A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
Authors
Keywords
-
Journal
SENSORS
Volume 18, Issue 9, Pages 2932
Publisher
MDPI AG
Online
2018-09-05
DOI
10.3390/s18092932
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
- (2018) Rui Zhao et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Learning Deep Spatio-Temporal Dependence for Semantic Video Segmentation
- (2018) Zhaofan Qiu et al. IEEE TRANSACTIONS ON MULTIMEDIA
- Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders
- (2018) Nastaran Mohammadian Rad et al. SIGNAL PROCESSING
- Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine
- (2017) Ruonan Liu et al. IEEE Transactions on Industrial Informatics
- A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes
- (2017) Ki Bum Lee et al. IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING
- Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks
- (2017) Osama Abdeljaber et al. JOURNAL OF SOUND AND VIBRATION
- An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition
- (2017) Eleni Tsironi et al. NEUROCOMPUTING
- Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
- (2017) Maciej Wielgosz et al. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
- An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox
- (2017) Luyang Jing et al. SENSORS
- Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
- (2017) Rui Zhao et al. SENSORS
- Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm
- (2017) Viet Tra et al. SENSORS
- Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks
- (2017) Tim de Bruin et al. IEEE Transactions on Neural Networks and Learning Systems
- Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies
- (2017) Xiaoya Xu et al. IEEE Access
- Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
- (2016) Turker Ince et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Tool condition monitoring for form milling of large parts by combining spindle motor current and acoustic emission signals
- (2016) Masahiro Uekita et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- Maxout neurons for deep convolutional and LSTM neural networks in speech recognition
- (2016) Meng Cai et al. SPEECH COMMUNICATION
- Fault Diagnosis in Internal Combustion Engines Using Extension Neural Network
- (2013) Yousef Shatnawi et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Planetary gearbox fault diagnosis using an adaptive stochastic resonance method
- (2012) Yaguo Lei et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started