Level Of Reliability Research Articles

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.

The psychometric properties of the Utrecht Work Engagement Scale on a Zimbabwean population

Orientation: Work engagement has been found to be related to positive job attitudes such as job involvement, job satisfaction, organisational commitment, and low turnover intention.Research purpose: The primary goal of this study was to test the psychometric properties of the Utrecht Work Engagement Scale (UWES) on a Zimbabwean sample.Motivation for the study: The UWES is a widely used measure of work engagement. Therefore, there is a need to assess the reliability and construct validity of the UWES in a Zimbabwean sample owing to the paucity of studies on its psychometric properties in this setting.Research approach/design and method: A cross-sectional survey research design was employed (N = 304). A non-probability sample of 304 participants from selected security organisations was studied. The UWES was used to measure work engagement, and its reliability was evaluated using SPSS. Construct validity was assessed through confirmatory factor analysis (CFA) using LISREL.Main findings: Moderately high levels of reliability were found for the UWES subscales. A poor model fit with the data was found for first-order measurement models through CFA.Practical/managerial implications: The UWES demonstrated a reasonable fit for the 9-item scale CFA model tested in this study. Moderately high reliability coefficients were recorded for all the subscales of the UWES.Contribution/value-add: The study promotes the use of reliable and valid instruments in Zimbabwe by confirming the psychometric properties of the UWES.

Correction: Investigation on a predetermined point of failure for stainless steel 316L pressure loaded components made by laser powder bed fusion through stress analysis and experimental testing

Correction: Investigation on a predetermined point of failure for stainless steel 316L pressure loaded components made by laser powder bed fusion through stress analysis and experimental testing

Failures of geometrically asymmetric steel-UHPC composite beams

Geometrically asymmetric steel-ultra-high performance concrete (UHPC) composite structures (SUCSs) suffer several typical failure modes during applications in structural engineering. But the competing mechanism of these failure modes remains a challenge to be urgently overcome. In the present paper, the failure modes of the geometrically asymmetric SUCSs under lateral loads are theoretically predicted and numerically studied. Considering both the geometric asymmetry of the structure and the tensile-compressive asymmetry of UHPC, a theoretical model is proposed to predict the critical failure load for each failure mode (e.g., bending and core shear), and then a failure mechanism map is constructed by comparing each critical failure load. The theoretical predictions are in line with the present numerical results and the previous experimental measurements. The results demonstrate that geometric asymmetry, together with some other geometric parameters and material properties, have remarkable influences on the failure modes of structures. This work provides an effective approach for predicting and designing the failure modes of geometrically asymmetric SUCSs in structural engineering applications, such as undersea tunnels, offshore platforms, and nuclear shielding walls.

Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence

Dairy farmers and beef cattle breeders aim for one calf per year to optimize breeding efficiency, relying on artificial insemination of both dairy and beef cows. Accurate estrus detection and timely insemination are vital for improving conception rates. However, recent challenges such as operational expansion, increased livestock numbers, and heightened milk production have complicated these processes. We developed an artificial intelligence (AI)-based pregnancy probability diagnostic tool to predict the optimal timing for artificial insemination. This tool analyzes external uterine opening image data through AI analysis, enabling high conception rates when inexperienced individuals conduct the procedure. In the initial experimental phase, images depicting the external uterine opening during artificial insemination were acquired for AI training. Static images were extracted from videos to create a pregnancy probability diagnostic model (PPDM). In the subsequent phase, an augmented set of images was introduced to enhance the precision of the PPDM. Additionally, a web application was developed for real-time assessment of optimal insemination timing, and its effectiveness in practical field settings was evaluated. The results indicated that when PPDM predicted a pregnancy probability of 70% or higher, it demonstrated a high level of reliability with accuracy, precision, and recall rates of 76.2%, 76.2%, and 100%, respectively, and an F-score of 0.86. This underscored the applicability and reliability of AI-based tools in predicting optimal insemination timing, potentially offering substantial benefits to breeding operations.

Effect of systemic vascular resistance on the agreement between stroke volume by non-invasive pulse wave analysis and Doppler ultrasound in healthy volunteers.

Stroke volume can be estimated beat-to-beat and non-invasively by pulse wave analysis (PWA). However, its reliability has been questioned during marked alterations in systemic vascular resistance (SVR). We studied the effect of SVR on the agreement between stroke volume by PWA and Doppler ultrasound during reductions in stroke volume in healthy volunteers. In a previous study we simultaneously measured stroke volume by PWA (SVPWA) and suprasternal Doppler ultrasound (SVUS). We exposed 16 healthy volunteers to lower body negative pressure (LBNP) to reduce stroke volume in combination with isometric hand grip to elevate SVR. LBNP was increased by 20 mmHg every 6 minutes from 0 to 80 mmHg, or until hemodynamic decompensation. The agreement between SVPWA and SVUS was examined using Bland-Altman analysis with mixed regression. Within-subject limits of agreement (LOA) was calculated from the residual standard deviation. SVRUS was calculated from SVUS. We allowed for a sloped bias line by introducing the mean of the methods and SVRUS as explanatory variables to examine whether the agreement was dependent on the magnitude of stroke volume and SVRUS. Bias ± limits of agreement (LOA) was 27.0 ± 30.1 mL. The within-subject LOA was ±11.1 mL. The within-subject percentage error was 14.6%. The difference between methods decreased with higher means of the methods (-0.15 mL/mL, confidence interval (CI): -0.19 to -0.11, P<0.001). The difference between methods increased with higher SVRUS (0.60 mL/mmHg × min × L-1, 95% CI: 0.48 to 0.72, P<0.001). PWA overestimated stroke volume compared to Doppler ultrasound during reductions in stroke volume and elevated SVR in healthy volunteers. The agreement between SVPWA and SVUS decreased during increases in SVR. This is relevant in settings where a high level of reliability is required.

A two-loop RBDO approach for steel frame structures using EVPS, GWO, and Monte Carlo simulation

This paper evaluates Enhanced Vibrating Particle System (EVPS) and Grey Wolf Optimizer (GWO) algorithms for Reliability-based Design Optimization (RBDO) of steel frames. The two-loop RBDO method minimizes frame weight while satisfying strength, drift, and reliability constraints, considering uncertainties in material properties and loads. Both EVPS and GWO effectively reduce structure weight and ensure required reliability levels, demonstrating their applicability in sustainable steel frame design.

A Statistical Evaluation Method Based on Fuzzy Failure Data for Multi-State Equipment Reliability

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.

The strength prediction model of unidirectional fiber reinforced composites based on the renormalization group method

When unidirectional fiber reinforced composites are subjected to longitudinal tensile loading and reach a critical failure state, they experience a sudden transition from local damage to catastrophic failure, commonly termed as an avalanche event. This paper integrates the self-organized criticality theory (SOC) concepts into the prediction of longitudinal tensile strength of composites and establishes a strength prediction model of composites based on the renormalization group method (RGM). The predictions of the RGM model are successfully validated against experimental results in the literatures, and it demonstrates relatively acceptable predictive accuracy compared to classical strength criteria. Compared to other state-of-the-art prediction models considering stochastic fiber strength distribution, the RGM model effectively provides strength statistics for fiber bundles of any size and describes the occurrence of composites avalanche failure induced by local stress concentration. This present model can be very conveniently implemented as a User Material Subroutine (UMAT) for finite simulations, facilitating practical prediction of the strength of composites structures.

Dynamic reliability analysis of angular contact ball bearing based on maximum entropy quadratic fourth moment method*

ABSTRACT Bearing failure occurs when the dynamic contact stress exceeds the yield strength of the bearing material during operation. To minimise the risk of bearing failure, a new reliability theory method is proposed based on the stress-strength model of bearings, incorporating random input variables to analyse the reliability of angular contact ball bearings (ACBBs) and quantify their dynamic performance. The maximum entropy quadratic fourth moment method is used to calculate the probability distribution of maximum orthogonal shear stress-strength of bearing, and the maximum entropy principle is used to obtain the approximation of the minimum human error factors. Additionally, reliability-based sensitivity indices are derived to investigate the parametric significance of random input variables. Using B218 bearing as an example, the engineering application of this method in dynamic reliability and reliability-based sensitivity analysis is illustrated. Monte Carlo simulation is performed to verify the accuracy and effectiveness of the method to provide benchmark results. The results show that the Poisson’s ratio have a greater influence on the dynamic reliability of ACBBs than other parameters. Improving parameters such as ball diameter, raceway groove curvature radius and contact angle can also improve the reliability of bearings to some extent.

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

As the demand for battery-powered and hydrogen-based electric vehicles continues to rise, the need for integrated charging and refueling systems with energy systems becomes more critical. Therefore, it is crucial to consider the stations serving diverse vehicle users during the system design process. This paper presents an optimal sizing strategy for a multi-energy system that covers clean energy sources and two energy storage technologies under the presence of a fast-charging station and a hydrogen refueling station for various types of car users. For the considered system, the optimal sizing problem aims to minimize the loss of power supply probability (LPSP) and annualized total life cycle cost (TLCC) through a two-stage stochastic programming-based multi-objective optimization approach. Using a scenario-based approach, the study considers the uncertainties associated with renewables, homeowner’s vehicle demands, and energy demands from homes and stations. Different cases are produced to evaluate the impacts of the different stations with the availability of various electric vehicle profiles under different levels of reliability. The TLCC decreases if LPSP increases in each case. Also, the cost of installing a fast charging station is significantly lower than that of a hydrogen refueling station for all LPSP limits. Moreover, the overall cost of the system design is at its lowest when homeowners’ vehicles provide vehicle-to-grid services. It is worth noting that when the station loads which come from the customers’ vehicle increase by 10%, the TLCC increases by 1.19%. Additionally, the revenue from hydrogen refueling station is approximately 70% less than the fast charging station.

YouTube Videos Are a Moderately Comprehensive, Reliable, and Quality Option to Learn About “Multiple Sclerosis and Sexuality”

Objectives This study aimed to evaluate the performance, comprehensiveness, reliability, and quality of English-language YouTube videos addressing the subject of multiple sclerosis and sexuality. Methods In August 2023, a search was conducted on a computer using the keywords “multiple sclerosis and sexuality,” “multiple sclerosis and sexual health,” “multiple sclerosis and sexual health problems,” and “multiple sclerosis and sexual dysfunction” for this descriptive study. According to predetermined inclusion and exclusion criteria, 38 videos that met the research purpose were examined. The related URLs were recorded. For each video, the following information was collected: content producers, performance with YouTube statistics, comprehensiveness with a form developed by researchers, reliability with Singh’s Reliability Evaluation Form, and quality with Global Quality Scale. Two researchers independently evaluated the videos. Results Eighty-nine and a half percent of the videos contained information presented by professionals. The average number of views was 2699.132 ± 3382.848, the comprehensiveness score was 4.2 ± 1.711, the reliability score was 3.184 ± 1.182, and the quality score was 3.421 ± 1.2. Nearly half (42.2%) contained good and useful information for viewers, and half (50%) had high video quality. The reliability and quality scores of videos containing each item in terms of comprehensiveness were higher compared to videos that did not include that item (p < 0.05). In addition, the videos with higher comprehensiveness scores had higher quality and reliability scores (p < 0.001). Conclusion These results underscore the constrained performance attributes of YouTube videos addressing multiple sclerosis and sexuality, with their content exhibiting a moderate level of comprehensiveness, reliability, and quality. These results may provide a basis for increasing the effectiveness of YouTube videos on multiple sclerosis and sexuality.

Next-Generation Protection: Leveraging Federated Learning and Blockchain for Intrusion Detection in Smart Vehicle Network

Smart Car's era ushers in new challenges, some of which are in the field of information security such as wise cyber-attack prevention. Traditional IDS systems would lose everything in such a dynamic environment with their centralized architectures, and this approach could create single points of failure and privacy issues. Along with interconnectedness, cyber security becomes an inevitable problem as smart vehicles are incorporated into a daily life. Traditional security mechanisms usually lack scalability and privacy, which brings about the need to develop alter-nate or innovative methods. This research demonstrates a mixed security system that combines both federated learning and blockchain technologies to improve intrusion detection in smart vehicular networks. We evaluated the effectiveness of this framework using four machine learn-ing models as respectively; Support Vector Machine (SVM), Decision Tree, Neural Network, and Random Forest. Empirical results show that SVM had the highest accuracy of both 93.88% in training and 91.84% in validation, which is higher than Decision Tree, Neural Network, and Random Forest models. These findings evidently demonstrate that the federated learning and blockchain are a strong solution for the plausible security of smart vehicle networks; with SVM being employed mostly in complex security scenarios. DOI: https://doi.org/10.52783/pst.353

Some results on the odds rate order along with its statistical testing

Recently, Lando, et al. [2022. Properties of increasing odds rate distributions with a statistical application. Journal of Statistical Planning and Inference 221,313–-325 ] define the odds rate order and show that it is stronger than the usual stochastic order, but weaker than the hazard rate order. The purpose of this paper is to investigate the other properties of this new stochastic order. Some characterization and preservation results and its relationship with other well-known stochastic orders are given. In addition, two relative ordering based on the odds rate functions are also defined and their relation with the odds rate order is investigated. Applications in statistical theory of reliability and a statistical test for the odds rate order are also included.

ANALYSIS OF THE PROPERTIES OF COATINGS USED TO PROTECT THE PUMP AND COMPRESSOR PIPE

In this article considers basic properties of protective coatings applied to the inner wall of tubing in order to extend pump and compressor pipe (PCP) service life PCP and minimize operational risks. The main complicating factors, such as scale deposits, corrosion aggressiveness, mechanical impurities, etc., arising during PCP operation are specified, groups of test media and test parameters for grading different types of coatings according to their resistance to complicating factors are singled out. Influence of various aggressive media on PCP, such as distilled water, petroleum product simulator, etc., coatings is described, and test media and test parameters for protective coatings are reviewed. The basic methods for determining the physical and mechanical characteristics, such as determining adhesion, thickness and dielectric continuity, of coatings are given, and brief informative and descriptive references for each of them are given. Recommendations are given for testing protective coatings on the inner surface of PCP for compliance with technical requirements, which ultimately allows selecting materials most resistant to aggressive field environments. The relevance of studying the influence of aggressive media on the protective coatings of the inner wall of tubing is due to the need to increase the level of reliability and durability of technical systems in operating conditions. The work is of interest to specialists in the field of materials science, technical expertise and tubing design, as well as researchers involved in the development of new materials and methods for surface protection in operating conditions. The results obtained will optimize the choice of protective coatings for specific operating conditions, which helps to increase the service life of tubing and reduce maintenance costs. This is of direct importance for reducing the risks of accidents and the environmental consequences of oil and gas production processes. In addition, the development of innovative materials and methods for protecting the surfaces of the inner wall of the tubing contributes to improving the efficiency of the operation of oil and gas wells, as well as helps to reduce equipment downtime.

Axial compression performance of underwater columns reinforced with stainless steel tube-FRP grids

Stainless steel tubes have excellent plasticity and ductility, and FRP grids have favorable advantages for application in specific structural reinforcements. For systems including underwater construction without drainage, underwater columns reinforced with stainless steel tube-FRP grids are proposed using nondispersive mortar (NDM) as the reinforcement layer to best leverage the material properties of all elements. This paper carried out axial compression tests on 18 underwater columns reinforced with stainless steel tube-FRP grids (SS-FGWCs), 6 columns reinforced with stainless steel tube-FRP grids on land (SS-FGLCs), 3 underwater columns reinforced with stainless steel tubes (SSWCs), and 3 unreinforced concrete columns. The research parameters include the number of FRP grid layers, the types of FRP grids, and the reinforcement layer mortar pouring environments. The test results showed that FRP grids can be combined with stainless steel tubes (SSs) and nondispersive mortar (NDM) to reinforce concrete columns in underwater construction without drainage. In addition, compared with those of the SSWC specimens, after the BFRP grid and CFRP grid were built in, the ultimate stresses of the specimens increased by 13.4% ∼ 20.8% and 19.5% ∼ 25.4%, respectively, and the ultimate strains increased by 6.8% ∼ 31.7% and 21.8% ∼ 59.1%, respectively. Compared to BFRP grids, CFRP grids provide better reinforcement of the underwater columns. Existing prediction models were evaluated for ultimate stress calculations, the calculation methods for the ultimate stress and ultimate strain applicable to the SS-FGWCs and SS-FGLCs were revised, and a calculation model for the FRP grid failure point was proposed. Finally, a corresponding full stress–strain curve model was established.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

A qualitative analysis of cascading effects of critical infrastructure service failure post torrential floods in formal & informal settlement: the study-case of Medellin city, Colombia

ABSTRACT Critical infrastructure (CI) services are crucial to the functioning of modern communities, and a single CI failure can cause cascading effects through a network of services, impacting an entire city. Challenges such as climate change and rapid urbanisation are increasing the risk of cascading effects of CI failure, particularly impacting the urban poor in peri-urban and informal settlements. This study uses a systems thinking approach to validate the hypothesis if CI services are susceptible to generating severe cascading effects in informal than formal settlements. Qualitative data was collected on cascading effects via interviews and focus group discussion in a formal and an informal settlement in Medellin, Colombia. The responses were analysed and structured using a service chain framework and a Causal Loop Diagram. Our findings show that cascading effects vary between the formal and informal contexts concerning CI service availability and accessibility and may be attributed to differences in demography, geography, socio-economic development, risk regulations, community coping capacities and other factors associated with urban development.

The Role of Flexibility in the Integrated Operation of Low-Carbon Gas and Electricity Systems: A Review

The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system’s ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems.

Robust language-based mental health assessments in time and space through social media

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about “mentally unhealthy days” or feelings of “sadness.” Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.