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

Alejandro Blanco-M., Pere Marti-Puig, Karina Gibert, Jordi Cusidó and Jordi Solé-Casals
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Alejandro Blanco-M.: Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain
Pere Marti-Puig: Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain
Karina Gibert: 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
Jordi Cusidó: Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain
Jordi Solé-Casals: Data and Signal Processing Group, University of Vic—Central University of Catalonia, 08500 Vic, Catalonia, Spain

Energies, 2019, vol. 12, issue 10, 1-20

Abstract: 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.

Keywords: wind turbine; service history; classification; fault diagnosis; renewable energy; text mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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