior of multi-state system considering functional and physical isolation effects

The multi-state system (MSS) is a system that may exhibit multiple states or performance levels. Most existing studies assumed that the transition probabilities from states to states are known. However, in practical engineering, the complex stress conditions lead to great difficulty of collecting the statistical data of state transitions. In this paper, based on physics-of-failure theory, we consider different levels of damages caused by failure mechanisms (FMs) are the main reasons to components’ multiple states. Besides, the physical isolation (PI) effect on degradations of FMs is also studied, which is neglected in the existing studies about the MSS with functional dependence groups. Decision-diagram based methods are used for modeling the failure behavior of the MSS. An automatic collision avoidance system is analyzed for illustrating the proposed modeling and analyzing methods. The results show that comparing to the results without the consideration of PI effect, the probabilities of different states with PI effect of multi-state components and system may decrease or increase, which depends on the actual PI effects to the stress conditions.