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1 – 10 of over 97000Abstract
Purpose
Quantitative reliability analysis can effectively identify the time the driving system needs to be maintained. Then, the potential safety problems can be found, and some catastrophic failures can be effectively prevented. Therefore, this paper aims to evaluate the reliability of the switched reluctance generator (SRG) driving system.
Design/methodology/approach
In this paper, a method considering different thermal stresses and fault tolerance capacity is proposed to analyze the reliability of an SRG. A full-bridge power converter (FBPC) instead of the asymmetric half-bridge power converter (AHBPC) is adopted to drive the SRG system. First, the primary fault modes of the SRG system are introduced, and a fault criterion is proposed to determine whether the system fails. Second, the thermal circuit model of the converter is established to quickly and accurately obtain the junction temperature of the devices. At last, the Markov models of different levels are established to evaluate the reliability of the system.
Findings
The results show that the two-level Markov model is the most suitable when compared to the static model and the one-level Markov model.
Originality/value
The driving system of SRG will be more reliable after the reliability of the system is evaluated by the Markov model. At the same time, an FBPC is adopted to drive the SRG. The FBPCs have the advantages of fewer switching devices, higher integration and lower cost. The proposed driving strategy of the FBPC avoids the current reversal and the generation of dead zone time, which has the advantage of reliable operation. In addition, a precise thermal circuit model of the FBPC is proposed, and the junction temperature of each device can be obtained, respectively.
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Satadal Ghosh and Sujit K. Majumdar
The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical…
Abstract
Purpose
The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.
Design/methodology/approach
The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.
Findings
For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.
Practical implications
This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.
Originality/value
With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.
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Xue-Qin Li, Lu-Kai Song and Guang-Chen Bai
To provide valuable information for scholars to grasp the current situations, hotspots and future development trends of reliability analysis area.
Abstract
Purpose
To provide valuable information for scholars to grasp the current situations, hotspots and future development trends of reliability analysis area.
Design/methodology/approach
In this paper, recent researches on efficient reliability analysis and applications in complex engineering structures like aeroengine rotor systems are reviewd.
Findings
The recent reliability analysis advances of engineering application in aeroengine rotor system are highlighted, it is worth pointing out that the surrogate model methods hold great efficiency and accuracy advantages in the complex reliability analysis of aeroengine rotor system, since its strong computing power can effectively reduce the analysis time consumption and accelerate the development procedures of aeroengine. Moreover, considering the multi-objective, multi-disciplinary, high-dimensionality and time-varying problems are the common problems in various complex engineering fields, the surrogate model methods and its developed methods also have broad application prospects in the future.
Originality/value
For the strong demand for efficient reliability design technique, this review paper may help to highlights the benefits of reliability analysis methods not only in academia but also in practical engineering application like aeroengine rotor system.
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The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly…
Abstract
Purpose
The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.
Design/methodology/approach
In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.
Findings
The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.
Practical implications
The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.
Originality/value
Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.
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Nalina Suresh, A.N.V. Rao and A.J.G. Babu
Most of the existing software reliability models assume time between failures to follow an exponential distribution. Develops a reliability growth model based on non‐homogeneous…
Abstract
Most of the existing software reliability models assume time between failures to follow an exponential distribution. Develops a reliability growth model based on non‐homogeneous Poisson process with intensity function given by the power law, to predict the reliability of a software. Several authors have suggested the use of the non‐homogeneous Poisson process to assess the reliability growth of software and to predict their failure behaviour. Inference procedures considered by these authors have been Bayesian in nature. Uses an unbiased estimate of the failure rate for prediction. Compares the performance of this model with Bayes empirical‐Bayes models and a time series model. The model developed is realistic, easy to use, and gives a better prediction of reliability of a software.
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The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.
Abstract
Purpose
The purpose of this study is to propose the time series decomposition approach to analyze and predict the failure data of the repairable systems.
Design/methodology/approach
This study employs NHPP to model the failure data. Initially, Nelson's graph method is employed to estimate the mean number of repairs and the MCRF value for the repairable system. Second, the time series decomposition approach is employed to predict the mean number of repairs and MCRF values.
Findings
The proposed method can analyze and predict the reliability for repairable systems. It can analyze the combined effect of trend‐cycle components and the seasonal component of the failure data.
Research limitations/implications
This study only adopts simulated data to verify the proposed method. Future research may use other real products' failure data to verify the proposed method. The proposed method is superior to ARIMA and neural network model prediction techniques in the reliability of repairable systems.
Practical implications
Results in this study can provide a valuable reference for engineers when constructing quality feedback systems for assessing current quality conditions, providing logistical support, correcting product design, facilitating optimal component‐replacement and maintenance strategies, and ensuring that products meet quality requirements.
Originality/value
The time series decomposition approach was used to model and analyze software aging and software failure in 2007. However, the time series decomposition approach was rarely used for modeling and analyzing the failure data for repairable systems. This study proposes the time series decomposition approach to analyze and predict the failure data of the repairable systems and the proposed method is better than the ARIMA model and neural networks in predictive accuracy.
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Abdel‐Aziz M. Mohamed, Mahmood A. Qureshi and Ali R. Behnezhad
The reliability of accounting internal control systems (AICS) is often viewed as a primary concern of auditors. Over the past three decades, several reliability models have been…
Abstract
The reliability of accounting internal control systems (AICS) is often viewed as a primary concern of auditors. Over the past three decades, several reliability models have been proposed for internal control. The main goal of these models is to provide an objective approach to evaluate the reliability of internal control systems. In addition, the models seek to assess the degree of audit reliance that can be placed on internal controls. This paper has a two‐fold objective: (1) to present an overview of the descriptive and prescriptive reliability models developed for the design and evaluation of internal control systems, and (2) to discuss the effects of various factors on the reliability assessment. Furthermore, two methods to estimate process reliabilities are presented and several numerical examples are provided to show the detailed calculations of the reliability and economic efficiency of accounting internal control systems.
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Thomas Paul Talafuse and Edward A. Pohl
When performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective…
Abstract
Purpose
When performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective actions can be implemented to improve system reliability. Current methods for estimating discrete (one-shot) reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. The purpose of this paper is to present an application of a modified GM(1,1) model for handling system-level testing constrained by small sample sizes.
Design/methodology/approach
The paper presents a methodology for incorporating failure data into a modified GM(1,1) model for systems with failures following a poly-Weibull distribution. Notional failure data are generated for complex systems and characterization of reliability growth parameters is performed via both the traditional AMSAA model and the GM(1,1) model for purposes of comparing and assessing performance.
Findings
The modified GM(1,1) model requires less complex computational effort and provides a more accurate prediction of reliability growth model parameters for small sample sizes and multiple failure modes when compared to the AMSAA model. It is especially superior to the AMSAA model in later stages of testing.
Originality/value
This research identifies cost-effective methods for developing more accurate reliability growth parameter estimates than those currently used.
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Mahesh Narayan Dhawalikar, V. Mariappan, P.K. Srividhya and Vishal Kurtikar
Degraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow…
Abstract
Purpose
Degraded 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/approach
The 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.
Findings
The 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/implications
The models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.
Practical implications
These 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/value
A 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.
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Anusha R. Pai, Gopalkrishna Joshi and Suraj Rane
This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality…
Abstract
Purpose
This paper is focused at studying the current state of research involving the four dimensions of defect management strategy, i.e. software defect analysis, software quality, software reliability and software development cost/effort.
Design/methodology/approach
The methodology developed by Kitchenham (2007) is followed in planning, conducting and reporting of the systematic review. Out of 625 research papers, nearly 100 primary studies related to our research domain are considered. The study attempted to find the various techniques, metrics, data sets and performance validation measures used by researchers.
Findings
The study revealed the need for integrating the four dimensions of defect management and studying its effect on software performance. This integrated approach can lead to optimal use of resources in software development process.
Research limitations/implications
There are many dimensions in defect management studies. The authors have considered only vital few based on the practical experiences of software engineers. Most of the research work cited in this review used public data repositories to validate their methodology and there is a need to apply these research methods on real datasets from industry to realize the actual potential of these techniques.
Originality/value
The authors believe that this paper provides a comprehensive insight into the various aspects of state-of-the-art research in software defect management. The authors feel that this is the only research article that delves into the four facets namely software defect analysis, software quality, software reliability and software development cost/effort.
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