A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment
Crossref DOI link: https://doi.org/10.1007/s00170-019-03557-w
Published Online: 2019-03-22
Published Print: 2019-07
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Li, Zhe http://orcid.org/0000-0002-7627-2075
Li, Jingyue
Wang, Yi
Wang, Kesheng
Funding for this research was provided by:
NordForsk (83144)
Text and Data Mining valid from 2019-03-22
Article History
Received: 17 October 2018
Accepted: 8 March 2019
First Online: 22 March 2019