Cross-Level Compositional Reliability Analysis for Embedded Systems

At the LKAB Kiruna Iron Ore Mine in Sweden, reliability and maintainability analysis techniques have been gradually accepted as standard tools for planning and operation of automatic and complex mining systems since the mid-1980s. The reliability analysis and modeling of the production system at the Kiruna mine are discussed. Since a large number of random variables (phenomena) are involved in the analysis, a simulation program SIMURES was developed to model the reliability characteristics of the production process and analyze the criticality of the various units and operations. This program has helped in evaluating the gain in production volume with improvement in reliability of the various units, and thus forms a basis for future investment for increasing the production system efficiency and capability. >

무인기의 운용이 점차 보편화되면서 무인기의 기본 요구도에 많은 변화가 생기고 있다. 기존의 시스템은 기능 구현을 주목적으로 하였으나 현재의 무인기는 이를 넘어서 가용성에도 큰 무게를 둔다. 따라서 무인기 분야 선진 각국에서는 시스템의 신뢰도 분석 방법과 자료 수집에 많은 노력을 기울이고, 또 그 결과를 설계에 반영한다. 본 논문에서는 이러한 신뢰성 분석 기법을 소개하고 이를 기초로 현재 개발 중인 무인기 탑재시스템을 대상으로 신뢰성을 분석하여 그 결과를 제시함으로써 무인기 개발 및 운용 과정에서의 효율성 증대를 도모하였다. As usage of UAV becomes more common, a basic requirements on the system are changing. Existent system did function embodiment by major object, but current UAV puts bigger weight to availability. Therefore, all the advanced countries in UAV technologies put great efforts in reliability analysis techniques and source collection of system, and reflect the result in design. The authors are developing a flight control system for a UAV and using the reliability analysis techniques in the process. This paper introduces basic reliability analysis techniques and results of analysis for a small UAV flight control system that is developing present. The result plans efficiency enlargement UAV development and operation process.

The study and analysis of repairable systems is an important topic in reliability. Analytical techniques become very complicated and unrealistic especially for modern complex systems. There have been attempts in the literature to evolve more realistic techniques using simulation approach for reliability analysis of systems. The purpose of this paper is to develop a novel Markov system dynamics (MSD) simulation framework for the reliability modeling and analysis of a repairable system. This paper proposes a hybrid approach called as MSD approach which combines the Markov approach with system dynamics simulation approach for reliability modeling. This approach will have the advantages of both Markov as well as system dynamics methodologies. The proposed framework is illustrated for a repairable two component system. The results of the simulation obtained in this work when compared with that obtained by traditional Markov analysis clearly validate that this novel MSD approach is an alternative approach for reliability modeling and analysis.

The purpose of this study is to develop and evaluate a probabilistic framework for reliability analysis of information-theoretic computer-assisted detection (IT-CAD) systems in mammography. The study builds upon our previous work on a feature-based reliability analysis technique tailored to traditional CAD systems developed with a supervised learning scheme. The present study proposes a probabilistic framework to facilitate application of the reliability analysis technique for knowledge-based CAD systems that are not feature-based. The study was based on an information-theoretic CAD system developed for detection of masses in screening mammograms from the Digital Database for Screening Mammography (DDSM). The experimental results reveal that the query-specific reliability estimate provided by the proposed probabilistic framework is an accurate predictor of CAD performance for the query case. It can also be successfully applied as a base for stratification of CAD predictions into clinically meaningful reliability groups (i.e., HIGH, MEDIUM, and LOW). Based on a leave-one-out sampling scheme and ROC analysis, the study demonstrated that the diagnostic performance of the IT-CAD is significantly higher for cases with HIGH reliability (A(z) = 0.92 +/- 0.03) than for those stratified as MEDIUM (A(z) = 0.84 +/- 0.02) or LOW reliability predictions (A(z) = 0.78 +/- 0.02).

This paper proposes a new goal-oriented (GO) method for reliability analysis of repairable systems with multiple-input and multi-function component (MIMFC). First, a new GO operator combination, which is composed of a new function GO operator and a new auxiliary GO operator, is created to represent MIMFC. The new function GO operator named as Type 22 operator is created to represent MIMFC itself, and the auxiliary GO operator named as Type 15B operator is created to represent multi-condition control signals of MIMFC. Then, GO operation formulas of the new GO operator combination are deduced based on logical relationships among inputs, outputs, and the component itself. The reliability analysis process of the new GO method is formulated. Furthermore, this new GO method is applied for the first time in steady availability analysis and qualitative analysis of the fan drive system of a power-shift steering transmission. Finally, the results obtained by the new GO method are compared with the results of fault tree analysis (FTA) and Monte Carlo simulation (MCS), and the comparison results show that this new GO method is reasonable and advantageous in reliability analysis of repairable systems with MIMFC. Moreover, the analysis process shows that it is more advantageous in the aspect of building system models and conducting reliability analysis. Overall, this paper not only improves the basic theory of the GO method and expands the application of the GO method, but it also provides a new approach for reliability analysis of repairable systems with MIMFC.

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.

Traditional reliability analysis generally uses probability approach to quantify the uncertainty, while it needs a great amount of information to construct precise distributions of the uncertain parameters. In this paper, a new reliability analysis technique is developed based on a hybrid uncertain model, which can deal with problems with limited information. All uncertain parameters are treated as random variables, while some of their distribution parameters are not given precise values but variation intervals. Due to the existence of the interval parameters, a limit-state strip enclosed by two bounding hyper-surfaces will be resulted in the transformed normal space, instead of a single hyper-surface as we usually obtain in conventional reliability analysis. All the limit-state strips are then summarized into two different classes and corresponding reliability analysis models are proposed for them. A monotonicity analysis is carried out for probability transformations of the random variables, through which effects of the interval distribution parameters on the limit state can be well revealed. Based on the monotonicity analysis, two algorithms are then formulated to solve the proposed hybrid reliability models. Three numerical examples are investigated to demonstrate the effectiveness of the present method.

In recent years, spatial variability modeling of geotechnical parameters using random field theory is gaining strength in the reliability based analysis of geotechnical problems. Slope stability analysis of highly weathered rock slope requires proper modeling of geotechnical parameters with due consideration of extent of weathering and overburden pressure. The present study demonstrates the approach for spatial variation modeling of geotechnical parameters and reliability analysis based stability assessment of highly weathered rock slope by considering a typical case of height = 5.0 m and slope angle of 30°. Commercially available software FLAC 5.0, via inbuilt FISH function, has been utilized for the numerical analysis purpose with the assumption that highly weathered rock mass can be analyzed within the framework of the concept of equivalent continuum model. A parametric study is performed to investigate the following: (i) influence of coefficients of variation of geotechnical parameters and (ii) auto-correlation distances on the reliability analysis results. For the reliability analysis, information on mean and variance of output parameter, i.e., factor of safety of the given rock slope is obtained from 2000 Monte Carlo simulations and results are utilized with first order reliability method to obtain reliability index values. The results of the analysis clearly demonstrated that numerical modeling of spatially varying geotechnical parameters gives more realistic treatment to the property variation of a natural material and stability assessment in reliability analysis framework is more comprehensive than conventional limit equilibrium based factor of safety approach.

As an innovative form of digital online education, educational games have potentially significant impacts on preschool children's enlightenment education. Identifying key factors for effective design is crucial in game development. This study identifies key factors for the design of educational games aimed at preschool children's enlightenment through decision analysis methods. The specific research steps include using SET analysis method to identify gaps in development opportunities, employing the Analytic Hierarchy Process to construct an interactive system design model for enlightenment educational games, calculating the weight value of each factor, and pinpointing the key factors for design practice. Based on this methodology, an enlightenment educational game has been developed, and Fuzzy Comprehensive Evaluation method has been utilized to assess the practical implementation. The approach outlined in this study enables designers to swiftly recognize the critical elements in designing educational games for preschool children's enlightenment, facilitating a responsive design process, achieving effective game designs, and ultimately enhancing preschool children's learning interest and outcomes.

To effectively perform the reliability analysis of the flap deflection angle, the reliability analysis framework is developed by introducing fault logic and a data-driven model. Herein, the fault logic analysis is used to study the fault mechanism and filter out the characteristic fault parameters that can be used to collect input data for data-driven modelling; the data-driven modelling is employed to establish a reliability analysis model with a small amount of input data. Under this proposed framework, the improved dung beetle optimization algorithm for back propagation (IDBO-BP) method is developed to perform the reliability modelling of the flap deflection angle. To validate the effectiveness of the proposed framework, we study the fault logic of flap symmetry and establish a surrogate model of flap deflection based on the fault parameters and the IDBO-BP algorithm. According to the predicted results of the flap deflection angle, the reliability model based on the fault mechanism can reflect the actual flap motion. At the same time, the proposed IDBO-BP algorithm has excellent modelling and simulation property by comparing with other optimization algorithms. Thus, the efforts of this study provide a new solution to the problem of reliable analysis with uncertain fault parameters. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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.

Along with the development of object-oriented technology, component development is gradually extended to commercial off-the-shelf (COTS) components and component-based software development (CBSD) has gradually become the dominant orientation of the field of software engineering. The claims of high reliability need further investigation based on reliability analysis techniques that are applicable to component-based applications. This paper introduces a new reliability analysis technique applicable to high-level designs. The technique is named reliability analysis based on rewrite logic (RABRL). RABRL is specific for component-based software whose analysis is strictly based on its operational profile and specification. Specification based on rewrite logic can be executed with the help of rewrite technique of Maude, driven by test cases designed from the operational profile. By analyzing the execution process with statistics, we can get the transition probability between components and expected number of visits to components approximately. The paper also adapts the probabilistic model "component-dependency graph" (CDG) proposed by Sherif M. Yacoub to model the component-based software, which is named "adapted component-dependency graph" (ACDG). ACDGs are directed graphs that represent components, component reliabilities, transitions, and transition probabilities. Based on ACDGs, an algorithm is presented to analyze the reliability of the application as the function of reliabilities of its components. A simple case study illustrates the applicability of the algorithm. The RABRL is also used to identify critical components.

More and more embedded systems provide a multitude of services, implemented by a large number of networked hardware components. In early design phases, dimensioning such complex systems in terms of monetary costs, power consumption, reliability etc. demands for new analysis approaches at the electronic system level. In this paper, two symbolic system level reliability analysis approaches are introduced. First, a formal approach based on Binary Decision Diagrams is presented that allows to calculate exact reliability measures for small to moderate-sized systems. Second, a simulative approach is presented that hybridizes a Monte Carlo simulation with a SAT solver and delivers adequate approximations of the reliability measures for large and complex systems.

Compared with the probability model, the convex model approach only requires the bound information on the uncertainty, and can make it possible to conduct the reliability analysis for many complex engineering problems with limited samples. Presently, by introducing the well-established techniques in probability-based reliability analysis, some methods have been successfully developed for convex model reliability. This paper aims to reveal some different phenomena and furthermore some severe paradoxes when extending the widely used first-order reliability method (FORM) into the convex model problems, and whereby provide some useful suggestions and guidelines for convex-model-based reliability analysis. Two FORM-type approximations, namely, the mean-value method and the design-point method, are formulated to efficiently compute the nonprobabilistic reliability index. A comparison is then conducted between these two methods, and some important phenomena different from the traditional FORMs are summarized. The nonprobabilistic reliability index is also extended to treat the system reliability, and some unexpected paradoxes are found through two numerical examples.

A reliability analysis framework coupled with statistical uncertainty characterization for geotechnical engineering