Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters

Arthur C. Santos, Wesley A. Souza, Gustavo V. Barbara, Marcelo F. Castoldi () and Alessandro Goedtel
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Arthur C. Santos: Department of Electrical Engineering, Federal University of Technology—Parana (UTFPR), Cornelio Procopio 86300-000, Brazil
Wesley A. Souza: Department of Electrical Engineering, Federal University of Technology—Parana (UTFPR), Cornelio Procopio 86300-000, Brazil
Gustavo V. Barbara: Department of Electrical Engineering, Federal University of Technology—Parana (UTFPR), Cornelio Procopio 86300-000, Brazil
Marcelo F. Castoldi: Department of Electrical Engineering, Federal University of Technology—Parana (UTFPR), Cornelio Procopio 86300-000, Brazil
Alessandro Goedtel: Department of Electrical Engineering, Federal University of Technology—Parana (UTFPR), Cornelio Procopio 86300-000, Brazil

Sustainability, 2023, vol. 15, issue 20, 1-17

Abstract: Machine learning techniques are a widespread approach to monitoring and diagnosing faults in electrical machines. These techniques extract information from collected signals and classify the health conditions of internal components. Among all internal components, bearings present the highest failure rate. Classifiers commonly employ vibration data acquired from electrical machines, which can indicate different levels of bearing failure severity. Given the circumstances, this work proposes a methodology for detecting early bearing failures in wind turbines, applying classifiers that rely on Hjorth parameters. The Hjorth parameters were applied to analyze vibration signals collected from experiments to distinguish states of normal functioning and states of malfunction, hence enabling the classification of distinct conditions. After the labeling stage using Hjorth parameters, classifiers were employed to provide an automatic early fault identification model, with the decision tree, random forest, support vector machine, and k -nearest neighbors methods presenting accuracy levels of over 95%. Notably, the accuracy of the classifiers was maintained even after undergoing a dimensionality reduction process. Therefore, it can be stated that Hjorth parameters provide a feasible alternative for identifying early faults in wind generators through time-series analysis.

Keywords: wind generator; vibration signal; bearing; Hjorth parameters; early fault diagnosis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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