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Elevator Fault Detection Using Profile Extraction and Deep Autoencoder Feature Extraction for Acceleration and Magnetic Signals

In this paper, we propose a new algorithm for data extraction from time-series data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction, elevator start and stop events are extracted from sensor data including both acceleration...

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Principais autores: Krishna Mohan Mishra, Kalevi Huhtala
Formato: Artigo
Idioma:Inglês
Publicado em: MDPI AG 2019-07-01
coleção:Applied Sciences
Assuntos:
elevator system
deep autoencoder
fault detection
feature extraction
random forest
profile extraction
Acesso em linha:https://www.mdpi.com/2076-3417/9/15/2990
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