TY - JOUR
T1 - Remaining useful life prediction based on state assessment using edge computing on deep learning
AU - Hsu, Hsin Yao
AU - Srivastava, Gautam
AU - Wu, Hsin Te
AU - Chen, Mu Yen
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Intelligent industrial production has recently emerged as an important trend for application of the Industrial Internet of Things (IIoT) in edge computing. This study applied remote edge devices and edge servers, preprocessing the signal sensor, through covert data to cloud storage, and loaded the data to propose several deep learning methods to assess the status of aircraft engines in operation, and to classify stages of operational degradation so as to predict the functional remaining lifespan of components. The predicted results are transmitted to a cloud-based server for monitoring and maintenance.
AB - Intelligent industrial production has recently emerged as an important trend for application of the Industrial Internet of Things (IIoT) in edge computing. This study applied remote edge devices and edge servers, preprocessing the signal sensor, through covert data to cloud storage, and loaded the data to propose several deep learning methods to assess the status of aircraft engines in operation, and to classify stages of operational degradation so as to predict the functional remaining lifespan of components. The predicted results are transmitted to a cloud-based server for monitoring and maintenance.
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U2 - 10.1016/j.comcom.2020.05.035
DO - 10.1016/j.comcom.2020.05.035
M3 - Article
AN - SCOPUS:85085736122
SN - 0140-3664
VL - 160
SP - 91
EP - 100
JO - Computer Communications
JF - Computer Communications
ER -