Reliability Analysis Model Research Articles

A dynamic system reliability analysis model on safety instrumented systems

This paper introduces a novel hybrid dynamic model for complex systems reliability assessment. The model synergizes expert knowledge elicitation and an enhanced Dempster-Shafer Theory (DST) with Dynamic Bayesian Networks (DBNs) modeling, aiming to surmount the limitations such as uncertainty and static modeling inherent in traditional methods. The proposed model is deployed on a Safety Instrumented System (SIS) designed to prevent runaway reactions within a Continuously Stirred Tank Reactor (CSTR), considering factors such as system degradation, human interventions, and proof testing on system reliability. The analysis pinpointed the logic solver subsystem as the principal vulnerability within the assessed SIS, leading to targeted recommendations to bolster system reliability. The outcomes offer insights for a wide range of safety-critical systems aiming to augment the safety and efficacy of SISs, thereby advancing safety and resilience management across various complex engineering systems, particularly in contexts where field data is scant.

Quantified active learning Kriging model for structural reliability analysis

A quantified active learning Kriging-based (qAK) methodology for structural reliability analysis is presented. The proposed approach is based on an updated probability density function (PDF), which is dominant in the vicinity of the limit-state surface. This PDF is created using weights based on an improved learning function called the most probable misclassification function. This function is used as a metric for efficiently updating the Kriging model, as it symmetrically quantifies the uncertainty of candidate points in terms of the model’s accuracy. The proposed approach accurately approximates the points that lie on the limit-state surface. Moreover, a probabilistic-based stopping criterion is proposed. The new support points are selected using the weighted K-means algorithm and the sample from the updated PDF. Thus, the method does not require solving an optimization problem or using a sampling algorithm. The proposed qAK methods are more reliable and robust than previous implementations of the Kriging method for structural reliability assessment. The proposed approach is presented within the framework of standard reliability methods, i.e., the Monte Carlo and the Subset Simulation methods. The efficiency of the proposed qAK methods is demonstrated with the aid of six case studies.

A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis

Reliability analysis of highly sensitive structures is crucial to prevent catastrophic failures and ensure safety. Therefore, these safety-critical systems are to be designed for extremely rare failure events. Accurate statistical quantification of these events associated with a very low probability of occurrence requires millions of evaluations of the limit state function (LSF) involving computationally expensive numerical simulations. Variance reduction techniques like importance sampling (IS) reduce such repetitions to a few thousand. The use of a data-centric metamodel can further cut it down to a few hundred. In data-centric metamodeling approaches, the actual complex numerical analysis is performed at a few points to train the metamodel for approximating the structural response. On the other hand, a physics-informed neural network (PINN) can predict the structural response based on the governing differential equation describing the physics of the problem, without a single evaluation of the complex numerical solver, i.e., data-free. However, the existing PINN models for reliability analysis have been effective only in estimating a large range of failure probabilities (10-1∼10-3). To address this issue, the present study develops a PINN-based data-free reliability analysis for low failure probabilities (<10-5). In doing so, a two-stage PINN integrated with IS (PINN-IS) is proposed. The first stage is employed to approximate the most probable failure point (MPP) appropriately while the second stage enhances the accuracy of LSF predictions at the IS population centred on the approximated MPP. The effectiveness of the proposed approach is numerically illustrated by three structural reliability analysis examples.

스포츠 브랜드 매장의 체험마케팅이 브랜드사랑에 미치는 영향 - 관여도의 조절효과를 중심으로

The aim of this study is to investigate the relationship between experiential elements in sports brand stores and brand love, with involvement serving as a moderating effect. This research seeks to enable sports product manufacturers and retailers to better understand consumers and establish a foundation for experiential marketing strategies. The survey was conducted over a 15-day period from January 10, 2024, to January 25, 2024. A total of 720 questionnaires were distributed online, and after excluding insincere responses, 697 questionnaires were used as the final data for analysis. The collected survey data were analyzed using SPSS 26.0 and AMOS 28.0, employing frequency analysis, factor analysis, reliability analysis, correlation analysis, and structural equation modeling. The results of the analysis are as follows: First, in the study of sports brand store experiences, targeting consumers who have experienced sports brand experiential marketing in Shanghai, China, it was found that sensory, emotional, cognitive, behavioral, and relational elements have a significant positive impact on brand love. Second, involvement was found to have a significant moderating effect on the relationship between experiential elements (sensory, emotional, cognitive, behavioral, and relational) and brand love. Therefore, it is important to develop experiential marketing strategies that enhance consumer experiences, leveraging the fact that the experiential elements (sensory, emotional, cognitive, behavioral, and relational) at sports brand stores positively impact brand love. In particular, since the relationship between experiential elements and brand love is strengthened when consumer involvement is high, it is necessary to plan customized experiential marketing activities to increase involvement. Such an approach will contribute to strengthening the relationship with consumers and enhancing brand loyalty.

A data-driven underground gas storage production system string failure prediction model for time-varying reliability analysis

In underground gas storage (UGS) systems, the production tubing string system undergoes complex thermo-mechanical loading conditions, leading to fatigue damage accumulation and potential failure risks. To accurately assess the fatigue reliability and mitigate risks, it is crucial to develop a data-driven physics-informed uncertainty modeling approach that captures the coupled thermo-mechanical effects and accounts for various uncertainties. This study proposes a data-driven physics-informed modeling method based on thermo-mechanical coupling for fatigue reliability assessment and risk mitigation in critical UGS systems. A quantitative analysis method is introduced, which relies on small-scale multi-stage loading experiments and combines the stochastic degradation process with time-series reliability theory. To analyze the fatigue life of joints under complex loading conditions, a modified S-N curve model is developed. This model takes into account the influences of the dimension coefficient, stress concentration factors, surface finish coefficients, and thermal load. It considers the uncertainties associated with different temperature loads and surface finish on fatigue life. By integrating physics-informed modeling, uncertainty quantification, and fatigue damage accumulation analysis, this approach provides a comprehensive framework for fatigue reliability assessment and risk mitigation in critical UGS systems. It enables informed decision-making for safe and reliable operations, as well as optimized maintenance strategies, ultimately enhancing the overall system performance and mitigating potential failures.

Structural relationship between mobile shopping characteristics, consumption values, impulse buying, and consumer satisfaction

This study aims to empirically determine the structural relationship between mobile shopping characteristics, consumption values, impulse buying, and satisfaction in order to promote an understanding of impulse buying in the mobile shopping environment. To this end, we conducted a survey using the convenience sampling method on sports management majors attending a four-year university in Seoul, South Korea, who have experience purchasing sports products on a mobile device. We analyzed the collected data using descriptive statistics analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structural equation modeling. The key findings are as follows: First, mobile shopping attributes had a positive effect on consumption values. Second, consumption values of college student consumers in the mobile shopping environment had an effect on impulse buying. Third, impulse buying in the mobile shopping environment had a positive effect on the satisfaction of college student consumers. The results of this study have significance in that they provide the basic data for establishing efficient marketing strategies that can induce impulse buying to act as a positive shopping driver. Furthermore, this study is significant because it empirically demonstrates the casual relationship between impulse purchases and consumer satisfaction, and that consumption value is a determinant of impulse purchases.

Estimation of the mean exponential survival time under a sequential censoring scheme

The exponential distribution can provide a simple and appealing survival-time model in reliability analysis and life tests. Due to certain experimental designs, time limitations, budgetary requirements, and other reasons, however, only censored data can be obtained. In this paper, we propose a novel estimation procedure for the mean survival time of an exponential distribution under a sequential censoring scheme, which can be treated as a combination of type I censoring and type II censoring. The procedure makes a trade-off between estimation error and sampling cost, using the minimum number of observations. An extensive set of Monte Carlo simulations is conducted to further validate its remarkable performance. To demonstrate the practical applicability, we then implement this newly proposed procedure to assess the reliability of various Backblaze's hard disk models.

A new failure analysis and validation scheme for shale gas pilot processes

Shale gas pilot processes are usually composed of skid-mounted assemblies of individual equipment, with weak process matching, unknown unit equipment wear, and high failure rates. We propose a solution using the grey relational analysis model, analytical hierarchy process model, and reliability analysis model to achieve failure analysis, prediction, and improvement validation, respectively. The probability and degree of influence relationships between the factors affecting the occurrence of failures are discussed. The practical application of the study area shows that: when the threshold value is set to 0.8833, it can match the grey correlation conclusion well, connect the logic of hierarchical analysis, and cluster the engineering problems; this study area needs to focus on preventing the problems of erosion damages and puncture leakage. Although the short life of the nozzle has a large impact on the overall process, but the frequency of occurrence is not high, the next step needs to continue to collect data, using other mathematical models to mine the fault data internal information.

A modular model for reliability analysis model of PMS with multiple K/N subsystems and mixed shocks

AbstractIn this article, a modular model is proposed for the reliability modeling of phased mission systems (PMSs) with complicated system behaviors. By the modular method, the system is divided into several levels: system, subsystem, and components. At the component level, the shock effect, including self‐degradation, additional wear and damage caused by shocks, is considered and the component reliability is evaluated. Then, the reliability modeling method of the K/N system consisting of multiple K/N subsystems is proposed, by a mathematics reasoning method. The correctness of the proposed method is also verified by a Monte Carlo (MC) simulation procedure. At last, the modular method is applied at the system level, and the reliability of a practical engineering case, the attitude and orbit control system (AOCS), is evaluated for illustration. Meanwhile, the parameter sensitivity analysis is also carried out for implementation.

Multi-fidelity wavelet neural operator surrogate model for time-independent and time-dependent reliability analysis

Operator learning frameworks have recently emerged as an effective scientific machine learning tool for learning complex nonlinear operators of differential equations. Since neural operators learn an infinite-dimensional functional mapping, it is useful in applications requiring rapid prediction of solutions for a wide range of input functions. A task of a similar nature arises in many applications of uncertainty quantification, including reliability estimation and design under uncertainty, each of which demands thousands of samples subjected to a wide range of possible input conditions, an aspect to which neural operators are specialized. Although the neural operators are capable of learning complex nonlinear solution operators, they require an extensive amount of data for successful training. Unlike the applications in computer vision, the computational complexity of the numerical simulations and the cost of physical experiments contributing to the synthetic and real training data compromise the performance of the trained neural operator model, thereby directly impacting the accuracy of uncertainty quantification results. We aim to alleviate the data bottleneck by using multi-fidelity learning in neural operators, where a neural operator is trained by using a large amount of inexpensive low-fidelity data along with a small amount of expensive high-fidelity data. We propose the multi-fidelity wavelet neural operator, capable of learning solution operators from a multi-fidelity dataset, for efficient and effective data-driven reliability analysis of dynamical systems. We illustrate the performance of the proposed framework on bi-fidelity data simulated on coarse and refined grids for spatial and spatiotemporal systems.

Modeling and reliability analysis of the micro-milling process considering stochastic tool wear with surrogate models

Micro-milling technology is widely employed in manufacturing micro-precision parts due to its flexible processing and high accuracy. Compared to the macro-milling process, micro-milling has a smaller machining size, which leads to tool wear, tool runout, and other factors being more sensitive to the impact of machining quality. This work proposes a reliability evaluation framework for machining systems based on the micro-milling mechanical model and its surrogate method. Establish a cutting force model under shear and plow conditions with periodic variations in instantaneous uncut thickness. Subsequently, the probability distribution of the cutting parameters is obtained based on Bayesian updates. An improved buffer failure probability method is proposed to quickly get the micro-milling system failure probability. Developing new ensemble and adaptive sampling methods and introducing stochastic configuration network (SCN), an adaptive stochastic configuration network ensemble (ASCNE) model is established to alleviate the time-consuming problem of repeated calculations of mechanical models in reliability analysis. Experimental results indicate that the mechanical model can achieve good predictive performance, and the constructed ASCNE model can achieve high-precision surrogate effects. Additionally, the reliability analysis results of the system can guide tool replacement during the machining process, ensuring safe and efficient execution of the micro-milling.

Non-probabilistic reliability analysis with both multi-super-ellipsoidal input and fuzzy state

In real-world engineering scenarios, incomplete uncertainty information and ambiguous failure states persist and pose significant challenges for structural reliability analysis. This paper introduces a non-probabilistic fuzzy reliability analysis (NPFRA) model featuring fuzzy output states, where the input uncertainties are quantified by a multi-super-ellipsoidal model. Initially, we define both reliability and failure indices of NPFRA, and provide the corresponding Monte Carlo simulation (MCS) solution. Additionally, an extended variable space (EVS) method is established to transform the NPFRA problem into a conventional non-probabilistic reliability analysis (NPRA) one, and MCS based on EVS is derived accordingly. To address the efficiency issue of MCS, a novel method called active learning kriging with norm-constrained expected risk function (ALK-NERF) is developed explicitly for NPFRA. Four examples are adopted to verify the rationality and effectiveness of the proposed ALK-NERF for NPFRA.

A stochastic rainfall model for reliability analysis of rainfall-induced landslides

ABSTRACT Rainfall is an important trigger for a large number of landslides. Physics-based quantitative risk assessment of rainfall-induced landslides requires modelling of rainfall uncertainty in addition to soil parameters. Explicit modelling of rainfall uncertainty should consider the uncertainties in rainfall intensity, duration, as well as the occurrence of rainfall events. Towards this aspect, this study proposes a stochastic rainfall model, where the joint distribution of rainfall duration and intensity is constructed using copula theory and the occurrence of rainfall events is described using Poisson process. Based on the proposed stochastic rainfall model, a computationally efficient reliability method with a machine learning-based surrogate model is developed for assessing the failure probability of a slope subjected to rainfall infiltration within a given time period. An illustrative example with real rainfall data from Singapore is utilised to demonstrate the proposed approach. The results suggest that the slope failure probability is less sensitive to the uncertainty in rainfall occurrence, but highly sensitive to the adopted inter-event time definition for characterisation of the rainfall event. Overall, this study provides a useful stochastic rainfall model and some practical guidelines for quantitative risk assessment of rainfall-induced landslides.

스크린골프 서비스품질과 인지적, 감정적 반응에 관한 태도 관계

The purpose of this study was to investigate relationship among screen golf service quality, cognitive and emotional responses on attitudes. The study recruited participants who had experience participating in screen golf facilities as the target population. Surveys were distributed primarily at screen golf facilities located in the capital area of Seoul and Gyeonggi province. A total surveys of 294 were used for analysis. The data processing methods included exploratory factor analysis, confirmatory factor analysis, reliability analysis, frequency analysis, correlation analysis, and structural equation modeling. Based on the above results, the following conclusions are suggested. First, it was found that the physical environment aspect of screen golf facilities' service quality influences cognitive responses. Second, the physical environment aspect of screen golf facilities' service quality influences emotional responses. Third, cognitive responses were found to influence attitudes. Screen golf facilities should devise measures to improve the physical environment aspect of service quality.

Structural Strength Analysis and Optimization of Commercial Aircraft Nose Landing Gear under Towing Taxi-Out Conditions Using Finite Element Simulation and Modal Testing

In the field of civil aviation, the nose landing gear is a critical component that is prone to damage during taxiing. With the advent of new technologies such as towing taxi-out and hub motors, the nose landing gear faces increasingly complex operational environments, thereby imposing higher performance demands. Ensuring the structural safety of the nose landing gear is fundamental for the successful application of these technologies. However, current research on aircraft nose landing gear under these new conditions is somewhat lacking, particularly in terms of reliable analysis models for real-world scenarios. This study focuses on a typical Class C aircraft, specifically the B-727 model, for which a finite element model of the nose landing gear is developed. Modal testing of the aircraft’s nose landing gear is conducted using the impact hammer method, and the results are compared with those from the simulations. The experimental data indicate that the error range for the first seven natural frequencies is between 0.23% and 9.27%, confirming the high accuracy of the developed landing gear model. Furthermore, with towing taxi-out as the primary scenario, a dynamic model of the aircraft towing system is established, and an analysis on the structural strength and topological optimization of the nose landing gear under various conditions, including high speeds and heavy loads, is performed. The results show that the developed model can effectively support the analysis and prediction of the mechanical behavior of the nose landing gear. Under high-speed, heavy-load conditions, the nose landing gear experiences significantly increased loads, with the maximum deformation primarily occurring at the lower section of the shock strut’s outer cylinder. However, no damage occurred. Additionally, under these conditions, an optimized structural design for the landing gear was identified, which, while ensuring structural strength, achieves a 22.32% reduction in the mass of the outer cylinder, also ensuring safety in towing taxi-out conditions.

An efficient latent map multi-fidelity Kriging model and adaptive point-selected strategy for reliability analysis with time-consuming simulations

An efficient latent map multi-fidelity Kriging model and adaptive point-selected strategy for reliability analysis with time-consuming simulations

Human Reliability Analysis for Fishing Vessels in Korea Using Cognitive Reliability and Error Analysis Method (CREAM)

In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments.

The mediating role of employee loyalty and job engagement relationship between job satisfaction and intention to leave among bank employees in Mongolia

Organizations need to focus on employee loyalty, commitment and retention in a dynamic business environment to gain a competitive advantage over rural areas. The success factors for organizational development and well-being are excessively contingent on engaged and skilled employees. This study is anticipated to examine the mediating role of employee loyalty and job engagement on the relationships between job satisfaction and intention to leave in the case of bank employees in Mongolia. The study used data from 444 employees across two private banks in Mongolia. Reliability analysis, correlation analysis, and structural equation modeling (SEM) were used to analyze the data. Results showed a moderately positive relationship between job satisfaction and job engagement and a negative relationship with the intention to leave. Employee loyalty had a weak and negative correlation with job engagement. Employee loyalty was found to partially mediate the relationship between job satisfaction and the intention to leave while job engagement showed no mediation effect. Improving job satisfaction can enhance employee loyalty and reduce intentions to leave the bank. However, enhancing engagement may not affect turnover intentions. Banks should focus on building loyalty among satisfied employees to retain talent. The mediating role of loyalty highlights its importance in influencing whether satisfied employees consider leaving. This study demonstrates the importance of employee loyalty in partially explaining the mechanism behind the relationship between job satisfaction and retention among Mongolian bank employees. The findings provide insights to help banks improve talent management strategies and retention.

Reliability analysis of subsea control module based on dynamic Bayesian network and digital twin

The reliability evaluation of the subsea control module(SCM) is the key to ensure the safety and stability of subsea oil-gas production. The failure probability and reliability of SCM components are dependent on time and working conditions. To analyze the SCMs’ reliability considering varying working conditions, this paper proposed a new digital twin and dynamic Bayesian network(DBN) based model utilizing historical working condition data in reliability analysis. In the proposed framework, critical working condition data is obtained by sensor-based Digital Twin(DT) simulation and used for dynamically updating the parameters in the DBN reliability analysis model. The reliability evaluation of an actual SCM electric system was carried out. The results revealed the most probable failure mode and the most vulnerable components in the system. Finally, the fault prediction based on the back-forward analysis capacity of the proposed method was conducted to predict the probability of the faulty device when unexpected failures occur.

마이크로 매니지먼트 측정도구 타당화 연구

The purpose of this study is to validate the micromanagement measurement developed by Sulphey and Upadhyay (2019). We translated the micromanagement measurement for validation and conducted content validity analysis. The preliminary study has showed that the subfactor structure of the instrument items was verified. In the main study with 392 individuals from Korean employees. Our analysis included factor analysis, reliability analysis, and Rasch model. The results indicated that one item with a low factor loading was removed, leaving the measurement of 14 items in four factors. The study revealed a significant association between micromanagement and organizational silence, as well as passive aggression in Korean organizations. We discuss the expected interpretations, practical applications and theoretical implications. Based on the findings, theoretical and practical implications were further discussed.