Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbines

Gustavo de Novaes Pires Leite, Guilherme Tenório Maciel da Cunha, José Guilhermino dos Santos Junior, Alex Maurício Araújo, Pedro André Carvalho Rosas, Tatijana Stosic, Borko Stosic and Osvaldo Anibal Rosso

Renewable Energy, 2021, vol. 164, issue C, 1183-1194

Abstract: Wind turbines operate almost uninterruptedly, and their operation is often subject to harsh environments, as well as complex and dynamic loads. Fourier analysis, a standard diagnostic technique, presents some limitations regarding the use of non-stationary, non-periodic, noisy data, which is precisely the case with wind turbine data. Due to these limitations, unseen faults could progress and cause severe, and even catastrophic, failure in wind turbines. Information theory quantifiers, such as entropy, divergence, and, statistical complexity measure, are proposed to evaluate the health status of wind turbine components. In this work, this is done via the decomposition of the signal in time, frequency, and time-frequency domain, namely via Bandt and Pompe, power spectrum, and wavelet packet decomposition. Two different real data sets from operational wind turbines were characterized by the proposed methods. Results demonstrate that the proposed method can distinguish (cluster) well between the states of fault, but also presented some limitations, mainly related to the complexity of the signal from operational wind turbines. Based on these results, new methods, complementary to Fourier analysis, are proposed to be employed in wind turbine data, aiming to increase the capability of detecting faults in such a complex environment.

Keywords: Entropy features; Statistical complexity; Vibration analysis; Wavelet packet; Bandt and pompe (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:164:y:2021:i:c:p:1183-1194

DOI: 10.1016/j.renene.2020.10.129

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