Related references
Note: Only part of the references are listed.Remaining useful life estimation in prognostics using deep convolution neural networks
Xiang Li et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2018)
Software reliability prediction using a deep learning model based on the RNN encoder-decoder
Jinyong Wang et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2018)
Background Information of Deep Learning for Structural Engineering
Seunghye Lee et al.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2018)
Automated structural health monitoring based on adaptive kernel spectral clustering
Rocco Langone et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
Yaguo Lei et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)
Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment
Linxia Liao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
Wenjun Sun et al.
MEASUREMENT (2016)
Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network
Peng Jiang et al.
SENSORS (2016)
Transformer fault diagnosis using continuous sparse autoencoder
Lukun Wang et al.
SPRINGERPLUS (2016)
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
Wade A. Smith et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)
Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples
Zhipeng Feng et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2013)
Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis
J Lin et al.
JOURNAL OF SOUND AND VIBRATION (2000)