Results 271 to 280 of about 3,926,244 (335)

An Integrated Multitasking Intelligent Bearing Fault Diagnosis Scheme Based on Representation Learning Under Imbalanced Sample Condition

IEEE Transactions on Neural Networks and Learning Systems, 2023
Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary mechanical system. In practice, the sample proportion between faulty data and healthy data in rotating mechanical system is imbalanced.
Jiusi Zhang, Kecheng Zhang, Yiyao An, Hao Luo, Shen Yin   +4 more
semanticscholar   +1 more source

Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis

IEEE Transactions on Industrial Informatics, 2020
Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling bearings. However, these neural networks are lack of interpretability for fault diagnosis tasks.
Huan Wang, Zhiliang Liu, Dandan Peng, Yong Qin   +3 more
semanticscholar   +1 more source

Meta-learning for few-shot bearing fault diagnosis under complex working conditions

Neurocomputing, 2021
Deep learning-based bearing fault diagnosis has been systematically studied in recent years. However, the success of most of these methods relies heavily on massive labeled data, which is not always available in real production environments.
Chuanjiang Li, Shaobo Li, Ansi Zhang, Qiang He, Zihao Liao, Jianjun Hu   +5 more
semanticscholar   +1 more source

Rolling Bearing Fault Diagnosis Method Base on Periodic Sparse Attention and LSTM

IEEE Sensors Journal, 2022
The rolling bearing fault signals are complex time series with complex dynamic characteristics and non-uniform periodicity due to the influence of random interference, such as random impulse noise and equipment vibration. This will affect the accuracy of
Yiyao An, Ke Zhang, Qie Liu, Yi Chai, Xinghua Huang   +4 more
semanticscholar   +1 more source

Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images

, 2021
The bearings are the crucial components of rotating machines in an industrial firm. Unplanned failure of these components not only increases the downtime, but also leads to production loss.
Anurag Choudhary, Tauheed Mian, S. Fatima   +2 more
semanticscholar   +1 more source

Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors

Measurement, 2021
Remaining useful life (RUL) prediction has been a hotspot topic, which is useful to avoid unexpected breakdowns and improve reliability. Different bearing failure behaviors caused by multiple failure modes may lead to inconsistent feature distribution ...
Cheng Han, Xianguang Kong, Chen Gaige, Qibin Wang, Rongbo Wang   +4 more
semanticscholar   +1 more source

What can bearings bear?

IEEE Industry Applications Magazine, 2006
This paper studies the series of 22 test runs performance for bearing damage assessment due to inverter-induced bearing currents. The influence of bearing current amplitude and type, calculated apparent bearing current density, time of operation, and inverter switching frequency on bearing damage was investigated.
A. Muetze, A. Binder, H. Vogel, J. Hering   +3 more
openaire   +1 more source

Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network

IEEE transactions on industrial electronics (1982. Print), 2019
Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency
Jun Zhu, Nan Chen, W. Peng
semanticscholar   +1 more source

A survey on Deep Learning based bearing fault diagnosis

Neurocomputing, 2019
Nowadays, Deep Learning is the most attractive research trend in the area of Machine Learning. With the ability of learning features from raw data by deep architectures with many layers of non-linear data processing units, Deep Learning has become a ...
Duy-Tang Hoang, Hee-Jun Kang
semanticscholar   +1 more source

A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion

IEEE Transactions on Instrumentation and Measurement, 2020
Bearing fault diagnosis has extensively exploited vibration signals (VSs) because of their rich information about bearing health conditions. However, this approach is expensive because the measurement of VSs requires external accelerometers. Moreover, in
Duy-Tang Hoang, Hee-Jun Kang
semanticscholar   +1 more source