A hierarchical verification approach to verify complex safety control systems based on STAMP

Han, Xiaotao; Tang, Tao; Lv, Jidong

doi:10.1016/j.scico.2018.11.006

Cited by 10 publications

(2 citation statements)

References 8 publications

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“…FRAM and STAMP are in essence qualitative safety analysis approaches, although in the case of FRAM some quantitative extensions (Patriarca et al, 2017; Patriarca, Falegnami, et al, 2018), including the application of Fuzzy Logic (Hirose & Sawaragi, 2020; Slim & Nadeau, 2020), have been described. STAMP has been extended with system dynamics (Bugalia et al, 2020; Kontogiannis & Malakis, 2012) and model checking tools (Han et al, 2019; Yang et al, 2019). EAST already has a quantitative element built‐in to its framework in the form of network metrics.…”

Section: About Systemic Safety Analysismentioning

confidence: 99%

Teaming with industrial cobots: A socio‐technical perspective on safety analysis

Adriaensen

Costantino

Gravio

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Collaborative human–machine interaction will be progressively intensified in industrial applications. The aim of this article is to examine current approaches to cobot safety by showing that these approaches can additionally benefit from systems thinking methods. The first part of this article covers a narrative literature review on predominantly techno‐centric robot safety approaches, with a strong focus on containing kinetic energy and ensuring separation with humans. The second part introduces systems thinking methods to analyze a socio‐technical perspective on cobot safety, including joint cognitive systems and distributed cognition perspectives. This explorative research dimension is expected to overcome an overly narrow interpretation of safety issues, anticipating the challenges ahead in ever more complex cobot applications. This article embraces a socio‐technical perspective to explore the potential of Joint Cognitive Systems to manage risk and safety in cobot applications. Three systemic safety analysis approaches are presented and tested with a demonstrator case study concerning their feasibility for cobot applications: System‐Theoretic Accident Model and Processes (STAMP); Functional Resonance Analysis Method (FRAM); and Event Analysis of Systemic Teamwork (EAST). These methods each provide interesting extensions to complement the traditional understanding of risk as required by current and future industrial cobot implementations. The power of systemic methods for safer and more efficient cobot operations lies in revealing the distributed and emergent result from joint actions and overcoming the reductionist view from individual failures or single agent responsibilities. The safe operation of cobot applications can only be achieved through alignment of design, training, and operation of such applications.

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Section: About Systemic Safety Analysismentioning

confidence: 99%

Teaming with industrial cobots: A socio‐technical perspective on safety analysis

Adriaensen

Costantino

Gravio

et al. 2021

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

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“…The goal of horizontal decomposition is to break complex systems' critical-factor identification problem down into numerous simple problems. The goal of vertical decomposition is to break the complicated system down into multiple-level subsystems which follow a predetermined rule [23,80,81].…”

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confidence: 99%

Critical Success Factors Evaluation for Blockchain’s Adoption and Implementing

Grida

Elrahman

Eldrandaly

2022

Systems

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Blockchain has completely changed how business is performed today, thus making it one of the most disruptive technologies in recent times. However, it is a challenging task to adopt and implement blockchain technologies in different services and industries. Therefore, this study introduces a framework for investigating critical factors influencing the successful adoption of blockchain technologies in different applications and prioritizes them using the hierarchical Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique. First, it provides fourteen critical success factors with the help of the extant literature and further classifies them into three categories: technological, organizational, and environmental. In addition, a set of sixteen key performance indicators (KPI) of successful blockchain adoption is introduced and classified into five categories: overall performance, system robustness, data robustness, accessibility, and overall cost. Then, the fourteen success factors are ranked based on their degree of prominence and relationships. It is concluded that environmental factors are the most critical factors for successful blockchain adoption, and law and policies and competitive pressure are the top two factors needed for blockchain adoption. In the technological context, only blockchain scalability is ranked among the top significant factors for blockchain adoption. On the other hand, adequate resources, top management support, and financial constraints are highly ranked in the organizational context.

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