Cargando…

A Novel Method for Fault Diagnosis of Rotating Machinery

When rotating machinery fails, the consequent vibration signal contains rich fault feature information. However, the vibration signal bears the characteristics of nonlinearity and nonstationarity, and is easily disturbed by noise, thus it may be difficult to accurately extract hidden fault features....

Descripción completa

Detalles Bibliográficos
Autores principales:	Tang, Meng, Liao, Yaxuan, Luo, Fan, Li, Xiangshun
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140465/ https://www.ncbi.nlm.nih.gov/pubmed/35626565 http://dx.doi.org/10.3390/e24050681

Ejemplares similares

An Imbalanced Fault Diagnosis Method Based on TFFO and CNN for Rotating Machinery
por: Zhang, Long, et al.
Publicado: (2022)

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing
por: Lu, Chen, et al.
Publicado: (2016)

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network
por: Guo, Sheng, et al.
Publicado: (2018)

A Review of Fault Diagnosis Methods for Rotating Machinery Using Infrared Thermography
por: Wang, Rongcai, et al.
Publicado: (2022)

Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning
por: Inyang, Udeme Ibanga, et al.
Publicado: (2023)

Rotating machinery fault diagnosis based on a novel lightweight convolutional neural network
por: Yan, Jing, et al.
Publicado: (2021)

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
por: Wei, Yu, et al.
Publicado: (2019)

Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks
por: Li, Hongmei, et al.
Publicado: (2020)

Intelligent fault diagnosis and remaining useful life prediction of rotating machinery
por: Lei, Yaguo
Publicado: (2016)

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis
por: Xiao, Qiyang, et al.
Publicado: (2022)

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition
por: Lei, Yaguo, et al.
Publicado: (2013)

Novel Investigation of Higher Order Spectral Technologies for Fault Diagnosis of Motor-Based Rotating Machinery
por: Ciszewski, Tomasz, et al.
Publicado: (2023)

A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery
por: Zhou, Funa, et al.
Publicado: (2018)

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis
por: Faysal, Atik, et al.
Publicado: (2021)

LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information
por: Liu, Zhiwen, et al.
Publicado: (2013)

Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer
por: Ye, Lihao, et al.
Publicado: (2021)

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning
por: Li, Chuan, et al.
Publicado: (2016)

A Lighted Deep Convolutional Neural Network Based Fault Diagnosis of Rotating Machinery
por: Ma, Shangjun, et al.
Publicado: (2019)

A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery
por: Jia, Sixiang, et al.
Publicado: (2021)

Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization
por: Li, Ke, et al.
Publicado: (2011)

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks
por: Yang, Jingli, et al.
Publicado: (2020)

Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions
por: Jang, Gye-Bong, et al.
Publicado: (2021)

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis
por: Cai, Yuxiang, et al.
Publicado: (2022)

A Precise Diagnosis Method of Structural Faults of Rotating Machinery based on Combination of Empirical Mode Decomposition, Sample Entropy, and Deep Belief Network
por: Guan, Zhaoyi, et al.
Publicado: (2019)

Fault Diagnosis of Rotating Machinery Based on Improved Self-Supervised Learning Method and Very Few Labeled Samples
por: Wei, Meirong, et al.
Publicado: (2021)

An improved wrapper-based feature selection method for machinery fault diagnosis
por: Hui, Kar Hoou, et al.
Publicado: (2017)

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors
por: Miao, Feng, et al.
Publicado: (2020)

A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
por: Gong, Wenfeng, et al.
Publicado: (2019)

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory
por: Gao, Yangde, et al.
Publicado: (2021)

The Optimized Multi-Scale Permutation Entropy and Its Application in Compound Fault Diagnosis of Rotating Machinery
por: Wang, Xianzhi, et al.
Publicado: (2019)

A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
por: Liu, Wenkai, et al.
Publicado: (2019)

Machine learning for fault analysis in rotating machinery: A comprehensive review
por: Das, Oguzhan, et al.
Publicado: (2023)

A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum
por: Purbowaskito, Widagdo, et al.
Publicado: (2021)

Rotating Machinery Fault Diagnosis Based on Improved Multiscale Amplitude-Aware Permutation Entropy and Multiclass Relevance Vector Machine
por: Chen, Yinsheng, et al.
Publicado: (2019)

Multi-Stream Convolutional Neural Networks for Rotating Machinery Fault Diagnosis under Noise and Trend Items
por: Dong, Han, et al.
Publicado: (2022)

An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis
por: Zhu, Fu, et al.
Publicado: (2022)

A Fault Detection Method Based on CPSO-Improved KICA
por: Liu, Mingguang, et al.
Publicado: (2019)

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery
por: Wang, Gang, et al.
Publicado: (2021)

A Fuzzy Fusion Rotating Machinery Fault Diagnosis Framework Based on the Enhancement Deep Convolutional Neural Networks
por: Yang, Daoguang, et al.
Publicado: (2022)

Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification
por: Mochammad, Solichin, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_olnhok21vrobf25p4ut4m7s3pb