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This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The purpose of this study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).

Industrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.

From the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).

The integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

This paper aims to present a novel dynamic reliability model that considers the interval mixed uncertainty for the air-breathing hypersonic flight vehicle (AHFV) to guarantee flight safety and structural reliability.

Initially, the force condition of the fuselage is analyzed based on the longitudinal elastic model of an AHFV. Subsequently, a new high-efficiency dynamic reliability model is presented to describe the failure probability evolution of the fuselage structure. For the random uncertainty problem with interval distribution parameters, the interval PHI2 method of time-dependent reliability is used to obtain the time-dependent reliability interval of the AHFV. Finally, the key variables that affect the failure probability accumulation are determined, which provide an important reference for ensuring structural reliability and improving the life span of AHFVs.

It is demonstrated that the proposed reliability model can obtain more accurate dynamic reliability results for the fuselage, and it is confirmed the key variables that affect the failure probability accumulation. The results also provide an important reference for the reliability analysis of hypersonic vehicles.

The novelty of this work comes from the first application of the PHI2 method (considering the interval mixed uncertainty) in the AHFV and the development of a new reliability model for the entire body of AHFVs. The proposed analysis scheme is implemented on the dynamic model of the AHFV, which provides a more accurate reference for improving the structural reliability and life span of AHFVs.

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

This study conducts a comparative study on the performance of reliability assessment methods based on adaptive surrogate models to accurately assess the reliability of automobile components, which is critical to the safe operation of vehicles.

In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components.

By comparing the reliability evaluation problems of four automobile components, the Kriging model and Polynomial Chaos-Kriging (PCK) have better robustness. Considering the trade-off between accuracy and efficiency, PCK is optimal. The Constrained Min-Max (CMM) learning function only depends on sample information, so it is suitable for most surrogate models. In the four calculation examples, the performance of the combination of CMM and PCK is relatively good. Thus, it is recommended for reliability evaluation problems of automobile components.

Although a lot of research has been conducted on adaptive surrogate-model-based reliability evaluation method, there are still relatively few studies on the comprehensive application of this method to the reliability evaluation of automobile component. In this study, different adaptive learning strategies and surrogate models are combined to study their performance in reliability assessment of automobile components. Specially, a superior surrogate-model-based reliability evaluation method combination is illustrated in this study, which is instructive for adaptive surrogate-model-based reliability analysis in the reliability evaluation problem of automobile components.

This paper aims to investigate the state of the art for the reliability evaluation of reinforced concrete beams in a fire situation. Special emphasis is placed on addressing which parameters were considered probabilistically or deterministically, the prescribed probabilistic models for the assumed stochastic variables, the treatment of the heat transfer mechanism, the quantification of the structural fire performance and the assumed target reliability levels.

Research papers were identified through a search on the Web of Science, Google Scholar and detailed searches within the journals Journal of Structural Fire Engineering, Fire Technology and Fire Safety Journal, supplemented with references known by the authors.

Considering the state-of-the-art review, gaps in the literature are identified related to (1) the probabilistic evaluation of shear capacity for standard fires and parametric fires, and bending capacity for parametric fires, (2) the absence of reference fragility curves for immediate design application/code calibration and (3) the specification of target safety levels for reliability-based design.

The lack of research papers gathering studies on the reliability of reinforced concrete beams in fire situation makes it difficult to further develop research in the area. The value of this work lies precisely in the collection of the basic information, making it possible to identify gaps to be addressed in future research and the suggestion of a research framework.

Because of growing demand for slim, thin and cheap handheld devices, reduced-volume solder interconnects like land grid array (LGA) are becoming attractive and popular choices over the traditional ball grid array (BGA) packages. This study aims to investigate the mechanical shock and impact reliability of various solder alloys and BGA/LGA interconnect configurations.

Therefore, this paper uses drop testing experiments and numerical finite element simulations to evaluate and compare the reliability performance of both LGA and BGA components when exposed to drop and impact loadings. Additionally, three common solder alloys, including 63Sn37Pb, SAC305 and Innolot, are discussed.

The results of this study showed that electronic packages’ drop and impact reliability is strongly driven by the solder configuration and the alloy type. Particularly, the combination of stiff solder alloy and shorter joint, LGA’s assembled with SAC305, results in highly improved drop reliability. Moreover, the BGA packages’ performance can be considerably enhanced by using ductile and compliant solder alloys, that is, 63Sn37Pb. Finally, this paper discussed the failure mode of the various solder configurations and used simulation results to explain the crack and failure situations.

In literature, there is a lack of published work on the drop and impact reliability evaluation and comparison of LGA and BGA solders. This paper provides quantitative analysis on the reliability of lead-based and lead-free solders when assembled with LGA and BGA interconnects.

This study aims to gain insights into how students perceive online examination practices and evaluation, as well as identify the key factors that impact their intentions toward online exams.

This empirical study conducted in India utilized an online survey method between May 24 and June 14, 2022. The data were collected from 712 undergraduate and postgraduate students through a Google Docs questionnaire. Descriptive analysis, exploratory factor analysis (EFA), regression and reliability analysis were conducted to analyze the data.

There are many studies related to the adoption of online learning. However, there are limited studies that explore the adoption and acceptance of online examinations and evaluations. This paper intends to fill this gap and tries to highlight important factors influencing online examination usage by the students. Five important factors emerged in the study include “pedagogy, validity, reliability,” “practicality,” “security,” “facilitating condition” and “intention,” The study concludes that taking tests online is preferred for several reasons, including the perceived increased dependability of grades and the decreased cost, time and effort associated with taking exams.

This research explores students' perceptions of learning management systems (LMS)-based online exams and provides insights for students to better prepare and adapt. It assists policymakers in developing fair assessment policies and improving LMS platforms. The study contributes to the theoretical understanding of e-learning and calls for further research to enhance the effectiveness of online exams. It identifies gaps in existing literature and recommends larger sample sizes, broader representation, longitudinal data collection and conversational methods for future research.

This study shows increasing research on e-learning and online examinations, mostly using quantitative methods. The important variables for understanding students' perceptions of online exams are “pedagogy, validity, reliability,” “practicality,” “security,” “facilitating condition” and “intention.” Concerns raised include validity, fairness and security. Electronic exams are more suitable for formative evaluation, and validity, reliability, security and adaptability are key pillars for successful online testing.

To meet energy demand and tackle the challenges posed by global warming, Bagasse-based Cogeneration Power Generation (BCPG) plant in sugar mills have tremendous potential due to large-scale supply of renewable fuel called bagasse. To meet this goal, an integrated framework has been proposed for analyzing performance issues of BCPG.

Intuitionistic Fuzzy Lambda-Tau (IFLT) approach was implemented to compute various reliability parameters. Intuitionistic Fuzzy Failure Mode and Effect Analysis (IF-FMEA) approach has been implemented for studying risk issues results in decrease in plant's availability. Moreover, IF- Technique for Order Performance by Similarity to Ideal Solution (IF-TOPSIS) is implemented to verify accuracy of IF-FMEA approach.

For membership and non-membership functions, availability decreases to 0.0006% and 0.0020% respectively for spread ±15% to ±30%, and further decreases to 0.0127% and 0.0221% for spread ±30% to ±45%. Under risk assessment failure causes namely Storage tank (ST3), Valve (VL6), Transfer pump (TF8), Deaerator tank (DT11), High pressure heater and economiser (HP15), Boiler drum and super heater (BS22), Forced draft and Secondary air fan (FS25), Air preheater (AH29) and Furnace (FR31) with Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance (IFHWED) based output scores – 0.8988, 0.9752, 0.9400, 0.8988, 0.9267, 1.1131, 1.0039, 0.8185, 1.0604 were identified as the most critical failure causes.

Reliability and risk analysis results derived from IFLT and IF-FMEA approaches respectively, to address the performance issues of BCPG is based on the quantitative and qualitative data collected from the industrial experts and maintenance log book. Moreover, to take care of hesitation in expert's knowledge, IF theory-based concept is incorporated so as to achieve more accuracy in analysis results. Reliability and risk analysis results together will be helpful in analyzing the performance characteristics and diagnosis of critical failure causes, which will minimize frequent failure in BCPG.

The framework will help plant managers to frame optimal maintenance policy in order to enhance the operational aspects of the considered unit. Moreover, the accurate and early detection of failure causes will also help managers to take prudent decision for smooth operation of plant.

The results obtained ensure continuous operation of plant by utilizing the bagasse as fuel in boiler and also mitigate the wastages of fuel. If this bagasse (green fuel) is not properly utilized, there remains a dependency on coal-based power plants to meet the power demand. The results obtained are useful for decreasing dependency on coal, and promoting bagasse as the green, and alternative fuel, the emission by burning of these fuels are not harmful for environment and thereby contribute in preventing the environment from harmful effect of GHGs gases.

IFLT approach has been implemented to develop reliability modeling equations of the BCPG unit, and furthermore to compute various reliability parameters for both membership and non-membership function. The ranking results of IF-FMEA are compared to IF-TOPSIS approach. Sensitivity analysis is done to check stability of proposed framework.