A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-t...
Main Authors: | Huihui Qiao, Taiyong Wang, Peng Wang, Shibin Qiao, Lan Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-09-01
|
Series: | Sensors |
Subjects: | |
Online Access: | http://www.mdpi.com/1424-8220/18/9/2932 |
Similar Items
-
Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
by: Yuheng Ji, et al.
Published: (2024-01-01) -
A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses
by: Hongjing Cui, et al.
Published: (2024-03-01) -
Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM
by: Liangchao Li, et al.
Published: (2023-06-01) -
An Integrated Framework for Spatiotemporally Merging Multi-Sources Precipitation Based on F-SVD and ConvLSTM
by: Sheng Sheng, et al.
Published: (2023-06-01) -
Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms
by: Yonghong Zhang, et al.
Published: (2023-01-01)