Fault diagnosis of rotating machinery by neural networks

被引：0

作者：

Ligteringen, R ^{[1
]}

Ypma, A ^{[1
]}

Duin, RPW ^{[1
]}

Frietman, EEE ^{[1
]}

机构：

[1] Delft Univ Technol, Fac Appl Phys, Pattern Recognit Grp, NL-2628 CJ Delft, Netherlands

来源：

NEURAL NETWORKS: BEST PRACTICE IN EUROPE | 1997年 / 8卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Machine failure due to mechanical problems might be predicted by unexpected large deviations in the standard vibration pattern. This pattern and its variations during normal use can be learned with neural network techniques from a training set of measured vibrations patterns. In this paper we describe the experimental setup for such measurements for the case of a medium size pump.

引用

页码：161 / 164

页数：4

共 50 条

[1] Application of neural networks in fault diagnosis of rotating machinery
Qing, He
Dongmei, Du
[J]. Proceedings of the ASME Power Conference 2007, 2007, : 279 - 282
[2] Study on Fault Diagnosis of Rotating Machinery with Hybrid Neural Networks
臧朝平
高伟
[J]. Journal of Southeast University(English Edition), 1997, (02) : 68 - 73
[3] Fault diagnosis of rotating machinery based on recurrent neural networks
Zhang, Yahui
Zhou, Taotao
Huang, Xufeng
Cao, Longchao
Zhou, Qi
[J]. MEASUREMENT, 2021, 171
[4] The Study on Rotating Machinery Fault diagnosis Based on Deep Neural Networks
Lang Bo
Jin Ying
Chen Yu Ping
Fan Xiaolong
[J]. 2016 INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION, BIG DATA & SMART CITY (ICITBS), 2017, : 125 - 129
[5] Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks
Xia, Min
Li, Teng
Xu, Lin
Liu, Lizhi
de Silva, Clarence W.
[J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2018, 23 (01) : 101 - 110
[6] Fault Diagnosis of Rotating Machinery based on Domain Adversarial Training of Neural Networks
Di, Yun
Yang, Rui
Huang, Mengjie
[J]. PROCEEDINGS OF 2021 IEEE 30TH INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2021,
[7] Fault diagnosis of rotating machinery based on harmonic wavelet fuzzy neural networks
Peng, Bin
Liu, Zhen-Quan
[J]. Dongli Gongcheng/Power Engineering, 2005, 25 (05): : 702 - 706
[8] Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis
Zhao, Ke
Jiang, Hongkai
Li, Xingqiu
Wang, Ruixin
[J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (05) : 1483 - 1499
[9] Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks
Li, Hongmei
Huang, Jinying
Yang, Xiwang
Luo, Jia
Zhang, Lidong
Pang, Yu
[J]. ENTROPY, 2020, 22 (08)
[10] Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis
Ke Zhao
Hongkai Jiang
Xingqiu Li
Ruixin Wang
[J]. International Journal of Machine Learning and Cybernetics, 2021, 12 : 1483 - 1499

← 1 2 3 4 5 →