Minimum Reliability Research Articles

Availability-based maintenance prioritization for data centres: a dynamic programming approach

Digital infrastructures and Data Centres (DCs) have played a significant role in society for several years in conducting digital transformation. Recently after the global COVID-19 crisis, the importance of DCs has greatly increased because of facing new realities. High demand for digital commerce, online meetings, and other online and cloud services in the digitisation process has given the DC industry special attention. Regarding the critical components of DCs, special requirements, such as minimum availability, reliability, quality, and performance levels, should be addressed in DC operations. Therefore, implementing optimised maintenance management is beneficial for these infrastructures to reduce the risk of failure and ensure a minimum level of reliability and availability while minimising maintenance costs. This paper presents a novel, availability-based model for optimising maintenance prioritisation in DCs. The model leverages a Multiple 0-1 Knapsack problem, solved using Dynamic Programming (DP), to determine the optimal set of components for maintenance actions, considering both DC availability requirements and budget limitations. By incorporating reliability and availability analysis, the DP-based approach generates a maintenance prioritisation plan that maximises DC availability within budgetary constraints. This study provides a practical framework for enhancing maintenance decision-making in DCs, ultimately contributing to more efficient and resilient digital infrastructures.

AISC360-22 design equations for high-strength concrete-filled tubes: From rectangular to circular sections

The current AISC Specification lacks clear guidance on the design of circular concrete-filled tube (CCFT) members, while updates to material strength restrictions and the P-M interaction design curve apply exclusively to rectangular shapes. This research is a response to the growing call within the engineering community to update the AISC 360–22 design guidelines for CCFTs. A deterministic and probabilistic approach is presented for assessing the applicability of unifying current AISC 360–22 P-M interaction design equations for use in circular-filled tubes. This assessment is informed by the most up-to-date experimental test results comprising 508 eccentric axially loaded CCFTs with different material strength combinations. Reliability analysis using Monti Carlo simulation demonstrates that the authors' proposed P-M interaction design equations in Appendix II of the AISC 360–22, along with the proposed equation for column, and beam maintain a consistent level of accuracy across different material strengths of circular shapes. An increased safety factor of ϕc = 0.85 for columns and ϕb = 0.9 for beams is recommended for a more economical design, aligning with AISC 360–22's requirement for achieving a minimum reliability index of 2.6 under commonly used load combinations. The research findings present a compelling proposal for inclusion in design codes, offering practical tools for engineers to enhance structural design and safety optimization.

A Simheuristic Approach to Scheduling Sustainable and Reliable Maintenance for Bridge Infrastructure

Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming to minimize CO2 emissions while meeting minimum reliability requirements and adhering to workforce and budget constraints. To achieve this, we present a Simheuristic algorithm that combines a metaheuristic core based on the Adaptive Large Neighborhood Search metaheuristic with a Monte Carlo simulation module. This integration allows for the evaluation of optimized scheduling solutions, accounting for the inherent randomness in the structural deterioration process. The proposed approach is tested in a comparative analysis against traditional time-based and condition-based scheduling methods. Results from diverse bridge portfolios demonstrate that the proposed algorithm offers improved performance in terms of both total costs and CO2 emissions.

Precision surgical education

Information and data are accelerating the implementation of competency-based medical education. The adoption of precision education can contribute to this purpose. This article discusses the extent to which precision surgical education can be used in assessing the minimum reliability standards of future surgeons - given the advent of Entrustable Professional Activities - and as an option to strengthen the career trajectory of residents.

Graphical Features & Perceptions of Reliability

This study examines the impact of graphical features on perceived reliability and user trust in automated systems. Participants viewed 60 static graphs showing system reliability over time, with variables such as maximum reliability, minimum reliability, average reliability, cumulative probability of success/failure, and slope. Each graph was presented with different success or failure rates. Participants rated the system’s reliability, predicted success rates, and willingness to rely on the system. Analysis revealed that perceived reliability was most influenced by minimum reliability when success was depicted and maximum reliability when failure was depicted. These findings suggest that user perceptions of reliability are complex and not solely dependent on the expected features of system performance, indicating a need for further research into the relationship between graphical representations and perceived reliability.

The first-order time-variant reliability expansion method

Time-variant reliability problems are frequently encountered in engineering due to factors like material degradation or random loading modeled as random processes. The PHI2 method, which employs the First Order Reliability Method (FORM), is commonly used to solve such problems. However, it requires repeated searches for Most Probable Points (MPPs), making it computationally expensive. To improve efficiency with little sacrifice of accuracy, this study proposes a First Order Time-variant Reliability Expansion (FOTRE) method, which provides an efficient explicit formulation for MPP regarding time, in contrast to the expensive optimization approach of the PHI2 method. It requires only a single accurate search for the so-called “worst MPP” over the whole lifespan and offers the “adaptive accuracy of outcrossing rate”, which avoids the repeated search for MPPs ensuring computational accuracy. The inspiration behind the FOTRE method stems from the observation that the outcrossing rate tends to be small at time points with relatively large reliability indexes compared to the minimum reliability index βmin, which has a negligible impact on the subsequent structural failure probability over the entire lifespan. This innovative approach significantly improves the efficiency of solving time-variant reliability problems without compromising much of the numerical accuracy. The effectiveness and accuracy of the FOTRE method are demonstrated through several numerical examples.

Energy-efficient scheduling for parallel applications with reliability and time constraints on heterogeneous distributed systems

Reliability is a crucial index of the system, and many safety-critical applications have reliability requirements and deadline constraints. In addition, in order to protect the environment and reduce system operating costs, it is necessary to minimize energy consumption as much as possible. This paper considers parallel applications on heterogeneous distributed systems and proposes two algorithms to minimize energy consumption for meeting the deadline and satisfying the reliability requirement of the applications. The first algorithm is called minimizing scheduling length while satisfying the reliability requirement (MSLSRR). It first transforms the reliability requirement of the application into the reliability requirement of the task and then assigns the task to the processor with the earliest finish time. Since the reliability generated by MSLSRR is often higher than the reliability requirement of the application, and the scheduling length is also less than the deadline, an algorithm called improving energy efficiency (IEE) is designed, which redefined the minimum reliability requirement for the task and applied dynamic voltage and frequency scaling (DVFS) technique for energy conservation. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume the least energy.

A layer assigned probability space partition method for structural small failure probability problem

The Physical Synthesis Method (PSM) stands out as a robust framework for conducting structural reliability analyses due to its clear conceptual foundation. However, this approach often necessitates significant computational resources when addressing scenarios with small failure probabilities. In response to this challenge, this study introduces a layer assigned probability space partition method designed to identify pivotal points based on the ultimate bearing capacity failure criterion of structural components within the PSM framework. Drawing inspiration from Harbitz's β-sphere, this method effectively utilizes the minimum reliability index of components to discern essential representative points within the probability space, thus streamlining computations. The efficacy of this approach is showcased through two case studies: a simply supported beam and a six-story reinforced concrete frame. The outcomes demonstrate that the proposed method, when integrated with PSM, exhibits a substantial enhancement in efficiency compared to the conventional Monte Carlo method. Besides, under equivalent computational resources, it achieves superior computational accuracy compared to the importance sampling method, particularly in scenarios with small failure probabilities. Furthermore, by introducing the notion of a common safe domain, this method addresses challenges in structural reliability analyses involving multiple failure surfaces.

Network design with vulnerability constraints and probabilistic edge reliability

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

Proposed Improvements to AISC 360-22 Chapter F for Built-Up I-Section Members

Recent analytical and experimental data has shown that the AISC 360–22 Ch. F lateral-torsional buckling (LTB) resistance equations significantly overpredict the strengths of a wide range of built-up I-girders. These overpredictions are mainly attributed to unaccounted-for yielding effects and an insufficient characterization of the plateau strengths by the AISC 360–22 compact- and noncompact-web limits. The objective of the current study is to present and justify the implementation of a new set of recommended AISC 360 Ch. F provisions (AISC 360 Rec.) for built-up I-girders. These recommended provisions were developed by making six targeted improvements to AISC 360–22, which consist of changes to the web classification limits, the inelastic/elastic LTB strength threshold, the handling of moment gradient, and the representation of the LTB strength curve itself. Two independent studies evaluate the performance of the AISC 360 Rec. equations. In the first study, professional factors (Mtest/Mn) were calculated and statistically evaluated using a newly updated LTB experimental database. The second study compares the LTB strength curves for targeted cross sections to the results from nonlinear shell finite element analysis (FEA) test simulations. The findings demonstrate that the recommended approach provides accurate-to-conservative strength predictions that meet or exceed a targeted minimum reliability index of 2.6, which AISC 360–22 does not achieve for a range of I-section member characteristics. The development of the AISC 360 Rec. provisions is significant as it fixes the strength overprediction problems recently observed for built-up I-girders while maintaining good accuracy for members where the AISC 360–22 equations perform well.

Psychometric properties of the Young’s Early Maladaptive Schema Questionnaire – Short Form (YSQ-S3) in Colombia

This article presents two studies evaluating the construct validity and reliability of the “Young’s Early Maladaptive Schema Questionnaire – Short Version” (YSQ-S3; Young, 2005). The first study, with 1004 participants, showed that the YSQ-S3 had excellent overall reliability according (Cronbach’s  = .97;  = .97); while the factors showed acceptable to good reliability according to the range in which the minimum and maximum reliability coefficients of the 18 scales were placed ( from .67 to .89, and  from .67 to .86). Confirmatory factor analysis (CFA) showed that the model with 18 first-order correlated factors had the best fit indices. In addition, using CFA with second-order factors, evidence was found to support the hierarchical organization of the instrument into five second-order domains. Study 2, with 806 participants, successfully replicated the best model of study 1 against an alternative one. It is concluded that the YSQ-S3 is a valid and reliable instrument for the Colombian adult population.

A mixed uncertain structural reliability analysis method considering random and convex set variables with correlation

AbstractIn this paper, a mixed model reliability analysis method is put forward for the problem of assessing the reliability of complex engineering structures containing both random and convex aggregate variables. By integrating the ellipsoidal model with correlation and the interval model, the uncertainty region characterized by the ellipsoidal model with correlation is optimized with full consideration of the limited amount of engineering structure sample data, and the risk region represented by the reliability model is redefined, a new mixed reliability assessment criterion is established, and the minimum safe nonprobability reliability index of the structure is built. A key reference is offered by the safety limit diagram of constant reliability indexes set for the first time for the optimal design of engineering structure reliability. The proposed mixed reliability model is compared and analyzed with three classical models. The sensitivity of nonprobability reliability indexes, influenced by random variable parameters and correlation coefficients, is analyzed. This verification confirms that the new reliability model not only provides an accurate assessment of engineering structures' reliability but also lowers the computational demands of engineering design. In this paper, aero‐engine blades and wind turbine blades are taken as examples to expound the validity of the reliability model built using this method and its importance to the structural safety analysis of actual engineering.

Reliability generalization meta-analysis of orthorexia nervosa using the ORTO-11/12/15/R scale in all populations and language versions

BackgroundThe ORTO scale was developed in 2004 as a self-report questionnaire to assess symptoms of orthorexia nervosa (ON). ON is an unhealthy preoccupation with eating healthy food. The scale aims to measure obsessive attitudes and behaviors related to the selection, purchase, preparation, and consumption of pure, healthy food. Since its development, the ORTO-15 has been adapted into several shorter versions. The objective was to conduct a reliability generalization meta-analysis of the ORTO scale and its variant versions in all populations and languages.MethodsA systematic literature search was conducted to identify studies reporting the internal consistency of ORTO. Random-effect models were used to evaluate summary statistics of reliability coefficients, weighting the coefficients by the inverse variance using the restricted maximum likelihood method. The heterogeneity among the reliability coefficients was evaluated and assessed using numerous statistical metrics. The tau (τ), tau2 (τ2), I2, H2, R2, df, and the Q-statistic are among those obtained. Meta-regression analyses were used to examine moderators such as age and sex.ResultsTwenty-one studies (k = 21) involving 11,167 participants (n = 11,167) were analyzed. The overall effect estimate on internal consistency was 0.59 (95% CI 0.49–0.68), with a minimum reliability coefficient of 0.23 and a maximum reliability coefficient of 0.83. The heterogeneity statistics were found to have an I2 of 99.31%, which suggested high heterogeneity owing to a decrease in the confidence interval (95% CI) and an increase in variability. Sensitivity analysis revealed that a few studies strongly influenced the overall estimate. Egger’s test suggested possible publication bias. Neither age nor sex significantly moderated reliability via meta-regression.ConclusionsThe ORTO scale has a relatively low pooled reliability coefficient. Alternative ON assessment tools with enhanced psychometric properties are needed. Clinicians should not base diagnoses or treatment decisions on ORTO alone. Comprehensive psychiatric assessment is essential for accurate ON evaluation.

APPLICATION OF PREDICTIVE CONTROL TO IMPROVE THE QUALITY OF MANUFACTURING PROCESSES

A progressive method of condition -based maintenance of production processes is considered. It is shown that an effective means for its implementation is the use of predictive control. Proposals have been formulated for the use of a reliability-cost criterion that characterizes the effectiveness of maintenance with predictive performance, quantitative relationships have been derived that make it possible to evaluate the reduction in the number of gradual failures when using maintenance with predictive control, as well as establishing the relationship between the effectiveness of maintenance and the reliability of forecasting. Using the concept of unused resource, the minimum reliability of predictive control is determined. Relationships have been obtained and practical recommendations have been formulated related to the selection of production process parameters for forecasting, which are based on the principle of their comparison based on the steepness of change over the observation interval, the root-mean-square error of prediction and the cost of costs for measures to monitor and prevent failures.

Reliability analysis of the cast-in-place bored pile with different defects

The defects of the cast-in-place bored piles heavily influence their integrities and bearing capacities. The bearing capacity and reliability index of the cast-in-place bored piles with different defects were studied using the finite element method. The accuracy of the numerical model is verified by the field test results of an in situ cast-in-place bored pile. Parametric studies are conducted to investigate the toe debris, mud cake, and necking and bulging defects on the ultimate bearing capacities of the cast-in-place bored piles. The results indicate that maximum reduction in the ultimate bearing capacities of the cast-in-place bored pile was observed, with the ultimate bearing capacity of the toe debris defect decreased by 17%, that of the mud cake defect decreased by 19%, that of the necking defect by 6.1%, and that of the pile bulging defect increased by 40.9%. The minimum reliability index can be 0.81 when the diameter of the pile necking defect is 0.2 m. The maximum reliability index can be 3.63 when the diameter of the pile necking defect is 2.0 m.

Configuration size optimization of gas-electric hybrid power systems on ships considering energy density and engine load response

System component sizes and their energy densities impact the long-term ship energy efficiency and economics, whereas power source load response influences system transient behavior, posing challenges to system design and energy management when clean fuels are used onboard. In this paper, a novel component sizing method is proposed for integrating reciprocating gas engines and energy storage systems (ESS) on ships, considering energy density and load response simultaneously. By specifying gas engine powers and ESS capacities as normalized optimization variables by ship scales, this method optimizes them based on the A. J. Klee multi-index evaluation and single-objective particle swarm optimization (PSO). Energy densities of engines and ESS were introduced to demonstrate the trade-off between the power-splitting and increased system mass and hull resistance. The engine load response was incorporated into the rule-based energy management strategy (EMS) to limit power ramps. This method was examined in the case study by taking four cruise ships as examples. It can be found that implementing the proposed evaluation-based optimization can improve cruise ship energy efficiency while maintaining reliability over the voyage. It is possible to reduce fuel consumption and carbon emissions by up to 17.1% and 14.8%, respectively. The minimum system reliability can be increased from 0.025 to 0.475 during transients. Furthermore, ship life-cycle costs can be reduced compared to the original design method, and the payback period for gas engines, ESS, and fuel tanks can be shortened to less than six years. Among the various cases, the definition of normalized optimization variables broadens the applicability of this method away from a specific ship, allowing it to be applied to ships of various tonnages and types.

Energy-Efficient Functional Safety Design Methodology Using ASIL Decomposition for Automotive Cyber-Physical Systems

Automotive cyber-physical systems (ACPS) are typical cyber-physical systems because of the joint interaction between the cyber part and physical part. Functional safety requirement (including response time and reliability requirements) for an ACPS function must be assured for safe driving. Auto industry is cost-sensitive, power-sensitive, and environment-friendly. Energy consumption affects the development efficiency of the ACPS and the living environment of people. This paper solves the problem of optimizing the energy consumption for an ACPS function while assuring its functional safety requirement (i.e., energy-efficient functional safety for ACPS). However, implementing minimum response time, maximum reliability, and minimum energy consumption is a conflicting problem. Consequently, solving the problem is a challenge. In this paper, we propose a three-stage design process toward energy-efficient functional safety for ACPS. The topic problem is divided into three sub-problems, namely, response time requirement verification (first stage), functional safety requirement verification (second stage), and functional safety-critical energy consumption optimization (third stage). The proposed energy-efficient functional safety design methodology is implemented by using automotive safety integrity level decomposition, which is defined in the ACPS functional safety standard ISO 26262. Experiments with real-life and synthetic ACPS functions reveal the advantages of the proposed design methodology toward energy-efficient functional safety for ACPS compared with state-of-the-art algorithms.

Optimization of selective maintenance problem with stochastic durations in mission-oriented system subjecting to [formula omitted]-dependent competing risks

The mission-oriented systems (MOS) are often required to operate a consecutive of missions interspersed with finite breaks. For MOS, an appropriate maintenance strategy is the selective maintenance (SM). The SM problem (SMP) aims then to identify an optimal set of components to be maintained under maintenance resource constraints such as limited break durations. However, most existing SM models merely deals with either stochastic independent (s-independent) components in MOS or deterministic mission and break durations. To overcome this unrealistic restriction, this paper develops a novel SM model for optimizing the SMP in MOS operating under stochastic mission and break durations and s-dependent competing risks (CRs). Durations of missions and breaks are respectively governed by normal and uniform distributions, while the s-dependent CRs are captured by the copula function of which the unknown parameters are estimated relying on the two-stage MLE method. Furthermore, due to the stochastic break durations, an integrated importance measure (IIM) is introduced to measure the maintenance priority on selected components. The resulting SM optimization model is therefore formulated as a mixed-integer nonlinear program (MINLP) whose objective is to minimize the total cost while ensuring the required minimum mission reliability. A differential evolution (DE) algorithm is designed to solve the MINLP. Two numerical experiments are conducted on a ball screw system. Results obtained demonstrate the validity of our proposed model, in addition to the robustness of the solution algorithm designed.

Measures for Quality Assurance of Electronic Examinations in a Veterinary Medical Curriculum.

Since 2008, electronic examinations have been conducted at the University of Veterinary Medicine Hannover, Germany which are analyzed extensively in the current study. The aim is to assess the quality of examinations, the status quo of the electronic examination system and the implementation of recommendations regarding the conduct of exams at the TiHo. Based on the results suitable indicators for the evaluation of examinations and items as well as adequate quality assurance measures and item formats are to be identified. For this purpose, 294 electronic examinations carried out from 2008 to 2022 of the veterinary medicine course with an average of 248 participants each were evaluated with regard to the quality criteria reliability, difficulty index, and discrimination index. The main finding was that the number of items and the proportion of reused questions were identified as factors through which the quality of the examinations can be increased with simple adjustments. A higher number of items led to better reliability, whereby the required minimum reliability in examinations of 0.8 was reliably achieved from an item number of 98 questions. The proportion of reused questions should be kept low, as these had a negative influence on the characteristic values. Measures accompanying examinations, such as training of question authors and a pre- and post-review process, should also ensure the quality of examinations. For the post-review process, the distribution of examination results, reliability, item and distractor analysis are adequate indicators for evaluating examinations.

Joint optimization of selective maintenance and repairpersons assignment problem for mission-oriented systems operating under [formula omitted]-dependent competing risks

Mission-oriented systems (MOS) are designed to operate a series of missions seceded by scheduled breaks of limited duration. For MOS, the most appropriate maintenance strategy is the selective maintenance (SM). Under limited maintenance resources, the SM problem (SMP) aims to identify the optimal set of components to maintain in order to meet the required minimum performance for the next missions. However, the majority of the existing SM models merely relies on stochastic independent (s-independent) components. To overcome this restrictive and unrealistic assumption, this work develops a novel SM approach for jointly optimizing SM and repairpersons assignment (RA) problem (RAP) in a MOS operating under s-dependent competing risks. The s-dependent competing risks between components are captured through copula functions whose unknown parameters are estimated by a two-stage maximum likelihood estimation (MLE) method. Furthermore, the MOS operate several missions and scheduled breaks, and multiple repair channels are available. The resulting joint SM and RAP (JSM-RAP) optimization model is formulated as a mixed-integer non-linear program (MINLP) where the objective is to minimize the total maintenance and labor costs for a SM plan that ensures a required minimum reliability threshold for each mission. A genetic algorithm (GA) is used as a solution method. Several numerical experiments are conducted on a ball screw system. The results obtained demonstrate the validity and the add value of the proposed approach, in addition to the robustness and efficiency of the solution method.