PHM based predictive maintenance optimization for offshore wind farms

Lei, Xin; Sandborn, Peter; Bakhshi, Roozbeh; Kashani-Pour, Amir; Goudarzi, Navid

doi:10.1109/icphm.2015.7245027

Cited by 19 publications

(9 citation statements)

References 6 publications

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“…The main aim is to alert the user in time to plan optimally the necessary maintenance actions. The maintenance optimization using prognostics information has been used in several applications, such as electronic systems [3,34], aerospace [2,11] and wind turbines [5,27].…”

Section: Maintenance Optimizationmentioning

confidence: 99%

Post-prognostics decision making in distributed MEMS-based systems

et al. 2017

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In this paper, the problem of using prognostics information of Micro-Electro-Mechanical Systems (MEMS) for post-prognostics decision in distributed MEMS-based systems is addressed. A strategy of postprognostics decision is proposed and then implemented in a distributed MEMS-based conveying surface. The surface is designed to convey fragile and tiny microobjects. The purpose is to use the prognostics results of the used MEMS in the form of Remaining Useful Life (RUL) to maintain as long as possible a good performance of the conveying surface. For that, a distributed algorithm for distributed decision making in dynamic conditions is proposed. In addition, a simulator to simulate the decision in the targeted system is developed. Simulation results show the importance of the postprognostics decision to optimize the utilization of the system and improve its performance.

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Section: Maintenance Optimizationmentioning

confidence: 99%

Post-prognostics decision making in distributed MEMS-based systems

et al. 2017

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“…(19), predictive maintenance option values are obtained. At each predictive maintenance opportunity, all option values are averaged to get the expected predictive maintenance option 2 A stochastic DCF approach was applied to a similar example (Lei, Sandborn, Goudarzi, and Bruck, 2015) that assumes that the predictive maintenance will always be implemented at some selected opportunity rather than treated as an option. Alternatively, the European ROA approach is an asymmetric approach that captures the upside value (when predictive values as shown in Figure 6, together with the histogram of ARULC.…”

Section: Results From European Roa Approachmentioning

confidence: 99%

International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008 PHM-Based Wind Turbine Maintenance Optimization Using Real Options

Lei

Sandborn

2020

IJPHM

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A simulation-based real options analysis (ROA) approach is used to determine the optimum predictive maintenance opportunity for a wind turbine with a remaining useful life (RUL) prediction. When an RUL is predicted for a subsystem in a single turbine using PHM, a predictive maintenance option is triggered that the decision-maker has the flexibility to decide if and when to exercise before the subsystem or turbine fails. The predictive maintenance value paths are simulated by considering the uncertainties in the RUL prediction and wind speed (that govern the turbine’s revenue earning potential). By valuating a series of European options expiring on all possible predictive maintenance opportunities, a series of option values can be obtained, and the optimum predictive maintenance opportunity can be determined. A case study is presented in which the ROA approach is applied to a single turbine.

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“…First, the numeric simulation is an evaluation based merely on simulated date, e.g. a Monte Carlo Simulation which generates data about hypothetical failures which must then be identified by the researcher's predictive maintenance approach [50], [51]. More than a third (37%) of the papers apply a simulation-based evaluation method to test their predictive maintenance techniques.…”

Section: Discussionmentioning

confidence: 99%

A Survey on Predictive Maintenance for Industry 4.0

Krupitzer,

Wagenhals,

Züfle

et al. 2020

Preprint

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Production issues at Volkswagen in 2016 lead to dramatical losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present a survey on the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literate survey, we present a classification of predictive maintenance in the context of Industry 4.0 and discuss recent developments in this area.

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PHM based predictive maintenance optimization for offshore wind farms

Cited by 19 publications

References 6 publications

Post-prognostics decision making in distributed MEMS-based systems

Post-prognostics decision making in distributed MEMS-based systems

International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008 PHM-Based Wind Turbine Maintenance Optimization Using Real Options

A Survey on Predictive Maintenance for Industry 4.0

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