Bearing Remaining Useful Life Prediction Based on CICAE and ResConv1D-LSTM

Bearings play a critical role in industrial machinery, and accurate predictions of the remaining useful life (RUL) of bearings are essential for ensuring the safe operation of rotating equipment. However, traditional autoencoders and CNN-based approaches tend to assign equal weights to all feature c...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 74; s. 1
Hlavní autori:	Qin, Yi, Zhao, Yihang, Zhuo, Yi'an, Chai, Yi, Yang, Zhenli, Mao, Yongfang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Autoencoders Bearing remaining useful life Channels CICAE Computer architecture Constraints Convolution Decoding Deep learning Degradation Entropy (Information theory) Feature extraction Life prediction Logic gates Long short term memory Machine learning Predictive models ResConv1DLSTM RUL prediction Training Useful life
ISSN:	0018-9456, 1557-9662
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