Related references
Note: Only part of the references are listed.A two-stage attention aware method for train bearing shed oil inspection based on convolutional neural networks
Xiao Fu et al.
NEUROCOMPUTING (2020)
Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)
Anil Kumar et al.
ALEXANDRIA ENGINEERING JOURNAL (2020)
An automatic system for bearing surface tiny defect detection based on multi-angle illuminations
Bin Liu et al.
OPTIK (2020)
A modified transmissibility indicator and Artificial Neural Network for damage identification and quantification in laminated composite structures
Roumaissa Zenzen et al.
COMPOSITE STRUCTURES (2020)
Improved ANN technique combined with Jaya algorithm for crack identification in plates using XIGA and experimental analysis
S. Khatir et al.
THEORETICAL AND APPLIED FRACTURE MECHANICS (2020)
A Novel Rolling Bearing Defect Detection Method Based on Bispectrum Analysis and Cloud Model-Improved EEMD
Yonghua Jiang et al.
IEEE ACCESS (2020)
A bearing data analysis based on kurtogram and deep learning sequence models
Sandeep S. Udmale et al.
MEASUREMENT (2019)
Accurate and Efficient Inspection of Speckle and Scratch Defects on Surfaces of Planar Products
Hui Kong et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2017)
Root Cause Identification of Machining Error Based on Statistical Process Control and Fault Diagnosis of Machine Tools
Hongrui Cao et al.
MACHINES (2017)
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
Yaguo Lei et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)
Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time-frequency ridge enhancement
Chuan Li et al.
ISA TRANSACTIONS (2016)
Computer vision algorithm for measurement and inspection of O-rings
Gaoliang Peng et al.
MEASUREMENT (2016)
A summary of fault modelling and predictive health monitoring of rolling element bearings
Idriss El-Thalji et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)
A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects
Jun Wang et al.
APPLIED ACOUSTICS (2014)
A Review of Dynamic Modeling and Fault Identifications Methods for Rolling Element Bearing
Dipen S. Shah et al.
2ND INTERNATIONAL CONFERENCE ON INNOVATIONS IN AUTOMATION AND MECHATRONICS ENGINEERING, ICIAME 2014 (2014)
Bearing defect inspection based on machine vision
Hao Shen et al.
MEASUREMENT (2012)
Rolling element bearing diagnostics-A tutorial
Robert B. Randall et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2011)
Current Status and Prospect of Roller Bearing Surface Defect Detection
Huinan Wang et al.
CEIS 2011 (2011)
Flaw detection of cylindrical surfaces in PU-packing by using machine vision technique
Yih-Chih Chiou et al.
MEASUREMENT (2009)
Automatic thresholding for defect detection
Hui-Fuang Ng
PATTERN RECOGNITION LETTERS (2006)
A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing
ZK Peng et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2005)
Bearing fault diagnosis based on wavelet transform and fuzzy inference
XS Lou et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2004)
Artificial neural network based fault diagnostics of rolling element bearings using time-domain features
B Samanta et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2003)
The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals
RB Randall et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2001)
Neural-network-based motor rolling bearing fault diagnosis
B Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2000)