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In environmental risk assessment either for registration purposes or for retrospective assessments of monitoring data, the hazard assessment is predominantly based on effect data from ecotoxicity studies. Most regulatory frameworks require studies used for risk assessment to be evaluated for reliability and relevance. Historically, the Klimisch methodology was used in many regulatory procedures where reliability needed to be evaluated. More recently, the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) have been developed for aquatic ecotoxicity studies, providing more detailed guidance on the evaluation and reporting of not only the reliability but also the relevance of a scientific study. Here, we discuss the application of the CRED methodology for assessing sediment and soil ecotoxicity studies, addressing important sediment‐ and soil‐specific criteria that should be included as part of the CRED evaluation system. We also provide detailed recommendations for the design and reporting of sediment and soil toxicity studies that can be used by scientists and researchers wishing to contribute ecotoxicological data for effect assessments carried out within regulatory frameworks. Integr Environ Assess Manag 2024;20:2162–2174. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: The Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) have been adapted to help risk assessors in assessing the relevance and reliability of soil and sediment ecotoxicity studies.Detailed recommendations for the design and reporting of sediment and soil ecotoxicity studies are provided that can be used by scientists and researchers wishing to contribute ecotoxicological data for effect assessments carried out within regulatory frameworks. [ABSTRACT FROM AUTHOR]

ABSTRACT Behavioural analysis has been attracting significant attention as a broad indicator of sub‐lethal toxicity and has secured a place as an important subdiscipline in ecotoxicology. Among the most notable characteristics of behavioural research, compared to other established approaches in sub‐lethal ecotoxicology (e.g. reproductive and developmental bioassays), are the wide range of study designs being used and the diversity of endpoints considered. At the same time, environmental hazard and risk assessment, which underpins regulatory decisions to protect the environment from potentially harmful chemicals, often recommends that ecotoxicological data be produced following accepted and validated test guidelines. These guidelines typically do not address behavioural changes, meaning that these, often sensitive, effects are not represented in hazard and risk assessments. Here, we propose a new tool, the EthoCRED evaluation method, for assessing the relevance and reliability of behavioural ecotoxicity data, which considers the unique requirements and challenges encountered in this field. This method and accompanying reporting recommendations are designed to serve as an extension of the “Criteria for Reporting and Evaluating Ecotoxicity Data (CRED)” project. As such, EthoCRED can both accommodate the wide array of experimental design approaches seen in behavioural ecotoxicology, and could be readily implemented into regulatory frameworks as deemed appropriate by policy makers of different jurisdictions to allow better integration of knowledge gained from behavioural testing into environmental protection. Furthermore, through our reporting recommendations, we aim to improve the reporting of behavioural studies in the peer‐reviewed literature, and thereby increase their usefulness to inform chemical regulation. [ABSTRACT FROM AUTHOR]

Hierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment. [ABSTRACT FROM AUTHOR]

Capacitors are components predominantly used to stabilize voltage, store energy, and lower electrical ripples. To improve the flexibility and capacitance of capacitors, we were motivated to develop hybrid film capacitors using two emerging thin film techniques, atomic and molecular layer deposition (ALD and MLD). Validation of its operational reliability in a power module or an electrical system is critical to facilitate its adoption. Equivalent series resistance (ESR) and capacitance are key performance characteristics (PC) of a capacitor, whose degradation path and process are nonlinear and dependent. Few studies have implemented the Wiener process (WP), to capture the dependency between the PC, based on the assumption of a normally distributed performance loss, notwithstanding that the performance loss may not be normally distributed. To address this concern as well as improve the accuracy of reliability prediction, a reliability framework is proposed. The gamma distribution is found to better fit the incremental PC degradation used in this work. The derived reliability model captured the non-linearity in the PC degradation path as well as its dependency using a selected Copula function. Akaike information criterion (AIC) was used to determine the most suitable Copula. Reliability estimation based on dependency consideration shows the least mean square error (MSE) compared to reliability estimate using a model that considers only one PC or assumes independent PCs. In addition, the hybrid capacitor was compared with an aluminum oxide dielectric layer capacitor, and the result was discussed. [ABSTRACT FROM AUTHOR]

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research. [ABSTRACT FROM AUTHOR]

This study introduces research trends in the electronics materials, such as joining materials and methods, reliability evaluations and conductive materials by reviewing the special issue published in Materials Transactions (Vol. 63, No. 6) entitled Frontier Researches Related to Interconnection, Packaging and Microjoining Materials and Microprocessing -Part III-. In the recent development of bonding materials and reliability evaluation methods for electronic components, in order to improve the bonding reliability bonding materials and bonding methods for not only dissimilar metals but also metals and resins by focusing on elements and processes that have received little attention are proposed. On the other hand, in the field of conductive materials, research that is not limited by conventional methods is being proposed in order to improve the efficiency of heat/electrical conduction and thermoelectric conversion. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate the effects of spatial variability in coastal silty soil properties on the stability and failure probability of deep excavation. A super-large irregularly shaped deep excavation in the coastal area in Wenzhou, China, is considered. A numerical model is established based on the random field theory, finite difference method and Monte-Carlo simulation method. Focusing on the friction angle and cohesion, the effects of horizontal and vertical correlation distances and variation coefficients on excavation stability are systematically studied. Meanwhile, the reliability evaluation approach is also applied to systematically examine the impact of the correlation distance on the surface settlement and horizontal displacement of pile. The results show that the greater correlation distance leads to a larger dispersion degree of the ground settlement curve and the displacement curve of the diaphragm wall around. The influence of the horizontal correlation distance on land settlement is more significant. The horizontal and vertical displacements of the pile top are less affected by the correlation distance. Compared with the deterministic analysis, the random analysis results considering the soil spatial variability produce larger displacement.

Reliability prediction for complex systems utilizing prognostic methods has attracted increasing attention. Furthermore, achieving accurate reliability predictions for complex systems necessitates considering the interaction between components and the multivariate functional relationship that exists among them. This paper proposes a bi‐level method to evaluate the variability of degradation processes and predictive reliability based on the profile monitoring approach for multicomponent systems. Firstly, a multivariate profile structure is introduced to model the framework of degradation analysis in scenarios where there exists stochastic dependency and a multivariate functional relationship between the degradation processes of components. At the component level, the objective is to evaluate the variability of the degradation process for each component considering the presence of stochastic dependence. For the system level analysis, the proposed approach enables the prediction of degradation variability and system reliability by considering the functional relationships among components, without the need for direct calculation of individual component reliabilities. The performance of the proposed model is evaluated through a numerical study and sensitivity analysis conducted on a multicomponent system with a k‐out‐of‐n structure. The results demonstrate the model's notable flexibility and efficiency. [ABSTRACT FROM AUTHOR]

When applying the fault enumeration method to evaluate the reliability of the whole transmission and distribution network composed of transmission network and flexible interconnected distribution network, apart from analyzing the influence of internal faults of the transmission network and distribution network on their respective power supply reliability, the interaction between the transmission network and distribution network should also be considered under the fault scenario. Based on existing independent reliability evaluation algorithms for the transmission network and distribution network, the reliability evaluation method for the whole transmission and distribution network is proposed in this paper. The proposed method in this paper can account for the interrelation between the operation states of the transmission network and the distribution network under the fault condition. The node load reduction after the failure of the transmission network is refined into the optimized load cutting scheme of the distribution network. While improving the accuracy of the reliability evaluation results of the transmission network, the load loss of the distribution network caused by the failure of the transmission network is included in the reliability evaluation of the distribution network. In addition, the load loss caused by internal faults of the distribution network and its influence on the reliability of the transmission network are included in the reliability evaluation of the transmission network. For the whole transmission and distribution network, the reliability index of the transmission and distribution network is calculated by the analytical method based on fault enumeration. The validity of the reliability evaluation model is verified by the results of the numerical analysis.

Objective Based on the virtual simulation fatigue test technology, fatigue life analysis and reliability evaluation of the bogie arm positioning node of a certain type of vehicle are carried out to improve the vehicle stability and guarantee the train operation safety. Method ABAQUS software is used to establish a finite element model of the arm positioning node and obtain the stress and strain data. FE-SAFE software is used to set the fatigue parameters of rubber material, simulate and analyze its fatigue life. Graph test method is used to test the distribution hypothesis of pseudo-failure fatigue life and to select the optimal life distribution. The relation between the life distribution parameters and the stress level is used to fit a proper functional relation and evaluate the reliability of the arm positioning node fatigue life under normal stress level. Result & Conclusion Under normal stress level, only the rubber bushing in arm positioning node will have fatigue damage. The bottom end of the rubber bushing is the area with the lowest pseudo-failure fatigue life value, and is also the area where the damage occurs first. On-site test and fatigue simulation results verify the accuracy of the finite element model of arm positioning node. The optimal normal life distribution is determined. Under normal stress level, the number of fatigue life cycles is about 210,000 when the reliability of the arm positioning node is 0.9.

Tool holders are one of the most important structures in transferring machine tools and energy for manufacturing in CNC lathe. Power servo tool holders influence kinematic accuracy and machining accuracy and so are vital to the transposition system. Reliability evaluation is also critical to guaranteeing and maintaining the accuracy of the transposition system. The first four statistical moments are derived to depict the transmission error and system characteristics. Considering the Edgeworth expansion with higher terms, reliability and reliability-based sensitivity evaluations using moments are proposed to assess system accuracy. Compared with different methods, the proposed method can represent higher statistical characteristics, helping to avoid underestimations of system reliability. Also, results calculated with the proposed method for the transposition system are in agreement with the results from the Monte Carlo simulation with 107 samples. The relative error of failure probability is 4.32%. Considering the plus–minus sign and values of results, reliability-based sensitivity represents the effects of the parameters' dispersions on system reliability. The reliability-based sensitivity indices can be utilized to optimize the system structure and to improve system accuracy, which can increase the system reliability from 98.34% to 99.99% in the transposition system of the power servo tool holder. [ABSTRACT FROM AUTHOR]

The computer numerical control swivel head drive system has a number of states from ideal operation to complete failure, and dividing these states into success and failure is not rational and may even lead to fatal errors. Given this problem, this study proposed a method of building a multistate reliability evaluation model based on a Bayesian network (BN). First, BN was built in accordance with the mechanism of the drive system and in consideration of the interaction between different failure modes. Second, stable working data were used to determine the failure probabilities in the different life cycles of nodes. Last, a probability distribution table was employed to describe the multistate characteristics of nodes, and a BN model of the multistate system was built. The ranking of the importance of risks that may lead to drive system failure was determined using the posterior probability calculation method of BN. [ABSTRACT FROM AUTHOR]

In this study, we focus on the reliability evaluation of the linear consecutive- k -out-of- n :G system, where the system is working if and only if at least k consecutive components work. The multiple state of the system is considered including not only the working and failure state, but also the intermediate state approaching failure, and the reliability evaluation is discussed including the system failure probability, the expected number of failed components and the expected time to the considered intermediate state. We also apply these reliability results to the maintenance problems, and discussed the condition-based maintenance for the system which is based on the state of the system, and give the results of the optimal maintenance interval which minimizes the expected cost rate. [ABSTRACT FROM AUTHOR]

With the improvement of equipment reliability, human factors have become the most uncertain part in the system. The standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) method is a reliable method in the field of human reliability analysis (HRA) to evaluate human reliability and assess risk in large complex systems. However, the classical SPAR-H method does not consider the dependencies among performance shaping factors (PSFs), which may cause overestimation or underestimation of the risk of the actual situation. To address this issue, this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient. First, the dependence between every two PSFs is measured by the Pearson correlation coefficient. Second, the weights of the PSFs are obtained by considering the total dependence degree. Finally, PSFs' multipliers are modified based on the weights of corresponding PSFs, and then used in the calculating of human error probability (HEP). A case study is used to illustrate the procedure and effectiveness of the proposed method. Graphic Abstract [ABSTRACT FROM AUTHOR]

By integrating the power and natural gas systems (IPGS), these two systems can synergistically provide electric and gas power through mutual energy sharing. However, disruptions occurring in one system can have detrimental effects on the optimal performance of the other system. Hence, the assessment of IPGS reliability becomes imperative. In this article, a reliability evaluation model is proposed that utilizes machine learning algorithms to tackle uncertainties associated with the failure rate of IPGS components in the presence of gas storage resources. To calculate system reliability indices, we employ Sequential Monte Carlo Simulation (SMCS) and an optimal load-shedding program. Moreover, a random forest (RF) algorithm is adopted to classify elements based on their importance in upholding the overall system's reliability. The proposed model is implemented using MATLAB, GAMS, and Python software, with the IEEE 14-bus system and the 10-node natural gas system as case studies. The simulation results illustrate that by considering the gas storage resources, the reliability of the power system and natural gas can be improved by 2.12 and 8.3%, respectively, and the overall reliability of IPGS is improved by 5.25%. Also, the prioritization of these resources is determined in IPGS. [ABSTRACT FROM AUTHOR]

Cirrhosis is a liver disease resulting from abnormal continuation of fibrosis, and ultrasound imaging is widely used for cirrhosis diagnosis because of its non‐invasiveness. However, due to unclear appearances of cirrhosis on ultrasound images, diagnoses are difficult and individual results possibly differ depending on the physician's experience. Recently, computer‐aided diagnostic systems using image processing and machine learning have been developed to help physicians detect cirrhosis as a 'Second opinion'. Some related studies have focused on a scenario where physicians set ROIs (Region of Interests) manually because selecting reliable ROIs for training a classifier and classification of patients is indispensable. But, the accuracy of such systems depends inherently on the quality of ROIs, and thus the workloads of physicians increase. In this paper, we propose a reliability evaluation method (REM) for each ROI based on its posterior probability and relationship to peripheral ROIs. The assumption of our proposal is that reliable regions of cirrhosis and normal can be observed in certain regions predominantly. We evaluated the effectiveness of the REM and its optimization for practical use. Experimental results showed that our proposed method curated reliable ROIs and improved classification performance in terms of AUC (Area Under the Curve). © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vehicular Ad-Hoc Networks have increased the opportunity to use Cognitive Radio (CR) to cope with spectrum scarcity and increase reliability. Despite all efforts to improve the reliability of CR-VANETs, there are no mathematical and scalable methods for modelling and evaluating CR-VANETs. This paper investigates the problem of assessing the reliability of a multi-hop and multi-channel CR-VANET in safety applications. Two formal and scalable methods, Boolean notation and the Petri Net model, are provided to model and evaluate the reliability of CR-VANET. We give a detailed simulation study with NS3 to support our theory. Our analytical and simulation results indicate the effectiveness of CR in increasing the reliability of VANET. By these results, one can evaluate the network's reliability and tune the network parameters for better reliability. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the calculation efficiency and reduce the uncertainty in the reliability evaluation process of flat car underframe, a new method based on an improved genetic simulated annealing algorithm-back propagation neural network (GSA-BP) and probability box is proposed. Firstly, a certain type of flat car underframe is taken as the research object, genetic evolution algorithm (GA) and simulated annealing algorithm (SA) are utilized to optimize the weights and thresholds of the GSA-BP neural network and obtain the best initial parameter values. Applying the values, the BP neural network is trained, and the improved GSA-BP neural network is established which is verified by test functions. Secondly, the central plate sub-model of the flat car underframe is obtained by sub-model technology and the correctness of its boundary conditions is tested. On this basis, the static strength analysis of the sub-model is carried out. Finally, the improved GSA-BP neural network and probability box are utilized to evaluate the reliability of the sub-model of flat car underframe, and the Monte Carlo method is utilized to verify it. The results show that the proposed method not only improves the accuracy of reliability evaluation but also shortens the calculation time from 10 hours to 6 hours. The calculation efficiency is increased by 40%, which further verifies its superiority and feasibility in structural reliability evaluation. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to model and evaluate the reliability of long-life high-voltage relays with small-sample fault data characteristics, a reliability analysis method integrating average rank, the minimum mean square distance empirical distribution function, and total least squares estimation is proposed. In the random truncation experiment, considering the influence of random truncation data, the average rank method is used to correct the rank of small-sample fault data; then, the optimal empirical distribution function for small-sample fault data is obtained through the minimum average square distance, which can overcome the impact of small-sample fault data randomness. Under the assumption of the Weibull distribution model, the total least squares estimation method is used for reliability model parameter estimations, and the linear correlation coefficient and d-test method are used for model hypothesis testing. If two or more distribution models pass the linear correlation coefficient test and the d-test simultaneously, the root mean square error and relative root mean square error are applied to determine the optimal reliability model. The effectiveness of this method is verified by comparing it with the maximum likelihood estimation method. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landslides represent a prevalent geological hazard in reservoir areas, posing significant risks to the lives and properties of residents. It is widely acknowledged that rainfall and the fluctuation of reservoir water level are two primary factors triggering reservoir landslides. However, there has been limited research on multi-sliding zone reservoir bank landslides, and existing studies fail to adequately characterize the evolutionary process of this type of landslide. In this paper, the Outang landslide located in Three Gorges Reservoir (TGR) was taken as a case study to evaluate its stability and failure probability. Sensitivity analyses were conducted on statistical mechanical parameters and autocorrelation functions initially, followed by a comparative analysis of annual stability and reliability. The results indicate that the stability of the Outang landslide is predominantly influenced by the water level fluctuation, and the lower sliding zone plays a pivotal role in affecting the overall stability compared with other regions in the landslide. Furthermore, variations in failure probability are notably influenced by the frictional angle and the vertical scale of fluctuation, with autocorrelation functions playing a more significant role when the coefficient of variation is small and the vertical scale of fluctuation is large. This study could provide valuable guidance for the prevention and mitigation of analogous multi-sliding zone reservoir bank landslides. [ABSTRACT FROM AUTHOR]

For complex equipment, it is easy to over-evaluate the impact of failure on production by estimating the reliability level only through failure probability. To remedy this problem, this paper proposes a statistical evaluation method based on fuzzy failure data considering the multi-state characteristics of equipment failures. In this method, the new reliability-evaluation scheme is firstly presented based on the traditional statistical analysis method using the Weibull distribution function. For this scheme, the failure-grade index is defined, and a fuzzy-evaluation method is also proposed by comprehensively considering failure severity, failure maintenance, time, and cost; this is then combined with the time between failures to characterize the failure state. Based on the fuzzy failure data, an improved adaptive-failure small-sample-expansion method is proposed based on the classical bootstrap method and the deviation judgment between distributions of the original and newborn samples. Finally, a novel reliability-evaluation model, related to the failure grade and its membership degree, is established to quantify the reliability level of equipment more realistically. Example cases for three methods of the scheme (the failure-grade fuzzy-evaluation method, the sample-expansion method, and the reliability-evaluation modeling method) are presented, respectively, to validate the effectiveness and significance of the proposed reliability-evaluation technology. [ABSTRACT FROM AUTHOR]

This paper presents an innovative EGSPN (Editable Generalized Stochastic Petri Net) model, optimizing the size of GSPN (Generalized Stochastic Petri Net) while preserving its core benefits. Specifically, the EGSPN introduces a transition termed "editable transition," which allows for the establishment of any dynamic logic, thereby circumventing the intricacies associated with the EGSPN model structure. A Monte Carlo (MC) simulation technique is put forth for EGSPN analysis, enhanced by the integration of Sobol sequences with a Bayesian optimizer. This MC simulation approach, unbounded by any specific distribution constraints, exhibits accelerated convergence rates. To address the issue of preset sampling size, an adaptive sampling strategy rooted in confidence intervals has been proposed. By real‐time computation of margin of errors under varying confidence levels, simulations can be halted prior to reaching the preset sampling size. This method strikes a balance between reduced simulation time and maintained accuracy. The applicability and efficacy of the proposed method are further elucidated through a numerical example of a flight control system. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vibrational environments can cause drift or changes in Micro-Electro-Mechanical System (MEMS) gyroscope rotor parameters, potentially impacting their performance. To improve the effective use of MEMS gyroscopes, this study introduced a method for evaluating the reliability of parameter degradation under vibration. We analyzed the working principle of MEMS gyroscope rotors and investigated how vibration affects their parameters. Focusing on zero bias and scale factor as key performance indicators, we developed an accelerated degradation model using the distributional assumption method. We then collected degradation data for these parameters under various vibration conditions. Using the Copula function, we established a reliability assessment approach to evaluate the degradation of the MEMS gyroscope rotor's zero bias and scale factor under vibration, enabling the determination of reliability for these parameters. Experimental findings confirmed that increasing stress levels lead to reduced failure times and increased failure rates for MEMS gyroscope rotors, with significant changes observed in the zero bias parameter. Our evaluation method effectively characterizes changes in the reliability of the MEMS gyroscope rotor's scale factor and zero bias over time, providing valuable information for practical applications of MEMS gyroscopes. [ABSTRACT FROM AUTHOR]

Ensuring the reliability of GPUs and their internal components is paramount, especially in safety-critical domains like autonomous machines and self-driving cars. These cutting-edge applications heavily rely on GPUs to implement complex algorithms due to their implicit programming flexibility and parallelism, which is crucial for efficient operation. However, as integration technologies advance, there is a growing concern regarding the potential increase in fault sensitivity of the internal components of current GPU generations. In particular, Special Function Unit (SFU) cores inside GPUs are used in multimedia, High-Performance Computing, and neural network training. Despite their frequent usage and critical role in several domains, reliability evaluations on SFUs and the development of effective mitigation solutions have yet to be studied and remain unexplored. This work evaluates the impact of transient faults in the main hardware structures of SFUs in GPUs. In addition, we analyze the main overhead costs and benefits of developing selective-hardening mechanisms for SFUs. We focus on evaluating and analyzing two SFU architectures for GPUs ('fused' and 'modular') and their relations to energy, area, and reliability impact on parallel applications. The experiments resort to fine-grain fault injection campaigns on an RTL GPU model (FlexGripPlus) instrumented with both SFUs. The results on both SFU architectures indicate that fused SFUs (in commercial-grade devices) require lower area overhead (about 27%) for their integration in GPUs but are more vulnerable to transient faults (in up to 47% for the analyzed cases) and less power efficient (in up to 36.6%) than modular SFUs. Moreover, the reliability estimation shows that Modular SFUs are structurally more resilient than Fused ones in up to one order of magnitude. Similarly, selective-hardening mechanism based on Triple-Modular Redundancy (TMR) shows that coarse-grain strategies might increase the reliability of the overall SFUs under feasible overhead costs. [ABSTRACT FROM AUTHOR]

In recent years, biomedical optics technology has developed rapidly. The current widespread use of biomedical optics was made possible by the invention of optical instruments. The advantages of being non-invasive, portable, effective, low cost, and less susceptible to system noise have led to the rapid development of functional near-infrared spectroscopy (fNIRS) technology for hemodynamics detection, especially in the field of functional brain imaging. At the same time, laboratories and companies have developed various fNIRS-based systems. The safety, stability, and efficacy of fNIRS systems are key performance indicators. However, there is still a lack of comprehensive and systematic evaluation methods for fNIRS instruments. This study uses the fNIRS system developed in our laboratory as the test object. The test method established in this study includes system validation and performance testing to comprehensively assess fNIRS systems' reliability. These methods feature low cost and high practicality. Based on this study, existing or newly developed systems can be comprehensively and easily evaluated in the laboratory or workspace. [ABSTRACT FROM AUTHOR]

Edge disjoint paths have a closed relationship with edge connectivity and are anticipated to garner increased attention in the study of the reliability and edge fault tolerance of a readily scalable interconnection network. Note that this interconnection network is always modeled as a connected graph G. The minimum of some of modified edge-cuts of a connected graph G, also known as the h-extra edge-connectivity of a graph G ( λ h (G) ), is defined as the maximum number of the edge disjoint paths connecting any two disjoint connected subgraphs with h vertices in the graph G. From the perspective of edge-cut, the smallest cardinality of a collection of edges, whose removal divides the whole network into several connected subnetworks having at least h vertices, is the h-extra edge-connectivity of the underlying topological architecture of an interconnection network G. This paper demonstrates that the h-extra edge-connectivity of the pentanary n-cube ( λ h (K 5 n) ) appears a concentration behavior for around 50 percent of h ≤ ⌊ 5 n / 2 ⌋ as n approaches infinity. Let e = 1 for n is even and e = 0 for n is odd. It mainly concentrates on the value [ 4 g (⌈ n 2 ⌉ - r) - g (g - 1) ] 5 ⌊ n 2 ⌋ + r for g 5 ⌊ n 2 ⌋ + r - ⌊ [ (g - 1) 2 + 1 ] 5 2 r + e 3 ⌋ ≤ h ≤ g 5 ⌊ n 2 ⌋ + r , where r = 1 , 2 , ⋯ , ⌈ n 2 ⌉ - 2 , g ∈ { 1 , 2 , 3 , 4 } ; r = ⌈ n 2 ⌉ - 1 , g ∈ { 1 , 2 } . Furthermore, it is shown that the above upper bound and lower bound of h are sharp. [ABSTRACT FROM AUTHOR]

Vehicle-sharing services are now integrating electric vehicles (EVs) into their sharing systems. Electric vehicle sharing (EVS) is a convenient and cost-effective approach to reducing the use of internal combustion engine vehicles and improving traffic flow. However, the randomness in the charging load transfer eventually affects the reliability of a distribution network that contains microgrids. This study analyzes the spatiotemporal transfer characteristics of the EVS charging load in a distribution network that contains microgrids in different geographical areas. Combined with the improved charging load model, a reliability evaluation model of the charging load transfer is developed based on the Gauss-Markov movement model, which describes the spatiotemporal transfer characteristics of a distribution network with high accuracy. Moreover, the sequential Monte Carlo method for evaluating the reliability of a distribution network that contains microgrids is improved so that considers charging load transfer. Different scenarios, i.e., randomness of charging load transfer, number of EVS, charging locations of the main feeder lines in the microgrid, and the configuration of the wind-photovoltaic-storage power generation capacity are considered in evaluating the impact of charging load transfer on a distribution network that contains microgrids. The results could serve as a basis for integrating EVS into vehicle-sharing networks considering efficient charging load transfer in distribution network that contains microgrids.

Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim at investigating the propagation of logical-shift errors (i.e., the unexpected modification of the qubit state) in QNNs. We propose a bottom–up evaluation reporting data from 13 322 547 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logical-shift error propagation through the qubits, channels, and subgrids, identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.

Temporary faults in photovoltaic (PV) systems can lead to system failure and compromise electronic subassemblies’ availability and reliability (A&R). Accurately assessing A&R in the presence of temporary faults is crucial for designing, operating, and maintaining large-scale grid-connected solar photovoltaic systems (LS-GCPS). In this study, we propose a modified reliability block diagram (RBD) technique for evaluating the A&R of seven practical LS-GCPS in the presence of temporary faults. Our approach extends beyond existing methods by examining the impact of subsystem availability on overall system performance, regardless of fault type (temporary or permanent). Furthermore, we investigate the degree and sequence of importance for various subsystems to highlight areas that require significant attention for boosting A&R and preventing potential failures. Our findings reveal the unique challenges posed by temporary faults in LS-GCPS and provide insights for improving system reliability and availability. Our proposed approach and insights can benefit researchers, engineers, and stakeholders involved in designing and managing LS-GCPS.

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research.

The reliability level of general aviation fleet system directly affects the economic benefits and safe operation of general aviation fleet. In order to effectively evaluate the reliability level of general aviation fleet, using the entropy weight variable fuzzy recognition and 1D-CNN depth learning reliability evaluation method. Firstly, taking the Cessna 172 general aviation fleet as the research object, refers to the maintenance statistical analysis of general aviation fleet reliability data, and classifies the fleet reliability evaluation indexes according to the ATA100 chapter standard. Combined with index importance analysis and Delphi expert investigation, 14 key items are extracted as reliability evaluation indexes of general aviation fleet. Secondly, using entropy weight method to obtain indexes weight objectively, and the evaluation level membership function is constructed based on variable fuzzy recognition method. Finally, a reliability evaluation model based on 1D-CNN deep learning method was established. Through training and testing the reliability data evaluation model of general aviation fleet, and comparing with the results of evaluation methods such as support vector machines. The results show that the recognition rate of the 1D-CNN deep learning method based on entropy weight variable fuzzy recognition can reach 91.95%, verifying the objective effectiveness of the evaluation method. [ABSTRACT FROM AUTHOR]

In this research paper, a comprehensive performance analysis was carried out for a 48-watt transformerless DC-DC boost converter using a Proportional–Integral–Derivative (PID) controller through dynamic modeling. In a boost converter, the optimal design of the magnetic element plays an important role in efficient energy transfer. This research paper emphasizes the design of an inductor using the Area Product Technique (APT) to analyze factors such as area product, window area, number of turns, and wire size. Observations were made by examining its response to changes in load current, supply voltage, and load resistance at frequency levels of 100 and 500 kHz. Moreover, this paper extended its investigation by analyzing the failure rates and reliability of active and passive components in a 48-watt boost converter, providing valuable insights about failure behavior and reliability. Frequency domain analysis was conducted to assess the controller's stability and robustness. The results conclusively underscore the benefits of incorporating the designed PID controller in terms of achieving the desired regulation and rapid response to disturbances at 100 and 500 kHz. The findings emphasize the outstanding reliability of the inductor, evident from the significantly low failure rates in comparison to other circuit components. Conversely, the research also reveals the inherent vulnerability of the switching device (MOSFET), characterized by a higher failure rate and lower reliability. The MATLAB® Simulink platform was utilized to investigate the results. [ABSTRACT FROM AUTHOR]

With the improvement of equipment reliability, human factors have become the most uncertain part in the system. The standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) method is a reliable method in the field of human reliability analysis (HRA) to evaluate human reliability and assess risk in large complex systems. However, the classical SPAR-H method does not consider the dependencies among performance shaping factors (PSFs), which may cause overestimation or underestimation of the risk of the actual situation. To address this issue, this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient. First, the dependence between every two PSFs is measured by the Pearson correlation coefficient. Second, the weights of the PSFs are obtained by considering the total dependence degree. Finally, PSFs’ multipliers are modified based on the weights of corresponding PSFs, and then used in the calculating of human error probability (HEP). A case study is used to illustrate the procedure and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the problem of researching the reliability of dynamic-pressure mechanical seals, this paper proposes a reliability evaluation method for dynamic-pressure mechanical seals based on the Monte Carlo method. Based on the influence of the mass transfer coefficient on vaporization phase transition, a liquid film vaporization model of a hydrodynamic mechanical seal's end face is established, and the working condition parameters and groove structure parameters are designed using the experimental design method. The vaporization characteristics of the liquid film under various parameters are analyzed, and the functional functions of the vaporization characteristics are obtained by fitting. Combined with the maximum vapor phase volume fraction when the dynamic-pressure mechanical seal changes from the liquid miscible phase to the vapor miscible phase, the limit state equation of the vapor phase volume fraction is obtained. Finally, based on the Monte Carlo simulation method, the sealing reliability under specific groove structure parameters is calculated. Our research shows that this method has practicability and effectiveness for the reliability evaluation of mechanical seals with different working conditions and different groove structures. [ABSTRACT FROM AUTHOR]

A Monte Carlo simulation method based on multisource bayes is proposed to improve the reliability of motorized spindles in cycloid gear grinding machines and reduce their failure rate. Based on field investigations and motorized spindle maintenance records, a fault tree model of a motorized spindle was established, and the fuzzy importance of each bottom event was evaluated. The fault tree of a motorized spindle was used as a Monte Carlo reliability simulation model, and its importance was used as the input parameter for the simulation. The reliability evaluation index of the motorized spindle was obtained at different simulation times. The feasibility and accuracy of the reliability simulation were verified by comparing the importance and simulation importance. A vibration test was designed for bearing faults with high importance, and fault extraction was performed by combining the wavelet packet transform and empirical mode decomposition. This method can also be used to simulate and analyze the reliability of other equipment or machine tools. [ABSTRACT FROM AUTHOR]