Big Data Analytics in the Maintenance of Off-Shore Wind Turbines: A Study on Data Characteristics

Nabati, Elaheh Gholamzadeh; Thoben, Klaus‐Dieter

doi:10.1007/978-3-319-45117-6_12

Cited by 8 publications

(7 citation statements)

References 11 publications

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“…A proper CMS can be used to detect more faults. Early detection of incipient faults prevents major component failures and allows predictive strategies to be carried out [18][19][20]. The capability of a CMS depends on the number and type of sensors, and signal processing [21][22][23][24][25].…”

Section: Introductionsupporting

confidence: 40%

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

et al. 2017

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Electrical and electronic components are very important subcomponents in modern industrial wind turbines. Complex multimegawatt wind turbines are continuously being installed both onshore and offshore, continuously increasing the demand for sophisticated electronic and electrical components. In this work, most critical electrical and electronic components in industrial wind turbines have been identified and the applicability of appropriate condition monitoring processes simulated. A fault tree dynamic analysis has been carried out by binary decision diagrams to obtain the system failure probability over time and using different time increments to evaluate the system. This analysis allows critical electrical and electronic components of the converters to be identified in different conditions. The results can be used to develop a scheduled maintenance that improves the decision making and reduces the maintenance costs.

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Section: Introductionsupporting

confidence: 40%

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

et al. 2017

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“…One example from the research community is the use of machine-learning algorithms applied to turbine power performance data, which can be used as an alternative means of dealing with the noisy data from which power curves are derived (Clifton et al 2013). Other examples in the research community focusing on O&M include condition monitoring and predictive maintenance (Hameed et al 2009;Garcia Marquez et al 2012;Nabati and Thoben 2016;Canizo et al 2017). The recognition of the need for data-driven research has even led to large-scale research programs in the European Union including such projects as the ROMEO project (https://www.romeoproject.eu/machine-learning-iot-improve-wind-farms/) and the VIS-Project Offshore Wind Operations & Maintenance Excellence (OWOME) project (http://www.owi-lab.be/content/vis-project-owome-offshore-wind-operations-maintenanceexcellence).…”

Section: Objectives Of Digitalizationmentioning

confidence: 99%

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

Dykes

Veers

Lantz

et al. 2019

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Research advanced erosion-resistant composite materials for use on the leading edge of wind turbine blades Research, development, and scale-up of thermoplastic resin systems to reduce wind turbine blade manufacturing cycle times, reduce blade costs, enable thermal welding of bond lines, increase recyclability, and enable advanced in-field blade repair techniques Research advanced thermoset materials that can be recovered and reused Research metals and other noncomposite materials for development of lightweight, high-strength, and high-stiffness turbine components Grand Challenge 2: Automation Develop comprehensive techno-economic models for automation and a virtual factory (or digital twin of a factory) to understand advantages and disadvantages of targeted automation for both wind turbine blade molding and finishing operations Identify and quantify all cost inputs for wind turbine component manufacturing (e.g., blades, towers) to identify production steps that would benefit from automation Develop automated production methods for the continuous manufacturing of one-piece wind turbine towers for on-site manufacturing Develop advanced design tools to optimize blade and tower design with respect to manufacturing with automation Research and develop automation technology able to locate complex wind turbine blade geometry in space to allow for effective automated finishing operations, such as sanding, drilling, and cutting Develop methods to automate the delivery, placement, and inspection of core material into blade skin molds during the composite laminate skin lay-up Integrate real-time inspection and quality control into automated production steps for wind turbine blades, towers, and other components Develop large-scale, low-cost, high-throughput, high-performance automated additive manufacturing technologies (e.g., 3D printing, automated fiber placement and tape layup, filament winding) for the production of towers, blade skins, blade spars, and other turbine components Develop embedded metrology and out-of-mold indexing technologies Grand Challenge 3: On-Site Manufacturing Develop robust composite materials for use in broad on-site environmental conditions Research the chemistry and processing of in-situ thermoplastic resin systems to enable thermally welded joints in an on-site manufacturing environment Develop targeted wind turbine blade finishing automation techniques designed to reduce the overall footprint of on-site finishing operations and minimize production facility floor space Research additive manufacturing to print wind turbine blade molds and tooling at on-site manufacturing locations IEA Wind TCP Task 11

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“…In the last years, Big Data analytic approaches are becoming more popular in a wide variety of industries and purposes such for medicine [20][21], social networks recommendations [22], logistic [23], structural health monitoring [24] [25], business strategy development [26] or power consumption in manufacturing [27]. For the wind energy industry, some studies have been also identified where a business intelligence approach is presented for failure prognosis [28] or the requirements for a Big Data approach are analyzed [29].…”

Section: State Of Artmentioning

confidence: 99%

Real-time predictive maintenance for wind turbines using Big Data frameworks

Canizo

Onieva

Conde

et al. 2017

2017 IEEE International Conference on Prognostics and Health Management (ICPHM)

144

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This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs.

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Big Data Analytics in the Maintenance of Off-Shore Wind Turbines: A Study on Data Characteristics

Cited by 8 publications

References 11 publications

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

Real-time predictive maintenance for wind turbines using Big Data frameworks

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