Software Tool for Importance Measures Computation Used in Reliability Analysis

Engineering fuzzy heat conduction problem with subjective uncertainties in input parameters constitutes a significant challenge. Based on fuzzy and interval theory, this paper presents novel numerical methods to efficiently identify the effect of fuzzy uncertainty on the system reliability analysis and optimization design. Firstly using the interval ranking strategy, the interval safety possibility in the transition state can be precisely quantified, and the eventual fuzzy safety possibility is calculated by integral operation. Then a fuzzy reliability-based optimization model is established with considerable computational cost caused by the two-layer nested loop. In order to improve the computational efficiency, a subinterval perturbation method based on the first-order Taylor series is presented to replace the inner loop. Comparing numerical results with traditional reliability model, two numerical examples are provided to evidence the superiority of proposed model and method for fuzzy reliability analysis and optimization in practical engineering.

In view of the current study on the reliability analysis for complex new equipment systems, the fault tree analysis method is used in reliability engineering (Fault Tree Analysis, FTA). However, qualitative and quantitative analysis for complex equipment systems reliability are difficult problems. In this paper, firstly, fault tree analysis method is introduced, secondly, through an instance of a certain type of equipment, mathematical description and analysis methods for qualitative and quantitative analysis of system reliability are carried out. Finally, the results from fault tree analysis is presented and discussed, which shows that FTA is a method can quickly and accurately analyze equipment systems reliability, and is a designer tool for evaluation and improvement of system reliability.

We performed a human reliability analysis of chemotherapy medication preparation for a comprehensive assessment of risks to both the pharmacy personnel and patients in a medium-sized hospital’s oncology department. We employed techniques such as hierarchical task analysis, concept maps, Reason’s (1990) Generic Error Modeling System (GEMS), and both qualitative and quantitative prospective Cognitive Reliability and Error Analysis Method (CREAM). Our results indicated that the pharmacist predominantly worked in tactical control mode, resulting in generally high performance reliability. We nevertheless identified several issues for risk mitigation and made recommendations for further improvement of safety of the pharmacy operation. This project highlights the benefits of systematic analyses of apparently routine operations. It also allowed us to evaluate both relative and combined merits of the methods used and test the feasibility of a novel technique, CREAM, in a healthcare application.

In this article we transmute the type I half logistic distribution using quadratic rank transmutation map to develop a transmuted type I half logistic distribution. The quadratic rank transmutation map enables the introduction of extra parameter into its baseline distribution to enhance more flexibility in the analysis of data in various disciplines such as reliability analysis in engineering, survival analysis, medicine, biological sciences, actuarial science, finance and insurance. The mathematical properties such as moments, quantile, mean, median, variance, skewness and kurtosis of this distribution are discussed. The reliability and hazard functions of the transmuted type I half logistic distribution are obtained. The probability density functions of the minimum and maximum order statistics of the transmuted type I half logistic distribution are established and the relationships between the probability density functions of the minimum and maximum order statistics of the parent model and the probability density function of the transmuted type I half logistic distribution are considered. The parameter estimation is done by the method of maximum likelihood estimation. The flexibility of the model in statistical data analysis and its applicability is demonstrated by using it to fit relevant data. The study is concluded by demonstrating that the transmuted type I half logistic distribution has a better goodness of fit than its parent model. We hope this model will serve as an alternative to the existing ones in the literature in fitting positive real data.

.The recent interest in human dynamics has led researchers to investigate the stochastic processes that explain human behaviour in various contexts. Here we propose a generative model to capture the dynamics of survival analysis, traditionally employed in clinical trials and reliability analysis in engineering. We derive a general solution for the model in the form of a product, and then a continuous approximation to the solution via the renewal equation describing age-structured population dynamics. This enables us to model a wide rage of survival distributions, according to the choice of the mortality distribution. We provide empirical evidence for the validity of the model from a longitudinal data set of popular search engine queries over 114 months, showing that the survival function of these queries is closely matched by the solution for our model with power-law mortality.

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in \cite{FENN15} so that it can generate mixture models,in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.

Most reliability models in the classical reliability theory use probability theory to quantify information under uncertainty and these models have a high demand of available data. While in practical applications, data related to system sand components are usually sparse or incomplete, moreover, some quantification of uncertainty should be based on subjective information and moreover subjective information may often be expressed in natural language. In these cases, classical reliability theory can not provide an effective way for the reliability analysis of systems. As a generalization of probability theory, imprecise probability theory may provide a tool which is called the natural extension to solve such problems. Research on the application of imprecise probability in reliability engineering has made a great progress. Unfortunately, this is all about theory and there are few applications and cases in practice. Therefore, this paper tried to overcome the flaws and shortcomings of classical reliability theory, and apply some theories of imprecise probability in practical engineering, and conduct reliability analysis on automotive semi-axle with establishing imprecise probability-based reliability model of automotive semi-axle. Natural extension is undoubtedly a powerful tool to compute reliability measures, however, when number of variables is large; the computation of natural extension is very difficult. Effective methods to simplify and build reliability models deserve more debate in the future.

PurposeDegraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.Design/methodology/approachThe research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.FindingsThe proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.Research limitations/implicationsThe models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.Practical implicationsThese models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.Originality/valueA unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

In practical engineering systems, accounting for various uncertainties during the design process is paramount. However, reliability analysis in structural engineering often entails substantial computational costs, particularly when dealing with implicit performance functions and scenarios involving very low failure probabilities. This inherent complexity underscores the challenges faced in real-world applications, where efficient and accurate reliability assessments are crucial for ensuring structural integrity and safety. In recent years, the concept of utilizing surrogate models for reliability analysis has garnered significant attention. The approach outlined in this study employs an innovative surrogate framework that concurrently integrates Cascade-forward Neural Networks (CFNN) and Self-Organizing Map (SOM) networks, alongside an optimized kriging model. The final reliability assessment is then determined as a weighted average of the outputs from these integrated models. To comprehensively illustrate the effectiveness of the proposed algorithm, a diverse range of examples are included: five mathematical examples and five engineering examples. Furthermore, a detailed discussion highlights the benefits of this proposed method in comparison to alternative approaches. The results demonstrate the effective performance of the developed methodology. For instance, in the mathematical examples, the minimum improvement observed over other methods is an 81%, coupled with an approximate 0% error in reliability calculation. Similarly, for the engineering examples, a minimum improvement of 47% is achieved over existing methods, with the reliability calculation error remaining low at approximately 1%.

A bibliometric analysis of human reliability research

This paper proposes a quantitative reliability analysis method for power systems with multi-level standby structure based on GO method. First, the GO model used to describe the multi-level standby structure is proposed. Then, the reliability analysis process for such systems based on GO method is formulated. Finally, the proposed GO method is applied in reliability analysis of AC power system for single unit with 100% capacity divisions in nuclear power plant. In order to verify the feasibility, advantage and reasonable of the proposed GO method, its analysis result compared with the analysis result of GO method without considering standby correlations among the multi-level standby structure. All in all, this study provides guidance of reliability analysis for power systems with multi-level standby structure.

Accounting for the reliability analysis of a generalized phased-mission system (GPMS) with two-level modular imperfect coverage (MIPC), the dynamic behavior & the statistical dependencies across phases are the most important factors for reliability modeling & analysis. There exist several approaches, such as a family of decision diagrams called ternary decision diagrams (TDD) and a multi-state binary decision diagram (MBDD)-based approach, proposed for the GPMS reliability evaluation. Based on simple & efficient algorithm (SEA) and binary decision diagram for phased-mission systems (PMS-BDD), we present a separable solution called GPMS-BDD approach, adding some new rules of phase algebra and a new BDD operation to ordinary rules of phase algebra and BDD operations of PMS-BDD. The computational complexity decreases due to taking advantage of the nature of the SEA & PMS-BDD, the separation of mission/phase imperfect coverage from the solution combinatorics & uses of phase algebra dealing with the dependencies across the phases. In this paper, the GPMS-BDD is presented, and compared with existing methods for analyzing the reliability of GPMS with MIPC & combinatorial phase requirements (CPR). An example generalized phased-mission system is analyzed to illustrate the advantage of our approach. GPMS analysis approach is an important part of reliability engineering. The upward two-level modular imperfect coverage model (UIMIPCM) helps lower complexity of reliability analysis. The GPMS-BDD approach is efficient and integrated, helping a reliability engineer use existing software packages for fault-tolerant GPMS

Statistical model selection and evaluation methods like Akaike information criteria (AIC) and Monte Carlo simulation (MCS) have often established efficient output for reliability analysis with large sample size. Information criterion can provide better model selection and evaluation in small sample sizes setup by considering the well-known measure of bootstrap resampling. Our purpose is to utilize the capabilities of bootstrap resampling in information criterion to check for uncertainty arising from model selection as well as statistics of interest for small sample size using reliability analysis. In this study, therefore, a unique and efficient simulation scheme is proposed which contemplates the best model selection devised from efficient bootstrap simulation or variance reduced bootstrap information criterion to be combined with reliability analysis. It is beneficial to compute the spread of reliability values as against solitary fixed values with desirable statistics of interest for uncertainty analysis. The proposed simulation scheme is verified using a number of sample size focused response functions under repetitions-centred approach with AIC-based reliability analysis for comparison and MCS for accuracy. The results show that the proposed simulation scheme aids the statistics of interest by reducing the spread and hence the uncertainty in sample size-based reliability analysis when compared with conventional methods.

Reliability and safety analysis for systems of fusion device

As the core foundation of reliability engineering and one of the most crucial aspects of general quality, reliability has been attracting increasing attention from industry and academia. Numerous publications on reliability analysis (RA) have appeared in academic journals, conference proceedings, and technical reports. However, there has not been a systematic summary that covers RA data, models, methods, indicators, and software comprehensively. To fill this gap, this paper analyzed the challenges and the state of the art, as well as determined the future prospects for research on RA. This paper introduces the features of modern complex electromechanical systems (CES), then summarizes the current challenges to the RA of CES. Next, it reviews the latest developments, such as reliability theories, models, methods, and software, then summarizes and compares their advantages and limitations. Finally, it discusses future prospects from the perspectives of reliability data, modeling, decoupling, evaluation indicators, and timely prediction. It is expected that this paper would serve as an introduction to RA for researchers new to this field and as a summary of the current frontiers of RA for experienced researchers.