An approach for lifetime reliability analysis using theorem proving

Abbasi, Naeem; Hasan, Osman; Tahar, Sofiène

doi:10.1016/j.jcss.2013.05.002

Cited by 11 publications

(13 citation statements)

References 14 publications

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“…The approach relies upon specifying the behaviors of analog components (such as transistors) by conservative approximation techniques based on piecewise-linear predicates on voltages and currents. Theorem proving was initially used to check for the implication relation between the implementation and the specification [19] [20]. In order to automate the verification process, the author proposed afterwards using constraint based techniques instead [21].…”

Section: Related Work Of Proof Based and Symbolic Methodsmentioning

confidence: 99%

Research Trends in Formal Verification Process for Analog and Mixed Signal Design

S¹,

Manjunath²

2015

IJCA

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Formal verification is one of the crucial stages of design phase that ensure the preciseness of the circuit design and its corresponding behavior with respect to specific system design. From last decade, there has been an abundant formal verification techniques introduced by the research community for analog and mixed signal circuits that used as an interface between analog and digital components. Owing to the maximized sophistication, and shrinking sizes of chip, analog, and mixed signal verification is encountering challenges in increasing verification requirement that calls for analyzing the prior techniques. The prime purpose of this paper is to discuss the most standard models, and techniques introduced till date and to excavate the various facts about their effectiveness in the area circuit design principles. The paper also discusses some of the critical research gaps explored from the study.

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Section: Related Work Of Proof Based and Symbolic Methodsmentioning

confidence: 99%

Research Trends in Formal Verification Process for Analog and Mixed Signal Design

S¹,

Manjunath²

2015

IJCA

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“…A number of higher-order-logic formalizations of probability theory are available in higher-order logic (e.g., [72,73,74]) and have been utilized to verify sampling algorithms of a number of commonly used discrete [72] and continuous random variables [75] based on their probabilistic and statistical properties [76,77]. Moreover, this formalization has been used to conduct the reliability analysis of a number of applications, such as memory arrays [78], soft errors [79], electronic components [80] and oil and gas pipelines [81].…”

Section: Higher-order-logic Theorem Provingmentioning

confidence: 99%

Reliability modeling and analysis of communication networks

Ahmed

Hasan

Pervez

et al. 2017

Journal of Network and Computer Applications

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In recent times, the functioning of various aspects of modern society-ranging from the various infrastructural utilities such as electrical power, water to socio-economical aspects such as telecommunications, business, commerce, education-has become critically reliant on communication networks, and particularly on the Internet. With the migration of critical facilities to the Internet, it has become vitally important to ensure the reliability and availability of networks. In this paper, we study various modeling and analysis techniques that can aid in the study of reliability of communication networks. In this regard, we provide background on the modeling techniques (such as reliability block diagrams, fault trees, Markov chains, etc.) and analysis techniques (such as mathematical analytical methods, simulation methods, and formal methods). Apart from providing the necessary background, we also critically evaluate the pros and cons of different approaches, and provide a detailed survey of their applications in communication networks. To the best of our knowledge, this is the first in-depth review of the application of reliability modeling and analysis techniques in communication networks. work, the ability of a network to maintain an acceptable reliability level under extreme environmental conditions and assess the impact of design changes on the reliability of the overall network [17]. Similarly, the availability prediction allows us to evaluate various maintenance design options to achieve the desired availability of the network [18].The qualitative and quantitative reliability analysis requires the selection of an appropriate mathematical modeling and analysis technique. The modeling technique must be able to effectively capture the important parameters of the real system and the analysis technique should be capable of providing insights into the system behavior without running (or executing) the real system. There are numerous techniques that can provide analysis in the early phase, when only initial details are available of the design, and there are other techniques that cater the analysis of the later design phases, when more precise implementation details are available [16]. Some of the most widely used formalisms/techniques for modeling reliability and availability of communication networks are: Reliability Block Diagram (RBD) [19], Fault Tree (FT) [20], and Markov Chain (MC) [21]. Traditionally, the models developed using these techniques are analyzed using analytical methods or simulation tools.Recently, the computer networks community has shown great interest in the utilization of formal methods [22] as a reliability analysis technique for communication networks. Formal methods use mathematical logic to precisely model the system's intended behavior and deploy mathematical reasoning to construct an irrefutable proof that the given system satisfies its requirements. This kind of mathematical modeling and analysis makes formal methods an accurate and rigorous analysis method compared to the traditional a...

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“…This paper is designed to show the test of Reliability on the "Model for reducing weight and load on Production server database" [5] using 3P-Weibull distribution method. We compute Probability of Failure, BX% Life, Mean Life MTTF Impact Factor ISRA (India) = 1.344 Impact Factor ISI (Dubai, UAE) = 0.829 based on International Citation Report (ICR) Impact Factor GIF (Australia) = 0.356 Impact Factor JIF = 1.500 Impact Factor SIS (USA) = 0.912 Impact Factor РИНЦ (Russia) = 0.179 Impact Factor ESJI (KZ) = 1.042 ISPC Education & Innovation, Avignon, France 62 [2] and Failure Rate by using Weibull++ software's 3P-Weibull distribution method for the reliability.…”

Section: Objectivementioning

confidence: 99%

“…Average time, the units in the collected data are expected to operate before failure. It is often referred to as mean time to failure (MTTF) or mean time between failures (MTBF) [2]. As it is made known in Table 1, that we have estimate MTTF =735.254298, on the "model for reducing weight and load on production server database" [5].…”

Section: Mean Life Mttfmentioning

confidence: 99%

“…The reliability of any system can be calculated as the probability to perform its purpose for a certain period of time in the specified environment [1,2]. Computation of reliability with simulation automated tools is popular, computation tools such as Weibull++ [3], which supports large base size and helps in controlling errors in computations.…”

Section: Introductionmentioning

confidence: 99%

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