A decision support model for the composite repair process in a collaborative platform

Thyagarajan, Sujatha; Gollnick, Volker

doi:10.1784/insi.2018.60.9.488

Cited by 1 publication

(3 citation statements)

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“…Bole, Goebel, and Vachtsevanos considered a system whose maintenance costs increased proportionally to its RUL and found an optimal maintenance policy through a Markov decision process and dynamic programming (2015). In two further publications, a level of repair analysis for the optimal maintenance action of an aircraft composite was presented (Thyagarajan & Gollnick, 2017. The considered actions were 'repair', 'replace' or 'discard'.…”

Section: Processesmentioning

confidence: 99%

“…Table 6. Publication-Concept Matrix Haddad et al, 2011bHaddad et al, 2011aAghdam & Liao, 2012Ning et al, 2013Herr et al, 2014Aghdam & Liao, 2014Wang et al, 2015Niknam et al, 2015Chrétien et al, 2015Bole et al, 2015Terrissa et al, 2016Meraghni et al, 2016Li et al, 2016Chrétien et al, 2016Thyagarajan & Gollnick, 2017Thyagarajan & Gollnick, 2018Meraghni et al, 2018Bougacha et al, 2018Skima et al, 2019 Reference…”

Section: Appendixmentioning

confidence: 99%

“…Herr et al determine the subset of running systems and their running profiles and factor in how much degradation is caused per each time period (2014). Multiple authors used Markov decision processes that use probabilities to transit from one stage to another(Bole et al, 2015;Thyagarajan & Gollnick, 2017 or supposed transition probabilities outside of Markovian decision processes(Haddad et al, 2011a). Ladj, Tayeb et al expressed the RUL as a fuzzy value (2017).Haddad et al used Monte Carlo simulations (2011b) to incorporate uncertainty.…”

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confidence: 99%

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Process-based Review of Post-Prognostics Decision-Making

Wesendrup¹,

Hellingrath²

2020

PHME_CONF

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Condition-based maintenance (CBM) and prognostics and health management (PHM) are established paradigms that evidently offer a competitive advantage to a company. However, to make a business case, it must be examined where PHM and a remaining useful life (RUL) estimation can lead to substantial benefits. These benefits are strongly tied to the decision-making that succeeds prognostics. While the prognostics component of PHM is well examined, research on post-prognostics decision-making (PDM) is still in its infancy. It is generally assumed that PHM can lead to benefits for business processes beyond 'traditional' maintenance management. Unfortunately, there is no overview for which processes (such as production scheduling or route planning) PDM can be applicable and how exactly specific optimizations and their corresponding benefits can be achieved. This work provides a structured literature review on PDM and identifies studies that exploit the RUL prediction for optimizing business processes. The review synthesizes the following aspects within a PDM framework: a) which processes are improved through post-prognostics decision-making, b) what decisions must be made, and c) what novel benefits are achieved and which challenges arise. This review enables scholars to identify how current prognostics research can be extended to the decision stage of CBM and PHM and aids practitioners in pinpointing how operations can be optimized through PDM.

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Section: Processesmentioning

confidence: 99%