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Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing
Article

A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History

1
Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain
2
Knowledge Engineering and Machine Learning Group at Intelligent Data Science and Artificial Intelligence Research Center (KEMLG-at-IDEAI), Polytechnic University of Catalonia, 08034 Barcelona, Catalonia, Spain
3
Smartive-ITESTIT SL, 08225 Terrassa, Catalonia, Spain
*
Authors to whom correspondence should be addressed.
Energies 2019, 12(10), 1982; https://doi.org/10.3390/en12101982
Received: 2 April 2019 / Revised: 7 May 2019 / Accepted: 16 May 2019 / Published: 23 May 2019
(This article belongs to the Special Issue Design, Fabrication and Performance of Wind Turbines 2019)
Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines. View Full-Text
Keywords: wind turbine; service history; classification; fault diagnosis; renewable energy; text mining wind turbine; service history; classification; fault diagnosis; renewable energy; text mining
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MDPI and ACS Style

Blanco-M., A.; Marti-Puig, P.; Gibert, K.; Cusidó, J.; Solé-Casals, J. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies 2019, 12, 1982. https://doi.org/10.3390/en12101982

AMA Style

Blanco-M. A, Marti-Puig P, Gibert K, Cusidó J, Solé-Casals J. A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History. Energies. 2019; 12(10):1982. https://doi.org/10.3390/en12101982

Chicago/Turabian Style

Blanco-M., Alejandro, Pere Marti-Puig, Karina Gibert, Jordi Cusidó, and Jordi Solé-Casals. 2019. "A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History" Energies 12, no. 10: 1982. https://doi.org/10.3390/en12101982

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