4.1 Article

Fault Diagnosis of Rolling Bearings Based on Spectral Kurtosis Graph and LFMB Network

Related references

Note: Only part of the references are listed.
Article Engineering, Multidisciplinary

Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method

Jun Li et al.

Summary: The paper introduces a novel fault diagnosis approach for rolling bearings, combining DA-RNN and CBAM technologies, which enhances diagnostic accuracy by handling imbalanced datasets and utilizing convolutional neural networks.

MEASUREMENT (2022)

Article Engineering, Mechanical

A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis

Qing Ni et al.

Summary: This study proposes a fault information-guided VMD (FIVMD) method for extracting weak bearing repetitive transients. By using statistical models and defining the fault characteristic amplitude ratio, the optimal decomposition parameters can be determined to successfully diagnose bearing faults.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2022)

Article Engineering, Mechanical

Unknown bearing fault diagnosis under time-varying speed conditions and strong noise background

Jianhua Yang et al.

Summary: The bearing fault diagnosis is an important problem due to the non-stationary nature and background noise interference of bearing vibration signals. This study proposes a method for extracting unknown fault characteristics from non-stationary vibration signals using stochastic resonance technology. The method successfully determines the bearing fault pattern through order tracking and coherence resonance theory.

NONLINEAR DYNAMICS (2022)

Article Materials Science, Characterization & Testing

Bearing Fault Feature Extraction Method Based on Variational Mode Decomposition of Fractional Fourier Transform

Ming Hui Wei et al.

Summary: A new method of fault feature extraction based on the FRFT-VMD method is proposed, which optimizes the FRFT and VMD for fault feature extraction and improves the accuracy of bearing fault diagnosis.

RUSSIAN JOURNAL OF NONDESTRUCTIVE TESTING (2022)

Article Engineering, Multidisciplinary

A hybrid deep-learning model for fault diagnosis of rolling bearings

Yang Xu et al.

Summary: This study proposes a hybrid deep learning model based on CNN and gcForest to improve the detection accuracy of bearing faults. By converting vibration signals into time-frequency images using continuous wavelet transform and extracting fault features from them, the method achieves higher performance compared to conventional CNN and gcForest models.

MEASUREMENT (2021)

Article Chemistry, Analytical

Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Shih-Lin Lin

Summary: This study introduces a method for bearing fault diagnosis based on VMD-DenseNet, which converts vibration signals into images through analyzing the Hilbert spectrum, and achieves diagnosis without the need to select features. Experimental results show that the method can accurately identify common motor faults with a prediction accuracy rate of 92%.

SENSORS (2021)

Article Engineering, Multidisciplinary

Extraction and enhancement of unknown bearing fault feature in the strong noise under variable speed condition

Jianhua Yang et al.

Summary: This paper proposes a method based on FRFT and SR to extract bearing fault features, successfully diagnosing scratches and removing noise interference in unknown situations. The method may provide reference for fault diagnosis in engineering occasions.

MEASUREMENT SCIENCE AND TECHNOLOGY (2021)

Article Computer Science, Artificial Intelligence

A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors

Christos T. Alexakos et al.

Summary: This paper proposes a new method for diagnosing the health of rolling element bearings in rotating electric machines, using STFT and ICT for processing and classifying vibration images, achieving higher accuracy and requiring less computational resources compared to the CNN approach.

MACHINE LEARNING AND KNOWLEDGE EXTRACTION (2021)

Article Engineering, Multidisciplinary

Fault diagnosis of rolling element bearing based on symmetric cross entropy of neutrosophic sets

Anil Kumar et al.

MEASUREMENT (2020)

Review Engineering, Mechanical

Applications of machine learning to machine fault diagnosis: A review and roadmap

Yaguo Lei et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

Article Engineering, Multidisciplinary

Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)

Anil Kumar et al.

ALEXANDRIA ENGINEERING JOURNAL (2020)

Article Engineering, Mechanical

A novel unsupervised learning method for intelligent fault diagnosis of rolling element bearings based on deep functional auto-encoder

Anas H. Aljemely et al.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2020)

Article Engineering, Mechanical

Fault diagnosis of rolling element bearing based on artificial neural network

Rohit S. Gunerkar et al.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2019)

Article Engineering, Multidisciplinary

A bearing data analysis based on kurtogram and deep learning sequence models

Sandeep S. Udmale et al.

MEASUREMENT (2019)

Article Engineering, Mechanical

An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

Bin Yang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)

Article Automation & Control Systems

Time-frequency analysis for bearing fault diagnosis using multiple Q-factor Gabor wavelets

Xin Zhang et al.

ISA TRANSACTIONS (2019)

Article Computer Science, Artificial Intelligence

A survey on Deep Learning based bearing fault diagnosis

Duy-Tang Hoang et al.

NEUROCOMPUTING (2019)

Article Chemistry, Multidisciplinary

Rolling-Element Bearing Fault Diagnosis Using Advanced Machine Learning-Based Observer

Farzin Piltan et al.

APPLIED SCIENCES-BASEL (2019)

Proceedings Paper Computer Science, Artificial Intelligence

An intelligent bearing fault diagnosis system: A review

S. R. Saufi et al.

ENGINEERING APPLICATION OF ARTIFICIAL INTELLIGENCE CONFERENCE 2018 (EAAIC 2018) (2019)

Article Multidisciplinary Sciences

Bearing vibration data collected under time-varying rotational speed conditions

Huan Huang et al.

DATA IN BRIEF (2018)

Article Engineering, Multidisciplinary

Deep Learning Based Approach for Bearing Fault Diagnosis

Miao He et al.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2017)

Article Engineering, Mechanical

A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM

Yongjian Li et al.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2017)

Review Engineering, Mechanical

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

Yanxue Wang et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)

Review Engineering, Mechanical

A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings

Akhand Rai et al.

TRIBOLOGY INTERNATIONAL (2016)

Article Engineering, Multidisciplinary

A Comparative Study of Various Methods of Bearing Faults Diagnosis Using the Case Western Reserve University Data

Adel Boudiaf et al.

JOURNAL OF FAILURE ANALYSIS AND PREVENTION (2016)

Article Engineering, Multidisciplinary

Rolling element bearing fault detection using PPCA and spectral kurtosis

Jiawei Xiang et al.

MEASUREMENT (2015)

Article Computer Science, Artificial Intelligence

Intelligent bearing fault detection by enhanced energy operator

M. Liang et al.

EXPERT SYSTEMS WITH APPLICATIONS (2014)

Article Engineering, Mechanical

The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings

P. Borghesani et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2014)

Article Engineering, Mechanical

Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition

George Georgoulas et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2013)

Article Computer Science, Artificial Intelligence

Fault diagnosis of ball bearings using machine learning methods

P. K. Kankar et al.

EXPERT SYSTEMS WITH APPLICATIONS (2011)

Article Engineering, Mechanical

The spectral kurtosis: a useful tool for characterising non-stationary signals

J Antoni

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2006)