Results 281 to 290 of about 3,926,244 (335)

Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine

, 2021
For small sample data, it is difficult to complete the requirements of training complex models in the field of fault diagnosis. To solve the problem, this paper combines convolutional neural network's excellent feature processing ability with the ...
Tian Han, Longwen Zhang, Z. Yin, A. Tan
semanticscholar   +1 more source

A novel deep learning method based on attention mechanism for bearing remaining useful life prediction

Applied Soft Computing, 2020
Rolling bearing is a key component in rotation machine, whose remaining useful life (RUL) prediction is an essential issue of constructing condition-based maintenance (CBM) system.
Yuanhang Chen, Gaoliang Peng, Zhiyu Zhu, Sijue Li   +3 more
semanticscholar   +1 more source

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Cognitive Systems Research, 2019
Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault ...
Duy-Tang Hoang, Hee-Jun Kang
semanticscholar   +1 more source

Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum

, 2021
The diagnosis of early-stage defects of rolling element bearings (REBs) using vibration signals is a very difficult task since bearing fault signals are usually weak and masked by shaft rotating signals, gear meshing signals, and strong background noise.
Lang Xu, S. Chatterton, P. Pennacchi
semanticscholar   +1 more source

Data-Model Combined Driven Digital Twin of Life-Cycle Rolling Bearing

IEEE Transactions on Industrial Informatics, 2021
The digital twin of a life-cycle rolling bearing is significant for its degradation performance analysis and health management. This article proposes a digital twin model of life-cycle rolling bearing driven by the data-model combination.
Yi Qin, Xingguo Wu, Jun Luo
semanticscholar   +1 more source

A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN

IEEE Transactions on Instrumentation and Measurement, 2020
Aiming at fault visualization and automatic feature extraction, this article presents a new and intelligent bearing fault diagnostic method by combining symmetrized dot pattern (SDP) representation with squeeze-and-excitation-enabled convolutional neural
Haibo Wang, Jiawen Xu, Ruqiang Yan, R. Gao   +3 more
semanticscholar   +1 more source

Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals.

ISA transactions, 2021
The rolling bearing vibration signals are complex, non-linear, and non-stationary, it is difficult to extract the sensitive features and diagnose faults by conventional signal processing methods.
Zhenya Wang, Ligang Yao, Gang Chen, Jiaxin Ding   +3 more
semanticscholar   +1 more source

Bear essentials

Nursing Standard, 1987
One of casualty's most closely guarded secrets was recently revealed by Dr Carew-McColl, Consultant in the Accident and Emergency department at the Royal Preston Hospital.
openaire   +2 more sources

Polar Bear, Polar Bear

Science, 2010
Evolution![Figure][1] CREDIT: OYSTEIN WIIG, UNIVERSITY OF OSLO Polar bears are adapted to living in one of the harshest environments on earth, their range being determined by the extent of Arctic polar sea ice. They arose from the brown bear lineage and are most closely related to a group of genetically distinct brown bears that inhabit the ...
openaire   +1 more source

Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder

, 2020
The collected vibration data with labeled information from bearing is far insufficient in engineering practice, which is challenging for training an intelligent diagnosis model.
He Zhiyi, Haidong Shao, Jing Lin, Junsheng Cheng, Yang Yu   +4 more
semanticscholar   +1 more source