Rotating Machinery Fault Diagnosis Using Long-short-term Memory Recurrent Neural Network
Authors:
- Rui YangRYRui Yang
- Mengjie HuangMHMengjie Huang
- Qidong LuQLQidong Lu
- Maiying ZhongMZMaiying Zhong
- 1 January 2018
- journal article
- Published by Elsevier in IFAC-PapersOnLine
- Vol. 51 (24) , 228-232
Abstract
No abstract availableKeywords
This publication has 16 references indexed in Scilit:
- Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network2016
- Fault diagnosis based on deep learning2016
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive dataMechanical Systems and Signal Processing, 2016
- Railway Track Circuit Fault Diagnosis Using Recurrent Neural NetworksIEEE Transactions on Neural Networks and Learning Systems, 2016
- Basic Research on Machinery Fault Diagnosis—What is the PrescriptionJournal of Mechanical Engineering, 2013
- On Designing $H_{\infty}$ Fault Detection Filter for Linear Discrete Time-Varying SystemsIEEE Transactions on Automatic Control, 2010
- The Fast Johnson–Lindenstrauss Transform and Approximate Nearest NeighborsSIAM Journal on Computing, 2009
- Compressed sensingIEEE Transactions on Information Theory, 2006
- Long Short-Term MemoryNeural Computation, 1997
- An innovations approach to fault detection and diagnosis in dynamic systemsAutomatica, 1971