Taxonomy of intelligence software reliability model

Reliability is the probability that the system will perform its intended function under specified working condition for a specified period of time. It is the analysis of failures, their causes and consequences. Reliability analysis is the most important characteristic of product quality as things have to be working satisfactorily before considering other quality attributes. Usually, specific performance measures can be embedded into reliability analysis by the fact that if the performance is below a certain level, a failure can be said to have occurred. Markov model is widely used technique in reliability analysis. The hypoexponential distribution is used in modeling multiple exponential stages in series. This distribution can be used in many domains of application. In this chapter we find a modified and simple form of the probability density function for the general case of the hypoexponential distribution. The new form obtained is used in an application of Markovian systems to find its reliability.

To date, over 75 software reliability models have appeared in the literature These models, however, require sophisticated numerical procedures to obtain estimates of the model parameters. This necessitates the use of a computer. This was the purpose of the development of the program called SMERFS (Statistical Modeling and Estimation of Reliability Functions for Software). It is an interactive program for software reliability modeling that provided a computer-based tool for the software analyst to perform software reliability analyses. That program has undergone a number of revisions over the years. SMERFS/sup /spl and//3 is the newest evolution of SMERFS. It is a strictly PC Windows-based application program that allows the user to carry out hardware, software and/or total systems reliability analyses. The aim is to perform both component- and systems-level assessments.

Fuzzy reliability analysis can be implemented using two discrete optimization maps in the processes of reliability and fuzzy analysis. Actually, the efficiency and robustness of the iterative reliability methods are two main factors in the fuzzy-based reliability analysis due to the huge computational burdens and unstable results. In the structural fuzzy reliability analysis, the first-order reliability method (FORM) using discrete nonlinear map can provide a C membership function. In this paper, a discrete nonlinear conjugate map is proposed using a relaxed-finite step size method for fuzzy structural reliability analysis, namely Fuzzy conjugate relaxed-finite step size method fuzzy CRS. A discrete conjugate map is stabilized using two adaptive factors to compute the relaxed factor and step size in FORM. The framework of the proposed fuzzy structural reliability method is established using two linked iterative discrete maps as an outer loop, which constructs the membership function of the response using alpha level set optimization based on genetic operator, and the inner loop, implemented for reliability analysis using proposed conjugate relaxed-finite step size method. The fuzzy CRS and fuzzy HL-RF methods are compared to evaluate the membership functions of five structural problems with highly nonlinear limit state functions. Results demonstrated that the fuzzy CRS method is computationally more efficient and is strongly more robust than the HL-RF for fuzzy-based reliability analysis of the nonlinear structural reliability problems.

In this paper, a new method is developed that can be applied efficiently in reliability analysis and design verification of structures under random vibration. This method is based on singular value decomposition of modal coordinates cross covariance matrix. Computational algorithm and relative flow chart for this method is developed and compatible reliability criterion with this algorithm has been extracted. In order to results verification, we use Miles relation and Monte Carlo simulation -that are two well known methods in reliability analysis- and all of these methods are applied to a simple finite element model. Results show that new algorithm is more efficient for reliability analysis of structures under random vibration.

Reliability is the ability to operate failure‐free. More specifically, it is defined as the probability of a product to operate free of failures for a specified period of time under specified operating conditions. This definition applies to products, services, systems, hardware, and also software. Reliability is a quality attribute that is highly valued by customers and users of products and services.We need a consistent definition of reliability between software, hardware, and systems. This is so they may be combined to determine the reliability of a product or service as a whole delivered to a user. However, there are differences between hardware and software that affect how we analyze their respective reliabilities. These differences are described.Software reliability engineering (SRE) assesses how well software‐based products and services meet users' operational needs. SRE uses quantitative methods based on reliability measures to do this assessment. The primary goal of SRE is to maximize customer satisfaction. SRE uses such quantitative methods as statistical estimation and prediction, measurement, and modeling. As the reliability of a product or service is highly dependent on operating conditions and the reliability of software is related to how the software is used, the quantitative characterization of the use of software is an integral part in SRE. This characterization of use of software is captured in an Operational Profile and is discussed.Software engineering is a broader discipline of which SRE is a subdiscipline. Software engineering is concerned with all aspects of designing, implementing, and managing the development of software. Other aspects of software engineering include the economics of developing software and the interfaces between software, systems, and humans and with the practices and processes for ensuring the quality of delivered software. Software quality assurance (SQA) monitors the processes and practices for ensuring software quality to ensure they are being followed and that they conform to established industry norms.SRE has been used on a wide variety of products and services. SRE is not limited by what software development methodology is used. There are no known theoretical or practical limits on its application except possibly on very small projects. Software under 1,000 source lines and possibly up to as high as 5,000 source lines may experience few operational failures. Analysis using few failure events results in large error bounds when estimating reliability. However, reliability estimation is only a part of SRE. The other parts of SRE such as determining the operational profile and using it to guide development and test are still useful for very small projects.

PurposeAs a clean and renewable energy source, wind energy will become one of the main sources of new energy supply in the future. Relying on the stable and strong wind resources at sea, wind energy has great potential to become the primary energy. As a critical part of the wind turbine, the gearbox of a wind turbine often bears a large external load. Especially at sea, due to the effects of ocean corrosion, waves and wind, the burden of the wind turbine gearbox is greater, which brings great challenges to its reliability analysis. This study aims to systematically review the reliability research in wind turbine gearboxes and guide future research directions and challenges.Design/methodology/approachThis study systematically reviews some design requirements and reliability analysis methods for wind turbine gearboxes. Then, it summarizes previous studies on wind load uncertainty modeling methods, including the processing of wind measurement data and the summary of three different classifications of random wind speed prediction models. Finally, existing reliability analysis studies on two major parts of the gearbox are described and summarized.FindingsFirst, the basic knowledge of wind turbine gearboxes and their reliability analysis is introduced. The requirements and reliability analysis methods of wind turbine gearboxes are explained. Then, the processing methods of wind measurement data and three different random wind speed prediction models are described in detail. Furthermore, existing reliability analysis studies on two common parts of wind turbine gearboxes, gears and bearings, are summarized and classified, including a summary of bearing failure modes. Finally, three possible future research directions for wind turbine gearbox reliability analysis are discussed, namely, reliability research under the influence of multiple factors on gears, damage indicators of bearing failure modes and quantitative evaluation criteria for the overall dynamic characteristics of offshore wind turbine gearboxes and a summary is also given.Originality/valueThis paper aims to systematically introduce the relevant contents of wind turbine gearboxes and their reliability analysis. The contents of wind speed data processing, predictive modeling and reliability analysis of major components are also comprehensively reviewed, including the classification and principle introduction of these contents.

Software reliability is a quantifiable metric, which is defined as the probability of software to operate without failure for a particular period of time in a specific environment. Various SRGMs (software reliability growth models) have been proposed to predict the reliability of software. The reliability is predicted by calculating the attributes of the software reliability growth models. But the parameters of SRGMs are generally in nonlinear relationships, which is a great problem in finding the optimal parameters by using traditional ways or the techniques for attribute calculation like Maximum Likelihood and least Square Estimation. The following paper is proposing a new approach for calculating the verticals or parameters of SRGM using a hybrid genetic algorithm. This algorithm is the hybridization of GA (Genetic Algorithm) which is real valued g and PSO (particle swarm optimization). Each chromosome is defined as a group of real values in RGA and then the operators of the real valued genetic algorithm are used in directly modifying these chromosomes. While particle swarm optimization is different optimization technique which is an alternative to genetic algorithm because of its simplicity and equal accuracy. The approach which is proposed has several benefits over conventional GA in the vertical or calculations of verticals of SRGM.

Software reliability refers to the ability of a system to perform its intended function under specified conditions for a specified period of time. The first critical step in the software reliability testing process is to create a Software Operational Profile (SOP). Several methodologies for creating SOP have been proposed. Nonetheless, nearly all the proposed studies have neglected the uniqueness of the new software paradigms, despite the fact that these are generally distinguished by their own concepts and methodologies. One of these software paradigms is multi-agent systems. Rather than using a generic one, it would be more useful to propose a specific methodology for creating SOP. In this paper, we propose a methodology for developing Operational Profile for specific kinds of multi-agent systems (so-called normative multi-agent systems). A detailed case study is used to demonstrate this methodology.

A new unequal-weighted sampling method for efficient reliability analysis

Maintenance, rehabilitation and replacement of existing bridges by using cost effective management are increasingly important topics in efforts to deal with aging and deteriorating infrastructure. Recently, engineers have shifted the emphasis from design and construction of new bridges to maintenance and rehabilitation of existing bridges. This paper uses the structural reliability method with a predefined performance function to assess the component performance of the main load carrying members of an inner span in a multi-span bridge. The performance of the components is a function of various random variables with certain probabilistic distributions. The analysis is extended using the same performance function to evaluate the reliability of the bay as subsystem and bridge as a system. System reliability in this case is defined as the probability that a system (bridge) performs its intended function under operating conditions for a specified period of time using second order reliability method (SORM) and Monte Carlo simulations. In addition, deterioration in the main carrying elements (components) due to environmental impacts is introduced and the sensitivity parameters for certain random variables are re-evaluated to determine the inspection and maintenance period of the bridge. The deterioration is imposed as a percentage reduction in certain random variables. The paper involves use of multi reliability analysis at component, sub-system and system levels. Results show that probability of failure (Pf) increases with decrease in distance of the bridge from source of deterioration. Reliability index (β) decreased with increase in the age of the bridge because of corrosion of reinforcing steel. Results also show that the rate of probability of failure for bridges exposed to deteriorating effects increased dramatically after 45-50 years.

Analysis of descriptive epidemiological characteristics of pancreatic cancer in Vojvodina, Serbia. The study covers population of Vojvodina in the period from 2000 to 2009. The method used for data processing was the descriptive. The data, referring to a specified period of time, were analyzed from chronological and demographic aspects and according to histological diagnosis. In the period from 2000 to 2009, there were 2,108 registered cases of pancreatic cancer of which 1,886 had a fatal outcome. Standardized incidence rates varied between 5.7 and 9.1 per 100,000 population in males and between 4.2 and 5.3 in females. Linear incidence trends in males in the specified period of time, based on crude (r=0.7883, p<0.05) and standardized (r=0,6373, p<0,05) incidence rates, demonstrated increase. Annual percent increase in the crude incidence rate was 4.5% in males, and 2.8% in females. Age-standardized mortality rates varied between 5.2 and 7.5 per 100,000 population in males and 3.6 and 4.7 in females. Linear mortality trends in males in the specified period of time, based on crude (r=0.8795, p<0.05) and standardized (r=0.7669, p<0.05) mortality rates, also demonstrated annual percent increase. Data analysis shows unfavorable onco-epidemiological situation related to pancreatic cancer in Vojvodina, in aspects of both incidence and mortality. Absence of primary and secondary prevention does not allow medical institutions to successfully fight against this disease.

In recent years, time‐variant reliability issues have garnered widespread attention, particularly due to their consideration of the cumulative effects of material degradation and the time dependency of stochastic process loads. Methods based on the outcrossing rate are typically employed for time‐variant reliability analysis. However, they fail to effectively capture the correlations among multiple failure modes. To address this limitation, this paper proposes a time‐variant reliability analysis method that accounts for the correlation of multiple failure modes by integrating copula and PHI2. The method first utilizes Copula to analyze the correlations among various failure modes, followed by applying the PHI2 method for the time‐dependent reliability analysis of each individual failure mode. Ultimately, case studies confirm that the proposed method proficiently evaluates the time‐variant reliability of systems with multiple failure modes, yielding a maximum relative error below 5% compared to MCS and significant performance enhancements.

Although an accurate reliability assessment is essential to build a resilient infrastructure, it usually requires time-consuming computation. To reduce the computational burden, machine learning-based surrogate models have been used extensively to predict the probability of failure for structural designs. Nevertheless, the surrogate model still needs to compute and assess a certain number of training samples to achieve sufficient prediction accuracy. This paper proposes a new surrogate method for reliability analysis called Adaptive Hyperball Kriging Reliability Analysis (AHKRA). The AHKRA method revolves around using a hyperball-based sampling region. The radius of the hyperball represents the precision of reliability analysis. It is iteratively adjusted based on the number of samples required to evaluate the probability of failure with a target coefficient of variation. AHKRA adopts samples in a hyperball instead of an n-sigma rule-based sampling region to avoid the curse of dimensionality. The application of AHKRA in ten mathematical and two practical cases verifies its accuracy, efficiency, and robustness as it outperforms previous Kriging-based methods.

Reliability analysis based on a novel density estimation method for structures with correlations

Klout, a famous App, could measure people's social network influence power. Klout score is measured according to the data from past 90 days and an individual who has high Klout score is thought as having high social influence power. Lots of businesses or organizations like to hire high Klout score people to help them to diffuse their brand images. However, Klout score cannot tell us who has high influence power in a specific short time period. For example, it is possible that some of the users might always have high influence power on Monday or on Monday morning. These time-dependent influential users probably have low Klout scores in average but have high influence power in some specific time periods. Businesses should not just know who are the high Klout score users but also they should identify who are the time-dependent influential users because all of them may have some sort of power to influence other users' buying decisions. In this study, a framework based on frequent pattern mining is proposed to find the time-dependent influential users. First of all, the framework will divide a predefined long time period into successive short time segments and then influential transactions that contain Facebook fans' influence power data will be defined in each time segment. From the frequent patterns, the proper time for time-dependent influence users to spread information can be found. A theoretical experiment is given to verify the effectiveness of the proposed framework.