Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter–Asymmetric Denoising Autoencoder

Supervisory control and data acquisition (SCADA) systems are widely utilized in power equipment for condition monitoring. For the collected data, there generally exists a problem—missing data of different types and patterns. This leads to the poor quality and utilization difficulties of the collecte...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 23; no. 24; p. 9697
Main Authors:	Jiang, Ling, Gu, Juping, Zhang, Xinsong, Hua, Liang, Cai, Yueming
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 08.12.2023 MDPI
Subjects:	Accuracy asymmetric denoising autoencoder Case studies Data entry data imputation Data transmission Deep learning Missing data moving average filter multi-type missing power equipment monitoring data Sensors Teaching methods moving average filter data imputation asymmetric denoising autoencoder power equipment monitoring data multi-type missing
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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