Cargando…

Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis

Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, f...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wu, Jie, Tang, Tang, Chen, Ming, Hu, Tianhao
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2018
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6211093/ https://www.ncbi.nlm.nih.gov/pubmed/30279383 http://dx.doi.org/10.3390/s18103312

Ejemplares similares

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation
por: Tang, Guiji, et al.
Publicado: (2020)

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform
por: Pang, Bin, et al.
Publicado: (2018)

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum
por: Lv, Yong, et al.
Publicado: (2015)

An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise
por: Hu, Bingbing, et al.
Publicado: (2022)

Bearing Fault Diagnosis Based on Randomized Fisher Discriminant Analysis
por: Ye, Hejun, et al.
Publicado: (2022)

Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing
por: Xu, Yonggang, et al.
Publicado: (2019)

Bearing Fault Diagnosis Based on Energy Spectrum Statistics and Modified Mayfly Optimization Algorithm
por: Liu, Yuhu, et al.
Publicado: (2021)

A novel intelligent bearing fault diagnosis method based on signal process and multi-kernel joint distribution adaptation
por: Xiong, Jundi, et al.
Publicado: (2023)

Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis
por: Wang, Xiaodong, et al.
Publicado: (2020)

Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing
por: Shao, Xiaorui, et al.
Publicado: (2022)

Color Recurrence Plots for Bearing Fault Diagnosis
por: Petrauskiene, Vilma, et al.
Publicado: (2022)

Fault Diagnosis Method of Roadheader Bearing Based on VMD and Domain Adaptive Transfer Learning
por: Qu, Xiaofei, et al.
Publicado: (2023)

Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion
por: Zhu, Huibin, et al.
Publicado: (2021)

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation
por: Zheng, Zejun, et al.
Publicado: (2020)

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis
por: Hoang, Duy Tang, et al.
Publicado: (2021)

A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings
por: Wang, Huaqing, et al.
Publicado: (2016)

An Explainable AI-Based Fault Diagnosis Model for Bearings
por: Hasan, Md Junayed, et al.
Publicado: (2021)

A Novel Method for Bearing Fault Diagnosis under Variable Speed Based on Envelope Spectrum Fault Characteristic Frequency Band Identification
por: Pei, Di, et al.
Publicado: (2023)

Ball Bearing Fault Diagnosis Using Recurrence Analysis
por: Kecik, Krzysztof, et al.
Publicado: (2022)

Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy
por: Yan, Xiaoan, et al.
Publicado: (2021)

Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions
por: Wang, Ning, et al.
Publicado: (2018)

Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum
por: Zhao, Xuejun, et al.
Publicado: (2019)

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings
por: Zhou, Hongdi, et al.
Publicado: (2017)

A Novel Fault Diagnosis Method of Rolling Bearing Based on Integrated Vision Transformer Model
por: Tang, Xinyu, et al.
Publicado: (2022)

Naive Bayes Bearing Fault Diagnosis Based on Enhanced Independence of Data
por: Zhang, Nannan, et al.
Publicado: (2018)

Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise
por: Hu, Lei, et al.
Publicado: (2022)

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding
por: Wang, Xiang, et al.
Publicado: (2015)

A Bearing Fault Diagnosis Method Based on PAVME and MEDE
por: Yan, Xiaoan, et al.
Publicado: (2021)

Rolling Bearing Fault Diagnosis Based on WGWOA-VMD-SVM
por: Zhou, Junbo, et al.
Publicado: (2022)

An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio
por: Wu, Dengyun, et al.
Publicado: (2020)

Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study
por: Mao, Meijiao, et al.
Publicado: (2023)

Network Construction for Bearing Fault Diagnosis Based on Double Attention Mechanism
por: Wu, QingE, et al.
Publicado: (2022)

Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection
por: Maliuk, Andrei S., et al.
Publicado: (2021)

Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis
por: Zheng, Xiaorong, et al.
Publicado: (2022)

Online Domain Adaptation for Rolling Bearings Fault Diagnosis with Imbalanced Cross-Domain Data
por: Chao, Ko-Chieh, et al.
Publicado: (2022)

An AVMD-DBN-ELM Model for Bearing Fault Diagnosis
por: Lei, Xue, et al.
Publicado: (2022)

Rolling Bearing Fault Diagnosis Based on Optimal Notch Filter and Enhanced Singular Value Decomposition
por: Pang, Bin, et al.
Publicado: (2018)

Optimized Adaptive Local Iterative Filtering Algorithm Based on Permutation Entropy for Rolling Bearing Fault Diagnosis
por: Lv, Yong, et al.
Publicado: (2018)

A Novel Fault Diagnosis Method of Rolling Bearings Based on AFEWT-KDEMI
por: Ge, Mingtao, et al.
Publicado: (2018)

A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis
por: Shen, Changqing, et al.
Publicado: (2013)

Cannot write session to /tmp/vufind_sessions/sess_jusagka6tgnm6rbua73ovf9s0b