Probabilistic model-checking based reliability analysis for failure correlation of multi-state systems

Wang, Rongxi; Tang, Ze-Zhou; Gao, Jianmin; Gao, Zhiyong; Wang, Zhen

doi:10.1080/08982112.2019.1692139

Cited by 7 publications

(3 citation statements)

References 57 publications

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“…Continuous-time Markov decision processes are used for the probabilistic verification of models that allow analyzing the reliability of failures in a multistate system, obtaining flexible and effective results regarding failure times [42]. An example of this is to use the continuous-time Markov chain to describe the degradation process of Traction Power Supply Equipment (TPSE).…”

Section: Consultation and Analysis Of The Literaturementioning

confidence: 99%

Optimization tools applied to physical asset maintenance management: state of the art

Pentón

Morales

Llanes

et al. 2021

DYNA

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This article presents the state of the art of the application of optimization tools such as Genetic Algorithms, Simulation, Neural Networks, Markov Chains and Bayesian Networks in the physical asset maintenance management. The bibliographic references used were extracted from a detailed search that allowed the selection of the empirical studies presented, in the time horizon from 2010 to 2021, through databases, research platforms and online libraries. The analysis of the identified case studies is carried out, taking into account the variables involved in the study, the optimization tool used, and the result obtained in the analysis of the physical asset maintenance management. The benefits of the application of optimization tools are identified and it is confirmed that maintenance costs and intervention times are present variables, which contribute to the improvement of reliability and maintenance management.

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Section: Consultation and Analysis Of The Literaturementioning

confidence: 99%

Optimization tools applied to physical asset maintenance management: state of the art

Pentón

Morales

Llanes

et al. 2021

DYNA

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“…Shao used probabilistic model checking to assess the reliability of phased-mission systems (PMSs) considering the influence of common cause failures (CCFs) [33]. Wang used probabilistic model checking to analyze the reliability of multistate systems and chose wind turbines as examples [34]. Silva proposed an approach that uses COI analysis to identify functionally safe faults [35].…”

Section: Related Workmentioning

confidence: 99%

Using Formal Methods to Evaluate Hardware Reliability in the Presence of Soft Errors

Xue

Zwoliński

2022

2022 17th Conference on Ph.D Research in Microelectronics and Electronics (PRIME)

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Reliability has been a major concern in embedded systems. Higher transistor density and lower voltage supply increase the vulnerability of embedded systems to soft errors. A Single Event Upset (SEU), which is also called a soft error, can reverse a bit in a sequential element, resulting in a system failure. Simulation-based fault injection has been widely used to evaluate reliability, as suggested by ISO26262. However, it is practically impossible to test all faults for a complex design. Random fault injection is a compromise that reduces accuracy and fault coverage. Formal verification is an alternative approach. In this paper, we use formal verification, in the form of model checking, to evaluate the hardware reliability of a RISC-V Ibex Core in the presence of soft errors. Backward tracing is performed to identify and categorize faults according to their effects (no effect, Silent Data Corruption, crashes, and hangs). By using formal verification, the entire state space and fault list can be exhaustively explored. It is found that misaligned instructions can amplify fault effects. It is also found that some bits are more vulnerable to SEUs than others. In general, most of the bits in the Ibex Core are vulnerable to Silent Data Corruption, and the second pipeline stage is more vulnerable to Silent Data Corruption than the first.

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“…Through the analysis of the formal model under different scenarios and property specifications, model checking aids in identifying trade-offs, potential bottlenecks, or performance concerns, enabling the optimization of the system's design to enhance correctness and reliability [6]. However, conventional model-checking approaches face challenges in handling the inherent dynamism of blockchain systems that can arise from runtime changes or developing requirements [7].…”

Section: Introductionmentioning

confidence: 99%

BlockASP: A Framework for AOP-Based Model Checking Blockchain System

AlSobeh,

Magableh

2023

IEEE Access

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Blockchain systems are lauded for their security and reliability. Security is a cornerstone, as they employ cryptographic techniques to ensure the immutability of data, making it extremely resistant to tampering. With decentralized networks, they also reduce the risk of a single point of failure, enhancing reliability. Model checking plays a vital role in ensuring the security and reliability of blockchain systems. However, traditional model-checking approaches face challenges in handling the inherent dynamism exhibited in blockchain systems. To overcome this challenge, Aspect-Oriented programming (AOP) offers capabilities to enhance blockchain model checking through the modularization of cross-cutting concerns, enabling traceability and monitoring, facilitating dynamic instrumentation, and supporting fine-grained property specifications. The aim of this research is to enable more effective and efficient verification of dynamic behaviors in blockchain systems compared to conventional model-checking techniques using AOP. As a result, this research introduces BlockASP, a novel blockchain model verification method that leverages AOP to analyze and monitor dynamic behavior of the blockchain system. BlockASP integrates the benefits of aspect-orientation and model checking into the blockchain architecture to strengthen security and reliability. This research has examined prior arts that are related to blockchain modeling using Objectoriented (OO) and those that are using AOP. Our research has proposed and discussed the BlockASP technique, the research provided a case study to demonstrate the validity and superiority in facilitating the monitoring of dynamic blockchain behavior using AOP compared to traditional approaches such as Model-Driven Architecture (MDA).

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Probabilistic model-checking based reliability analysis for failure correlation of multi-state systems

Cited by 7 publications

References 57 publications

Optimization tools applied to physical asset maintenance management: state of the art

Optimization tools applied to physical asset maintenance management: state of the art

Using Formal Methods to Evaluate Hardware Reliability in the Presence of Soft Errors

BlockASP: A Framework for AOP-Based Model Checking Blockchain System

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