Results 331 to 340 of about 15,854,076 (410)

A Meta-Learning Method for Electric Machine Bearing Fault Diagnosis Under Varying Working Conditions With Limited Data

IEEE Transactions on Industrial Informatics, 2023
Effective detection of fault in rolling bearings with a limited amount of data is essential for the safe operation of electric machines. This article proposes a novel meta-learning-enabled method for the detection of fault in rolling bearings of electric
Jianjun Chen, Weihao Hu, Di Cao, Zhenyuan Zhang, Zhe Chen, Frede Blaabjerg   +5 more
semanticscholar   +1 more source

Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2022
The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass ...
Xiaoli Zhao, Jianyong Yao, W. Deng, Peng Ding, Yifei Ding, M. Jia, Zheng Liu   +6 more
semanticscholar   +1 more source

Dynamic Stability of Copper Single-Atom Catalysts under Working Conditions.

Journal of the American Chemical Society, 2022
The long-term stability of single-atom catalysts is a major factor affecting their large-scale commercial application. How to evaluate the dynamic stability of single-atom catalysts under working conditions is still lacking.
Xiaowan Bai, Xunhua Zhao, Yehui Zhang, Chongyi Ling, Yipeng Zhou, Jinlan Wang, Yuanyue Liu   +6 more
semanticscholar   +1 more source

A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions

IEEE Transactions on Instrumentation and Measurement, 2021
The data-driven methods in machinery fault diagnosis have become increasingly popular in the past two decades. However, the wide applications of this scheme are generally compromised in real-world conditions because of the discrepancy between the ...
Te Han, Yan-Fu Li, Min Qian
semanticscholar   +1 more source

Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions

IEEE Transactions on Instrumentation and Measurement, 2021
Unsupervised domain adaptation (UDA)-based methods have made great progress in mechanical fault diagnosis under variable working conditions. In UDA, three types of information, including class label, domain label, and data structure, are essential to ...
Tianfu Li, Zhibin Zhao, Chuang Sun, Ruqiang Yan, Xuefeng Chen   +4 more
semanticscholar   +1 more source

Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions

IEEE transactions on industrial electronics (1982. Print), 2021
As a deep learning model, a deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis methods are proposed.
Saibo Xing, Y. Lei, Shuhui Wang, Feng Jia   +3 more
semanticscholar   +1 more source

Sustaining a Sense of Success: The Importance of Teacher Working Conditions During the COVID-19 Pandemic

Proceedings of the 2021 AERA Annual Meeting, 2021
COVID-19 shuttered schools across the United States, upending traditional approaches to education. We examine teachers’ experiences during emergency remote teaching in the spring of 2020 using responses to a working conditions survey from a sample of 7 ...
M. Kraft, Nicole Simon, M. Lyon
semanticscholar   +1 more source