Bearing early fault detection and degradation tracking based on support tensor data description with feature tensor

Sun, Bing; Liu, Xiaofeng

doi:10.1016/j.apacoust.2021.108530

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Our approach 0.98 0.98 0.97 0.98 WPD-STDD [38] 1.00 1.00 --KNN [38] 0.97 0.97 --COM-GOA-SVDD [39] 0.92 0.90 1.00 0.94 GMPOP [40] 0.99 -1.00 -CSC-NSVDD [41] 0.99 0.99 0.99 0.99…”

Section: Methodsmentioning

confidence: 99%

“…The method with the best metrics was WPD-STDD, implemented in [38], which achieved an accuracy and precision of 100%. However, it is a tensor-based method that also uses attributes extracted in both the time and frequency domains, making it a computationally more expensive method with higher data storage requirements than that presented in this article, which solely utilized attributes in the time domain.…”

Section: Methodsmentioning

confidence: 99%

“…The best performance achieved by the methods in the literature was attained in [38]. However, the models were trained using equal quantities of healthy and faulty samples, even though these machines spend most of their time operating under normal conditions.…”

Section: Comparative Analysismentioning

confidence: 99%

“…However, the models were trained using equal quantities of healthy and faulty samples, even though these machines spend most of their time operating under normal conditions. The works [38][39][40][41] applied their respective methodologies using only the data collected in Experiment 2, which consisted solely of faulty signals from the outer race, in contrast to the method presented in this study. Our approach utilized data collected from Experiments 1 and 2, enabling the classifier to receive faulty data from the inner race, outer race, and rolling elements.…”

Section: Comparative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Diagnostics of Early Faults in Wind Generator Bearings Using Hjorth Parameters

Santos,

Souza,

Barbara

et al. 2023

Sustainability

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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.

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“…Our approach 0.98 0.98 0.97 0.98 WPD-STDD [38] 1.00 1.00 --KNN [38] 0.97 0.97 --COM-GOA-SVDD [39] 0.92 0.90 1.00 0.94 GMPOP [40] 0.99 -1.00 -CSC-NSVDD [41] 0.99 0.99 0.99 0.99…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%