Towards dynamic safety assurance for Industry 4.0

Javed, Muhammad Atif; Muram, Faiz Ul; Hansson, Hans; Punnekkat, Sasikumar; Thane, Henrik

doi:10.1016/j.sysarc.2020.101914

Cited by 35 publications

(17 citation statements)

References 11 publications

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“…The authors proposed a real-time fuzzy inference system for controlled groups. Javed et al [69] discussed the safety concerns of automation and digitalization. The research illustrates use cases to demonstrate the interaction between autonomous machines and hazardous materials during manufacturing operations.…”

Section: Supply Chain Disruptions Amid Covid-19 Pandemicmentioning

confidence: 99%

Sustainable Performance through Digital Supply Chains in Industry 4.0 Era: Amidst the Pandemic Experience

Joshi

Sharma

2022

Sustainability

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Amidst the COVID-19 pandemic disruption, industry 4.0 technologies (I4TEs) and digital supply chains (DSCs) are reinforcing businesses to gain economic stability and agility to enrich their sustainable performance (S.P.). Survey methods have been deployed based on the constructs obtained from the literature. Data collection through a survey resulted in 202 valid responses. Confirmatory factor analysis (CFA) confirms the constructs and the mediating effect of the DSCs through partial least squares structural equation modeling (PLS-SEM). The study is among the few studies that examine the I4TE impact on DSCs and S.P. The results show that industry 4.0 technologies enhance the sustainable performance of firms. Results also show a complete mediation of DSCs on the inter-relationship between I4TEs and S.P. Those DSCs with I4TE inclusion can transform an organization’s strategic decision-making. For the authors, this study is the first of its kind. Although some of the literature explored different aspects of the concept of industry 4.0 and digitalizing supply chains, studies have yet to specifically evaluate the potential impacts of digital supply chains on sustainable performance. The novelty of DSCs is their support of firms in improving their preparedness, agility, and transparency to strengthen their sustainable performance. These DSCs will provide agile, collaboration, responsiveness, end-to-end visibility, and resilient supply chains to diminish supply risk and enrich preparedness and responsiveness to recuperate quickly from uncertainty amidst the pandemic. The study will help managers re-designing their strategic planning, resulting in new cost reduction and resilience models for supply chains. The study calls for firms to employ multiple DSCs once they have set clear strategic priorities. The overall findings of the work fill the literature gaps of studies in the digitalization of supply chains.

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Section: Supply Chain Disruptions Amid Covid-19 Pandemicmentioning

confidence: 99%

Sustainable Performance through Digital Supply Chains in Industry 4.0 Era: Amidst the Pandemic Experience

Joshi

Sharma

2022

Sustainability

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“…Dynamic factories are designed to be flexible and easily reconfigured. This makes safety analysis, which is traditionally conducted prior to operation, a challenge since the state of the system is hard to predict in advance [4]. Control in dynamic factories is also often widely distributed, with limited centralised control.…”

Section: Safety Assurance Of Dynamic Factoriesmentioning

confidence: 99%

“…The German automation technology supplier 'PILZ' [6] places emphasis on the necessary modular certification of the individual factory devices (PILZ uses the term Safety 4.0 to indicate modular safety solutions). Most of the literature however focuses on dependability in general but without much focus on safety which is challenging in dynamic environments [4].…”

Section: Safety Assurance Of Dynamic Factoriesmentioning

confidence: 99%

Analysing Ultra-Wide Band Positioning for Geofencing in a Safety Assurance Context

Hodge¹,

Hawkins²,

Hilder³

et al. 2022

Preprint

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There is a desire to move towards more flexible and automated factories. To enable this, we need to assure the safety of these dynamic factories. This safety assurance must be achieved in a manner that does not unnecessarily constrain the systems and thus negate the benefits of flexibility and automation. We previously developed a modular safety assurance approach, using safety contracts, as a way to achieve this. In this case study we show how this approach can be applied to Autonomous Guided Vehicles (AGV) operating as part of a dynamic factory and why it is necessary. We empirically evaluate commercial, indoor fog/edge localisation technology to provide geofencing for hazardous areas in a laboratory. The experiments determine how factors such as AGV speeds, tag transmission timings, control software and AGV capabilities affect the ability of the AGV to stop outside the hazardous areas. We describe how this approach could be used to create a safety case for the AGV operation.

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“…Similarly, in [16], an initial safety case framework is proposed with discussions on specific challenges for ML, which is later implemented with more details in [15]. A recent work [35] also explicitly suggests the combination of HAZOP and FTA in safety cases for identifying/mitigating hazards and deriving safety requirements (and safety contracts) when studying Industry 4.0 systems. In [41], safety arguments that are being widely used for conventional systems-including conformance to standards, proven in use, field testing, simulation, and formal proofs-are recapped for autonomous systems with discussions on the potential pitfalls.…”

Section: Related Workmentioning

confidence: 99%

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

Dong¹,

Huang²,

Bharti³

et al. 2021

Preprint

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The increasing use of Machine Learning (ML) components embedded in autonomous systems -so-called Learning-Enabled Systems (LES) -has resulted in the pressing need to assure their functional safety. As for traditional functional safety, the emerging consensus within both, industry and academia, is to use assurance cases for this purpose. Typically assurance cases support claims of reliability in support of safety, and can be viewed as a structured way of organising arguments and evidence generated from safety analysis and reliability modelling activities. While such assurance activities are traditionally guided by consensus-based standards developed from vast engineering experience, LES pose new challenges in safety-critical application due to the characteristics and design of ML models. In this article, we first present an overall assurance framework for LES with an emphasis on quantitative aspects, e.g., breaking down system-level safety targets to component-level requirements and supporting claims stated in reliability metrics. We then introduce a novel model-agnostic Reliability Assessment Model (RAM) for ML classifiers that utilises the operational profile and robustness verification evidence. We discuss the model assumptions and the inherent challenges of assessing ML reliability uncovered by our RAM and propose practical solutions. Probabilistic safety arguments at the lower ML component-level are also developed based on the RAM. Finally, to evaluate and demonstrate our methods, we not only conduct experiments on synthetic/benchmark datasets but also demonstrate the scope of our methods with a comprehensive case study on Autonomous Underwater Vehicles in simulation.

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Towards dynamic safety assurance for Industry 4.0

Cited by 35 publications

References 11 publications

Sustainable Performance through Digital Supply Chains in Industry 4.0 Era: Amidst the Pandemic Experience

Sustainable Performance through Digital Supply Chains in Industry 4.0 Era: Amidst the Pandemic Experience

Analysing Ultra-Wide Band Positioning for Geofencing in a Safety Assurance Context

Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance

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