A New Unsupervised Online Early Fault Detection Framework of Rolling Bearings Based on Granular Feature Forecasting

In online scenarios, the monitoring signals are collected in the form of streaming data and would raise some requirements for early fault detection (EFD) of rolling bearings: 1) enhancing the detection accuracy of online data; 2) lowering the computational cost of real-time detection; 3) reducing fa...

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Vydané v:	IEEE access Ročník 9; s. 159684 - 159698
Hlavní autori:	Liu, Keying, Mao, Wentao, Shi, Huadong, Liang, Xihui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	alarm threshold anomaly detection Datasets Degradation denoising autoencoder Early fault detection Employee welfare False alarms Fault detection Feature extraction Forecasting Mathematical models Prognostics and health management Representations Roller bearings Signal monitoring streaming data Task analysis Tensors Time series analysis Training Transfer learning
ISSN:	2169-3536, 2169-3536
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