International Journal of System Assurance Engineering and Management最新文献

This article examines the research carried out regarding the failure rate in electricity distribution systems. It introduces a comprehensive framework for managing failure rates in power distribution systems. This framework highlights that studies on failure rates in power distribution systems can be categorized into three distinct groups: modifying asset management activities in order to reduce failure rate, evaluate and control threats and risks, emergency measures after failure. In this article, all the studies conducted on the failure rate of electricity distribution systems are listed and presented, and categorized in the form of a comprehensive and conceptual framework. The relation of each category with the failure rate is explained and by studying the process of studies, the research gaps and the roadmap of future studies in the field of failure rate in electricity distribution systems are determined.

In our contemporary world, where technology is omnipresent and essential to daily life, the reliability of software systems is indispensable. Consequently, efforts to optimize software release time and decision-making processes have become imperative. Software reliability growth models (SRGMs) have emerged as valuable tools in gauging software reliability, with researchers studying various factors such as change point and testing effort. However, uncertainties persist throughout testing processes, which are inherently influenced by human factors. Fuzzy set theory has emerged as a valuable tool in addressing the inherent uncertainties and complexities associated with software systems. Its ability to model imprecise, uncertain, and vague information makes it particularly well-suited for capturing the nuances of software reliability. In this research, we propose a novel approach that amalgamates change point detection, logistic testing effort function modeling, and triangular fuzzy numbers (TFNs) to tackle uncertainty and vagueness in software reliability modeling. Additionally, we explore release time optimization considering TFNs, aiming to enhance decision-making in software development and release planning.

Utilization of the shovel fleet as a capital-intensive and operationally important asset in open-pit mines is a key indicator for mine production analysis. This paper investigates shovel utilization in surface mining using a novel smart platform integrated with the shovel operating joystick. It utilizes a unique algorithm to identify and differentiate operational and non-operational time based on comparing real-time data and average loading cycle time. This data is then employed to calculate overall uptime and identify downtime periods. A field study was carried out on six electric cable shovels consisting of P&H 2100 and TZ WK-12, at Sarcheshmeh Copper Mine. The analysis revealed that the average utilization of the whole fleet is equal to 33%, ranging from 16 to 48%, which is dramatically lower than the mine expectations. The statistical analysis showed that in 10–13% of the operating time, the utilization is higher than 75%, which is a moderately acceptable level. Finally, according to the outcomes of the field study and the developed smart platform, it could be concluded that improvements in dispatching system accuracy, revising the grade blending strategies, increasing processing plant flexibility and improved operator training could enhance shovel fleet utilization and whole mine productivity.

Refrigeration is a critical component of thermal environment engineering. The process of removing heat from a substance under precise conditions is referred to as refrigeration. It also includes the process of lowering and maintaining a body's temperature below the ambient temperature. In this paper, we examine the availability and cost function of the system of the Refrigeration plant. This system has three modes: normal, degraded, and failed. The system is divided into four sections: A (Compressor), B (Condenser), C (two standby expansion valves), and D. (three evaporators in series). A standby expansion valve is installed to improve the performance of the refrigeration plant. The supplementary variable technique is used to obtain state probabilities and the inversion process is used to obtain the expression of operational availability and profit functions. The MTTF (mean time to failure) is also estimated. A numerical example is presented with a graphical presentation to illustrate the practical advantages of the model.

This paper deals with multicomponent stress–strength system reliability (MSR) and its maximum likelihood (ML) as well as Bayesian estimation. We assume that ({X}_{1},{X}_{2},dots ,{X}_{k}) being the random strengths of k- components of a system and Y is the applied common random stress on them, which independently follows gamma distribution with parameters (left({alpha }_{1},{lambda }_{1}right)) and (left({alpha }_{2},{lambda }_{2}right)) respectively. The system works only if (sleft(1le sle kright)) or more of the strengths exceed the common load/stress is called s-out-of-k: G system. Maximum likelihood and asymptotic interval estimators of MSR are obtained. Bayes estimates are computed under symmetric and asymmetric loss functions assuming informative and non-informative priors. ML and Bayes estimators are numerically evaluated and compared based on mean square errors and absolute biases through simulation study employing the Metropolis–Hastings algorithm.

This article is concerned with the estimation of parameters, reliability and hazard rate functions of the exponentiated exponential distribution under progressive type-II censoring data. The maximum likelihood estimation and maximum product of spacing methods are presented to estimate the unknown parameters of the model in classical theme. In the Bayesian paradigm, we have considered both likelihood as well as product of spacing functions to estimates of the model parameters, reliability and hazard rate functions. Bayes estimates are considered under squared error loss function (SELF) using gamma prior for the shape parameter and a discrete prior for the scale parameter. Asymptotic confidence and highest posterior density credible intervals have also been obtained for the model parameters and reliability characteristics. Optimal criteria is also employed to find the best censoring scheme among the considered censoring schemes. A Monte Carlo simulation study is used to compare the performances the derived estimators under different progressive type-II censoring schemes. Finally, to illustrate the practical application of the proposed methodology, two real data analysis are conducted.

There is no such thing as stress-free work in today's environment. Every company gave their staff a challenging assignment to do in a certain amount of time. All of the employees are stressed out at work as a result of that work. Professionals in the Information Technology (IT) industry are frequently stressed at work and are at risk of developing health problems as a result of their jobs. The IT sector has a lot of severe workloads and has to deal with several issues like role ambiguity, gender inequality, and long working hours. The current research examines the numerous elements that lead to work-related stress, as well as the influence of demographic factors on stress among IT professionals. A sample of 240 data has been collected from the northern, central, and southern regions of Tamil Nadu. A Convenience Sampling Technique has been performed to collect the information. The results reveal the impact of stress factors on IT professionals in their work environment. Also, the outcome shows the significant impact of demographic factors like age, gender, marital status, and education of employees causing stress in their work environment.

Software vulnerabilities reported every year increase exponentially, leading to the exploitation of software systems. Hence, when a vulnerability is reported, a requirement arises to patch it as early as possible. Generally, this process requires some time and effort. For proper channelizing of the efforts, a requirement comes to predict the severity of the vulnerability so that the more critical ones can be given a higher priority. Therefore, a need arises to build a model that can analyze the data available on vulnerabilities and predict their severity. The experiment of this study is conducted on vulnerability reports of five software of Mozilla. As the data is textual, text mining techniques are applied to preprocess the data and form feature vectors. This input as text creates very high dimensional feature vectors leading to the requirement of dimensionality reduction. Hence, feature selection is done using chi-square and information gain. To develop the classifier, seven machine learning algorithms are chosen. Hence, fourteen software vulnerability severity prediction models (SVSPM) are developed. The result analysis allowed us to find the best-performing SVSPM. It is concluded that the model performed better for the medium and the critical severity level of the vulnerability. Out of the two feature selection techniques, information gain gave better results. An optimum number of features is also determined at which SVSPM gave good results. The best SVSPM using a machine learning algorithm corresponding to each dataset is found as well. A comparison is also made to identify significant differences among various SVSPMs developed using Friedman and Wilcoxon Signed Rank test.

The Gumbel-Hougaard family’s invention of copula distribution paved the way for new research, and it has been widely applied in recent years to a range of series–parallel multi-state complicated engineering systems, but not to agricultural applications. Recent study undertaken by a variety of organizations reveals that food grain production is not keeping up with population growth. Many technocrats use wireless sensing networks to collect and analyze data to increase production; nevertheless, by focusing on general repair, they fall short of their goal. To avoid this problem and restore the broken system as soon as achievable, in this paper we have developed a reliability formula in a way that numerical solutions can be obtained systematically in a reasonable computational time for precision agriculture that makes use of the copula distribution. This paper aims to analyze the various reliability measures such as availability, reliability, mean time to failure, and cost analysis of a wireless computer network for precision agriculture made up of three subsystems in series configuration. Hazard rates of all the units are assumed to be constant and follow exponential distribution, while repair supports general distribution and copula distribution. The system is analyzed by supplementary variable technique, Laplace transformation and Gumbel-Hougaard copula distribution. This paper we have used a significant feature of copula distribution under catastrophic failure by assuming two different forms of failure between neighboring transitions from which one can check the behavioral analysis of the designed system. This research may be beneficial for precision agriculture whereas a k-out-of-n-type configuration exists.

This paper aims to identify and analyze critical success factors (CSFs) of Lean Six Sigma (LSS) implementation in context to Industry 4.0 (I4.0) in Indian manufacturing industries. Twenty CSFs are identified from literature and expert’s opinion. A survey was conducted through administration of designed questionnaire in Indian manufacturing industries and reliability of the factors was tested calculating Cronbach’s alfa (α) value for all responses. Thereafter, out of twenty CSFs, sixteen were found reliable. Further, these sixteen factors were analyzed employing Interpretive Structural Modeling (ISM) technique and leveled as per developed model. The MICMAC analysis is employed for determining driving and dependence power of CSFs. The developed model provides a platform for the practitioners/researchers to design a framework for successful implementation of LSS in view of current manufacturing paradigm of I4.0. On analyzing the data using ISM technique, the ‘Organizational culture and belief’, ‘Effective top management commitment and attitude’ and ‘Motivated and skilled manpower’ are observed to be the most significant CSFs which drive the path for proper implementation of LSS in Indian manufacturing industries. The developed model will enable the practitioners to draw the effective strategy for proper implementation of LSS in view of Industry 4.0. The results will give an edge to the management to think strategically for improvements in this competitive environment.