Quantification Of Reliability Research Articles

Accelerating reliability-based topology optimization via gradient online learning and prediction

In reliability-based topology optimization (RBTO), both quantification and maximization of structure reliability considering uncertainty of various quantities are essential. In this study, A novel gradient online learning and prediction (GoLap) method is proposed to accelerate the double-looped RBTO and reduce total computational time by using a one-hidden-layer neural networks (NN) and gradient and non-gradient based reliability quantification. The total computational time saving is achieved by (i) skipping the finite element analysis (FEA) runs and sensitivity analyses and (ii) updating the design and random variables using the gradients predicted by the NN model in selected inner and outer loops. 2D and 3D numerical examples, including an aircraft engine pylon, are presented to demonstrate the total computational time saving of up to 84.4%, and the prediction accuracy of the reliability of optimum structures for the GoLap implemented RBTO methods.

Systematic framework for handling uncertainty in probabilistic failure analysis of corroded concretes

Due to ambiguous correlations between input random variables and multi-source uncertainties from the electrochemical model, significant differences between deterministic corrosion prediction models and actual measurements are often observed. A systematic framework of quantification of uncertainties is developed for structures with correlated random variables originated from multiple sources, which allows efficiently estimating the failure probability distribution of the steel corrosion over time considering the randomness of the cover depth, the surface chloride concentration, and the chloride diffusion coefficient. After partitioning correlated random variables into different groups based on their uncertain sources, the Morris one-step-at-a-time and Sobol model is established to rank with respect to the importance of each correlated random variable. Based on polynomial chaos expansions and genetic programming methods, a more condensed set of random variables is created to propagate parametric problems. The unknown probability distribution of the input random variables is formulated by the Markov chain Monte Carlo to realize rigorous uncertainty quantification of the structural reliability. The application of the systematic framework to a set of numerical examples of steel corrosion includes experimental validation and uncertainty quantification and propagation of environmental, material and geometric properties. The results show that the framework can be integrated with parametric electrochemical models to allow robustness and reliability of corrosion prediction.

Quantification of optimal target reliability for seismic design: Methodology and application to steel buildings

The reliability levels against which seismic standards are calibrated are currently quantified based on accepted practice, which may not lead to optimal designs that minimize the lifecycle cost. Deviation from optimal design is even more pronounced in countries whose regulations are adopted from the few leading codes. A major challenge in the quantification of optimal target reliability for the countries with such adopted codes is the lack of risk models that are suited for the local construction industry and design practices. This paper presents a holistic risk-based framework to quantify the optimal target reliability for seismic design through the notion of minimum lifecycle cost. To enable the implementation of the framework in countries with adopted provisions, the present paper proposes an approach to adapt the available risk models to the local conditions of those countries. The proposed framework is showcased through the National Building Code of Iran, which is adopted from the codes of the United States, using a case study of five residential steel building archetypes. The archetypes have various structural systems and comprise intermediate moment-resisting frame (IMF) buildings of various heights and special concentrically braced frame (SCBF) buildings. The results reveal 50-year optimal target reliability indices of 2.5, 2.0, and 1.9 for low-, mid-, and high-rise IMF buildings and 2.1 for mid-rise SCBF buildings.

Preliminary results on acoustic emission and vibration-based monitoring of locally corroded reinforced concrete beams

The current tendency to increase the lifetime of reinforced concrete (RC) structures creates concerns about their serviceability and safety. Reinforcement corrosion is the most common deterioration process in RC, which can severely decrease the structural capacity. Corrosion causes a reduction of the rebar’s cross section, cracking and spalling of the concrete cover due to the expansion of the corrosion product and a decrease of the bond strength at the reinforcement-concrete interface. Assessing the structural capacity of corroded existing structures remains an important issue to maintain good safety and to achieve more targeted repairs. On-site quantification of structural reliability requires advanced non-destructive techniques to evaluate the damage and efficient methods for structural health monitoring. This paper presents the initial results of an experimental campaign on corroding RC beams. Beams with a length of 3 m are subjected to local accelerated corrosion while being monitored with the acoustic emission (AE) technique and dynamic vibration testing (DVT). AE investigates the damage progress and underlying causes of the cracks. When cracks are formed, the stress redistribution in the material causes elastic waves, which can be recorded by piezoelectric transducers placed on the surface of the beam. Examination of the waveforms allows for a localization and characterization of the damage source. Additionally, DVT observes the change in modal parameters of the beams, measured by accelerometers. These changes can be related to a loss of stiffness in the structure, resulting in a perception of the decrease in structural capacity. The combination of local (AE) and global (DVT) monitoring techniques improves the damage inspection as both the damage process and the loss of stiffness are observed. The results of AE provides information when the cracks have not yet reached the surface, while DVT is most relevant when severe damage already occurred. After corrosion, a four-point bending test is performed to evaluate the remaining structural capacity.

Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference.

In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied.

The Quantification of Operational Reliability of Agricultural Tractors with the Competing Risks Method

The Quantification of Operational Reliability of Agricultural Tractors with the Competing Risks Method

Quantification of the chemical reactor reliability in the presence of uncertainties/errors in input parameters

Errors and uncertainties in the input parameters can have a significant effect on the reliability, quality and safety of a chemical process. However, the majority of mathematical models use deterministic operating conditions. Therefore, to have a more realistic mathematical design, it is essential to take them into account in order to analyse how they propagate in a chemical reactor and influence the safety criteria.In this work, random variations were imposed on the input parameters of a batch chemical reactor and the impact on the system reliability was studied. An exothermic reaction of the synthesis of peroxyformic acid was chosen and the maximum temperature of this reaction was taken as a safety criterion. The case of impurity presence in the reactor was also investigated. This impurity can lead to the reactant decomposition and consequently to thermal runaway, despite the adequate setting of the operating conditions.The probability density functions (PDFs) for the reaction maximum temperature were used to estimate the system reliability. Theses PDFs were obtained by solving Latin hypercube sampling and genetic algorithm combined with the least square method. Stochastic treatment provides a more significant information concerning the reliability and highlights the nonlinearity of the process.

Reliability Quantification of Railway Electrification Mast Structure Considering Buckling

This paper aims to quantify and assess the reliability of mast structures as a part of ensuring structure safety. The mast structure is a basic aspect of the overhead line electrification equipment (OHLE) used in railway systems. This structure is very important as the failure of structure leads to the failure of an electric system that supplies the power to the train. To ensure structural safety and reliability, this paper thus analyses the reliability index of the mast, stay tube, and bracket tube structures. According to Eurocode, buckling resistance under compression of these parts were calculated based on specific material properties, and the load condition of these structures is based on Australian Railcorp document TMC331. In this paper, the strength load combination with the wind loading on the wire at 45° on the track is considered in particular as being the worst load combination for structures to bear, and the random variables used to affect reliability probabilistic analysis. Various parameters including self-weight, wind load, dimension parameters, materials, geometrical properties are taken into consideration. Statistical models of these parameters are taken from previous studies. The reliability index value was calculated via quantification of structure reliability using the first-order reliability method (FORM). Finally, a sensitivity analysis is used to evaluate the impacts of yield strength, length, cross-section, density, and load combination on reliability. The obtained results show that increasing length of structure can potentially reduce the reliability of mast structure to buckling resistance while the density of material also plays a major role in the reliability index. The findings will provide the structural safety criteria of the railway mast structure and improve the standard design to mitigate the risks and unplanned maintenance due to the uncertainties.

Reliability assessment of mechatronic systems considering multi covariates with system topology

AbstractReliability assessments of mechatronic systems, considering covariates, are becoming more complex not just as a result of the integration of more diverse technologies into such systems but also because of increasing complexities in system topology; but traditional reliability assessment models with covariates tend to ignore the influence of system topology. This paper addresses such an exclusion and proposes a novel methodology dedicated to the analysis and quantification of system reliability using system topology and multiple covariates. There are essentially two new aspects contributing to the overall methodology. Firstly, using network theory, the system topology is abstracted as a network; in which network nodes represent a mechatronic system's minimum maintenance units, and the connections between them are regarded as edges. Secondly, the resultant topological network is combined with the inherent properties of the minimum maintenance units to construct the system baseline hazard function and considering the associated multi covariates from the perspective of failure propagation to provide a foundation for assessing system reliability. To validate the proposed methodology, by way of a case study, it was applied to the reliability assessment of a traction power supply system for a high‐speed train. The results, compared with three other assessment approaches, suggest that the proposed methodology provides for significantly more accurate assessments while requiring less computational resources. Furthermore, it can be flexibly extended to many other mechatronic systems.

A quantification of the short-term reliability of HDPE geomembrane seaming methods

This study provides a preliminary approximate quantification of the difference in seam reliability between fusion seams and extrusion seams of high-density polyethylene (HDPE) geomembranes based on leaks that exist at the end of installation. The two different seaming methods as well as seam testing methods are discussed. The reliability of the two seaming methods is compared using the number of leaks associated with extrusion seams versus fusion seams, which were found using electrical leak location surveys performed on a sampling of large geomembrane-lined facilities.

A computational framework for finite element modeling of traveling loads on bridges in dynamic regime

AbstractThe rigorous modeling of traveling loads on bridges in a dynamic regime is not an easy task as the traveling load contributes to the dynamic of the structural system with its mass and damping. In this paper, a simplified approach for traveling loads on bridges in dynamic regime is proposed. The main simplification consists in neglecting the traveling mass. The validity of the proposed simplification is checked with a sensitivity analysis considering a few realistic case‐study bridges. The ranges of velocity and traveling‐force/bridge‐weight ratio are identified. Eventually, the proposed methodology is applied to two case studies, (i) an existing Italian reinforced concrete viaduct and (ii) a pedestrian bridge. The applications show that the proposed simplified approach can be applied to a range of civil engineering problems such as the quantification of the structural reliability of existing structures or the assessment of pedestrian comfort.

Status and Gap in Rechargeable Lithium Battery Supply Chain: Importance of Quantitative Failure Analysis

Rechargeable lithium batteries (RLBs), including lithium-ion batteries (LIBs), are accelerating the electrification of transportation and grid energy storage. This transformation of the transportation and energy sector could bring more clean energy into our energy security. The RLB technology is growing rapidly in these sectors due to substantial cost reductions and mobility needs. Yet, the durability, reliability, and safety issues of RLB remain concerns due to the nature of high-energy content in RLB. The concern of limited resources of the critical materials to sustain the RLB use is also escalated. Reuse and recycling of RLB to extend the useful life and recovery of the critical materials become important. Here, we provide a critical review of these topics to give a timely assessment of the status and gap of the RLB technologies and their supply chain. A key concept to use a quantitative failure mode and effect analysis is proposed to help advance RLB design, development, manufacturing, and deployment. The approach can be a viable method to enable physical principle-based technology assessment, failure identification, quantification, and verification of reliability and safety issues in the RLB supply chain.

A review of probabilistic creep assessment reporting relating to volume 2/3 of the R5 procedure

Probabilistic assessments allow the uncertainties present in reactor design and operation to be understood and reflected in the quantification of reliability. Increased awareness of the limitations of deterministic structural integrity assessments have highlighted that the unquantified margins do not provide an adequate measure of component risk.The R5 procedure is a “UK nuclear power industry standard, frequently used in safety cases for structural integrity assessments of Advanced Gas-Cooled Reactor components operating in the creep range” [Assessment Procedure R5, EDF Energy Nuclear Generation Ltd.]. Within this procedure, Volume 2/3 covers creep-fatigue crack initiation in defect-free structures and a drafted appendix provides guidance on the application of probabilistic approaches.A review of probabilistic approaches using the R5 procedure has been conducted, including a wider review of probabilistic assessments to ascertain general good practice. The review identifies a lack of consistency in the reporting of probabilistic assessments, particularly for correlations and convergence of outputs. Providing a structured method for reporting probabilistic assessments would be valuable to ensure consistent application and enable reproducibility of results.

A Confidence-Guided Technique for Tracking Time-Varying Features

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. The efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

Performance characterization of plain and CFRP-bonded concrete subjected to sulfuric acid

This paper presents the durability performance of concrete subjected to sulfuric acid. Accelerated conditioning is conducted at a 5% concentration employing concrete blocks bonded with carbon fiber reinforced polymer (CFRP) sheets, which are used for rehabilitating impaired structural members, and plain concrete blocks. After deteriorating the specimens for up to 9 weeks, various physical and mechanical tests are carried out such as digital microscopy, resonance frequency, porosity, thermogravimetry, and flexural loading. The initial uptake of sulfuric acid raises the mass of the concrete by 5.9%, on average; however, with the increased exposure period, the mass decreases by 19.8% because of the dissolved cement paste and enlarged pores. The presence of a CFRP layer partially hinders the ingress of sulfuric acid and improves the integrity of the concrete. Irreversible damage in the conditioned specimens reduces their resonant frequency and dynamic modulus as low as 20.3% and 49.0%, respectively. According to thermogravimetric results, the acid conditioning changes the microstructure of the concrete. While the flexural capacity of the blocks rises over 409% after CFRP-bonding, the level of uncertainty increases owing to the irregular deterioration of the CFRP-concrete interface. Furthermore, the capacity reduction and flexural stiffness of the specimens, as well as the debonding characteristics of CFRP, are dependent upon exposure period. A probability-based analytical model is formulated to complement the experimental findings, including the quantification of hazard and reliability. By comprehending the unexplored degradation mechanisms of concrete with and without CFRP-bonding under the sulfuric acid environment, a link is established between material-level damage and sustainable rehabilitation systems.

Integrating reliability and resilience to support the transition from passive distribution grids to islanding microgrids

Reliability and resilience are the main drivers for the transition of distribution networks from passive systems to active microgrids; as such, quantifying the potential benefits of microgrids in the design phase can support the transition of passive distribution networks into microgrids. To support this transition, this paper presents a mathematical optimization model which integrates techno-economic, resilience and reliability objectives. Storage and distributed generation are optionally installed to complement renewable generation, enabling the microgrid to supply priority demands during stochastic islanding events with uncertain duration. Islanding due to external events is combined with a detailed model of internal faults for comprehensive quantification and optimization of microgrid resilience and reliability. Minimizing the interruption costs yields optimal capacities and placements of distributed energy resources and new lines for reconfiguration. The proposed method produces microgrid designs with up to 95% reliability and resilience gain and moderate cost increase in two benchmark distribution networks using data from the United States Department of Energy. The developed methodology is scalable to large networks owing to the tailored Column-and-Constraint-Generation approach, reducing the computational time by 92% compared to state-of-the-art Benders decomposition for a 123 bus network.

SHyFTOO, an object-oriented Monte Carlo simulation library for the modeling of Stochastic Hybrid Fault Tree Automaton

Dependability assessment is a crucial activity to ensure the correct operation of complex systems. The output of dependability assessment activities include the quantification of reliability, availability, maintenance and safety related metrics. These metrics can assist in the identification of the system weak points or in the conception of mitigation strategies to increase the system dependability level. The development of advanced computer-aided methodologies to support dependability assessment activities is essential to automate and reduce the efforts implied by this process and similarly, the development of accurate dependability assessment methods is very important to increase the quality of the results. In this context, it is possible to identify different contributions that improve the dependability assessment through general-purpose modeling methodologies. However, existing solutions are ad-hoc applications specified with low-level stochastic formalisms and this complicates their adoption in the industry. Accordingly, this paper presents Stochastic Hybrid Fault Tree Automaton (SHyFTA) based simulation algorithm that allows the accurate dependability analysis of repairable multi-state systems. SHyFTA integrates the stochastic and deterministic operation of the system under study as well as their interactions. The algorithm is formalized through an object-oriented software architecture, which is developed as a software library for the modeling and simulation of repairable SHyFTA models. Following the proposed architecture, a Matlab® implementation of this library, SHyFTOO, has been developed and validated with a thorough test campaign. In order to provide a guideline to the end-users and show the potential of the SHyFTOO library, the case study of a feed-water pumping system is implemented in detail and it is used to evaluate different preventive maintenance policies. The SHyFTOO library can open the way to further investigations that address the interactions between the failure behavior and the functional operation of a system and their combined effect on system dependability.

An Analysis of Reliability of Military Vehicles

Abstract The paper describes issues related to reliability of military vehicles based on recorded operational events. Seeking the quantification of reliability for exploiting vehicles in military units, an extensive analysis of factors shaping the reliability level was made, taking into consideration all phases of military vehicles existence and a peculiar character of the exploitation process of military vehicles. The importance of reliability research in the decision process optimization was emphasized, controlling the efficiency and availability of the exploitation system.

A fuzzy system hazard analysis approach for human-in-the-loop systems

ObjectiveThe objective of this study was to show how fuzzy representation of hazard risk (membership functions and risk bands) could be used along with quantification of human-automation reliability to produce precise risk assessments and broad sets of control measures. BackgroundAlthough some prior research has integrated fuzzy logic into several hazard analysis techniques and identified advantages in terms of risk estimation and recommended control measures, in applying systems hazard analysis (SHA), analysts still rely on discrete risk assessment approaches. The SHA approach is the only hazard analysis technique that accounts for hazards due to interactions among system components. However, the original method does not consider the impact of human-automation reliability in risk exposure calculations. MethodThe Mamdani max-min inference method was used to calculate a “risk-reliability” (R-R) score based on a fuzzy definition of frequency of hazard occurrence, severity of hazard outcomes, and system reliability. The application of the proposed model is presented in the context of a complex-human-in-the-loop system using the MATLAB® fuzzy logic toolbox. ResultsThe fuzzy R-R score provided a precise risk assessment and led to broader set of control measures, as compared to use of the original SHA in a construction safety application. The proposed fuzzy inference system can be used by analysts through a graphical user interface. ConclusionThe enhanced SHA supports broader safety control recommendations and provides more comprehensive results than the original technique. However, the new approach needs to be further evaluated for reliability in different applications.

Analysis of the performance of an integrated small-scale biomass gasification system in a Canadian context

Small-scale downdraft biomass gasification is a promising technology for off-grid combined heat and power generation utilizing Canada’s vast biomass resources. As small-scale gasifiers are currently emerging into the Canadian market, the current study investigates the required feedstock characteristics for stable operation using Canadian softwood, along with the quality of the biochar produced as a byproduct. It was found that reducing feedstock moisture from 20 to 9% increased overall system efficiency from 8.2 to 12%. Equilibrium simulations were utilized to evaluate the deviation from ideal conversion when using feedstock outside the gasifier feedstock specifications. These showed the gasifier was operating close to equilibrium, with the lower heating value of the actual producer gas deviating by an average of 9% from simulated results. This study provides a novel methodology for the quantification of reliability and efficiency of these systems which can be used to evaluate the economic benefit of these systems in Canada.