A deep transfer learning method based on stacked autoencoder for cross-domain fault diagnosis
Authors:
- Ziwei DengZDZiwei Deng
- Zhuoyue WangZWZhuoyue Wang
- Zhaohui TangZTZhaohui Tang
- Keke HuangKHKeke Huang
- Hongqiu ZhuHZHongqiu Zhu
- 1 November 2021
- journal article
- research article
- Published by Elsevier in Applied Mathematics and Computation
- Vol. 408
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China
This publication has 28 references indexed in Scilit:
- Fault diagnosis in spur gears based on genetic algorithm and random forestMechanical Systems and Signal Processing, 2016
- Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processesNeurocomputing, 2016
- Coupled Projections for Adaptation of DictionariesIEEE Transactions on Image Processing, 2015
- Multivariate Statistical Process Monitoring Based on Statistics Pattern AnalysisIndustrial & Engineering Chemistry Research, 2010
- Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inferenceExpert Systems with Applications, 2009
- A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVMMeasurement, 2007
- Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimensionMechanical Systems and Signal Processing, 2007
- Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensembleMechanical Systems and Signal Processing, 2007
- Multidimensional Visualization of Principal Component Scores for Process Historical Data AnalysisIndustrial & Engineering Chemistry Research, 2004
- Statistical process monitoring: basics and beyondJournal of Chemometrics, 2003