Design for Zero-Maintenance

Farnsworth, Michael; McWilliam, Richard; Khan, Samir; Bell, Colin; Tiwari, Ashutosh

doi:10.1007/978-3-319-49938-3_21

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…Integrating the two methods exploits their respective benefits; neural networks enable nonlinear estimation and GA methods bring increased robustness. The results indicated improved fault detection and reduced false alarms 13 . More recently, deep learning methods have been introduced to look at fault diagnosis and learn the deep architectures of fault data [21].…”

Section: Requirements For System Health Managementmentioning

confidence: 94%

“…• Operating environment is an afterthought [13]. As a result, system capabilities (e.g., physical connections or data transfer) to interact with the environment can become limited;…”

Section: The Role Of Autonomous Maintenancementioning

confidence: 99%

“…To investigate this technological gap, the authors concentrate on self-learning technologies that are emerging within maintenance systems that exhibit a degree of autonomy. The transition to autonomous systems should bring with it the potential to approach zero-maintenance-a variety of strategies that ultimately provide the elimination of maintenance-centric costs-and its relation to through-life engineering services for a range of high-value products and assets [2,13]. Within this paradigm, the overarching focus remains to carry out maintenance but limiting (or even removing) human intervention.…”

Section: Introductionmentioning

confidence: 99%

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On the requirements of digital twin-driven autonomous maintenance

Khan

Farnsworth

McWilliam

et al. 2020

Annual Reviews in Control

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Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence.Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering.With recent developments in Industry 4.0, machine learning and digital twin , there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level.However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process.In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored.A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technology.Click here to view linked References

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Section: Requirements For System Health Managementmentioning

confidence: 94%

“…• Operating environment is an afterthought [13]. As a result, system capabilities (e.g., physical connections or data transfer) to interact with the environment can become limited;…”

Section: The Role Of Autonomous Maintenancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the requirements of digital twin-driven autonomous maintenance

Khan

Farnsworth

McWilliam

et al. 2020

Annual Reviews in Control

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“…However, within the zero-maintenance paradigm the benefits of graceful and predictable degradation and self management of faults are extremely attractive. At the sub-system level, online fault discrimination and monitoring operates at the modular level and within maintenance-heavy products, such as land, air and space vehicles, it becomes possible to monitor component aging via key response factors that that are expected to degrade more progressively over time [2]. These methods focus on self-correction of stuckat faults within nanoscale logic units, for which there are two reasons to consider fine-grained redundancy: firstly, fabrication processes are more prone to defect and variability [63] and the high density of nanoscale manufacturing exacerbates the challenge of high volume production.…”

Section: A Passive Methodsmentioning

confidence: 99%

“…An ideal selfengineering system should be capable of repairing itself in-situ without the need for human decision (or intervention). This will have a significant impact on reducing the overall cost of the maintenance process [2]. However, the application of this philosophy within engineering is a challenge and the authors aim to draw attention to the need for maintenance systems to have self-diagnosis and self-repair capabilities, built-in logic for self-reconfiguration and a cost weighted solution.…”

Section: Towards Zero-maintenancementioning

confidence: 99%

Zero-maintenance of electronic systems: Perspectives, challenges, and opportunities

McWilliam

Khan

Farnsworth

et al. 2018

Microelectronics Reliability

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Self-engineering systems that are capable of repairing themselves in-situ without the need for human decision (or intervention) could be used to achieve zero-maintenance. This philosophy is synonymous to the way in which the human body heals and repairs itself up to a point. This article synthesises issues related to an emerging area of self-healing technologies that links software and hardware mitigations strategies. Efforts are concentrated on built-in detection, masking and active mitigation that comprises self-recovery or self-repair capability, and has a focus on system resilience and recovering from fault events. Design techniques are critically reviewed to clarify the role of fault coverage, resource allocation and fault awareness, set in the context of existing and emerging printable/nanoscale manufacturing processes. The analysis presents new opportunities to form a view on the research required for a successful integration of zero-maintenance. Finally, the potential cost benefits and future trends are enumerated.

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State of the art in Through-life Engineering Services

Redding

Tjahjono

2018

Computers in Industry

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Design for Zero-Maintenance

Cited by 3 publications

References 20 publications

On the requirements of digital twin-driven autonomous maintenance

On the requirements of digital twin-driven autonomous maintenance

Zero-maintenance of electronic systems: Perspectives, challenges, and opportunities

State of the art in Through-life Engineering Services

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