Deep variational auto-encoders: A promising tool for dimensionality reduction and ball bearing elements fault diagnosis

Variational Inference: A Review for Statisticians

(2017) David M. Blei et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

(2017) David Verstraete et al.   SHOCK AND VIBRATION

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

(2017) Chen Lu et al.   SIGNAL PROCESSING

Principal component analysis: a review and recent developments

(2016) Ian T. Jolliffe et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

(2015) Wade A. Smith et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Structural damage assessment using linear approximation with maximum entropy and transmissibility data

(2015) V. Meruane et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples

(2013) Zhipeng Feng et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A review on empirical mode decomposition in fault diagnosis of rotating machinery

(2012) Yaguo Lei et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

PRIOR AND POSTERIOR ROBUST STOCHASTIC PREDICTIONS FOR DYNAMICAL SYSTEMS USING PROBABILITY LOGIC

(2012) James L. Beck et al.   International Journal for Uncertainty Quantification

Fault detection based on Kernel Principal Component Analysis

(2010) Viet Ha Nguyen et al.   ENGINEERING STRUCTURES