research on novel bearing fault diagnosis method based on

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform their fault diagnosis has attracted considerable research attention Established fault feature extraction methods focus on statistical characteristics of the vibration signal which is an approach that loses sight An intelligent fault diagnosis method of rolling element bearing based on acoustic imaging and Gabor wavelet transform 25th International Congress on Sound and Vibration Hiroshima Japan 2018 p 2684-2691 Grządziela A Musial J Muślewski Ł Pajak M

Intelligent Bearing Fault Diagnosis Method Combining

Abstract: Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems Traditionally time-domain vibration-based fault diagnosis has some deficiencies such as complex computation of feature vectors excessive dependence on prior knowledge and diagnostic expertise and limited capacity for learning complex relationships in fault

2019-7-30Research Article A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy GuijiTang XiaolongWang andYulingHe School of Energy Power and Mechanical Engineering North China Electric Power University Baoding China

Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment and it has important economy and security to realize their quick and accurate fault detection As one kind of powerful cyclostationarity signal analyzing method spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating

2019-3-6sensors Article Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning Gaowei Xu 1 Min Liu 1 * Zhuofu Jiang 1 Dirk Sffker 2 and Weiming Shen 3 4 1 School of Electronics and Information Engineering Tongji University Shanghai 201804 China gaoweixutongji edu cn (G X ) 1732919tongji edu cn (Z J )

An intelligent fault diagnosis method of rolling element bearing based on acoustic imaging and Gabor wavelet transform 25th International Congress on Sound and Vibration Hiroshima Japan 2018 p 2684-2691 Grządziela A Musial J Muślewski Ł Pajak M

Research Article A Novel Method of Fault Diagnosis for

2019-7-30Research Article A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy GuijiTang XiaolongWang andYulingHe School of Energy Power and Mechanical Engineering North China Electric Power University Baoding China

Rolling element bearing and gear are the typical supporting or rotating parts in mechanical equipment and it has important economy and security to realize their quick and accurate fault detection As one kind of powerful cyclostationarity signal analyzing method spectral correlation (SC) could identify the impulsive characteristic component buried in the vibration signals of rotating

2017-8-1Bearing fault diagnosis under varying working condition based on domain adaptation Bo Zhanga b Wei Lia Zhe Tong a Meng Zhang aSchool of Mechatronic Engineering China University of Mining and Technology Xuzhou 221116 Peoples Republic of China bSchool of Computer Science And Technology China University of Mining and Technology Xuzhou 221116 Peoples Republic of China

Chapter 4 A Novel Biomimetic Design Method Based on Biology Texts Under Network Chapter 11 Research on Product Redesign Process Based on Functional Analysis Chapter 16 Thermodynamic Lubrication Performance and Stability for a Deep/Shallow Pocket Hybrid Bearing Considering Bubbly Oil

Introduction Gears are important parts of almost every operating mechanism in many industries The gear condition monitoring is an important unit of condition monitoring of a mechanism as a whole The vibration is one of the most used sources of information for equipment technical diagnostics Traditionally vibration is measured by accelerometers which are fixed on the mechanism body

Rolling bearing faults are among the primary causes of breakdown in multistage centrifugal pump A novel method of rolling bearings fault diagnosis based on variational mode decomposition is presented in this contribution The rolling bearing fault signal calculating model of different location defect is established by failure mechanism analysis and the simulation vibration signal of the

2016-2-2In this paper we propose a novel fault diagnosis method using the spectrum image of vibration signal as the feature The spectrum images of normal bearings and faulty bearings are obtained based on the fast Fourier transformation (FFT) of vibration signals where

An intelligent fault diagnosis method of rolling element bearing based on acoustic imaging and Gabor wavelet transform 25th International Congress on Sound and Vibration Hiroshima Japan 2018 p 2684-2691 Grządziela A Musial J Muślewski Ł Pajak M

Advances in Mechanical Engineering 2019 Vol 11(3) 1–13

2019-10-30Bearing fault diagnosis attracts great attention because the bearing condition has direct effects on productivity and safety in industry To accurately identify the operating condition of bearings a novel bearing fault diagnosis method based on

2015-10-1A novel ball bearing fault diagnosis method based on Integral Extension Local Mean Decomposition (IELMD) is proposed • The integral extension can suppress the end effects of LMD • Both the left and right side extreme points are extended • The ball bearing fault diagnosis experiment improved the efficiency and validity of the new IELMD

2019-7-6We propose a novel intelligent fault diagnosis approach based on principal component analysis (PCA) and deep belief network (DBN) techniques By adopting PCA technique the dimension of raw bearing vibration signals is reduced and the bearing fault features are extracted in terms of primary eigenvalues and eigenvectors

Abstract Nowadays Deep Learning is the most attractive research trend in the area of Machine Learning With the ability of learning features from raw data by deep architectures with many layers of non-linear data processing units Deep Learning has become a promising tool for intelligent bearing fault diagnosis This survey paper intends to provide a systematic review of Deep Learning based

DOI: 10 3390/s18051429 Corpus ID: 13682882 A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network article{Guo2018ANF title={A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network} author={Sheng Guo and Tao Yang and Wei Gao and Chen Zhang} journal={Sensors (Basel Switzerland)} year={2018} volume={18} }

2020-5-1A novel fault diagnosis method for rolling bearing using TV HVG and MD is proposed Abstract The total variation on graph (TV G ) is a powerful vertex domain index for measuring the smoothness of graph signals but its performance is closely related to the underlying graph

Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis However little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs

In order to solve the problem of correctly identifying incipient fault for electromechanical equipment and improve classification ability a novel method of incipient fault intelligent diagnosis based on lifting wavelet package transform (LWPT) and support vector

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform their fault diagnosis has attracted considerable research attention Established fault feature extraction methods focus on statistical characteristics of the vibration signal which is an approach that loses sight

Rolling bearings are vital components in rotary machinery and their operating condition affects the entire mechanical systems As one of the most important denoising methods for nonlinear systems local projection (LP) denoising method can be used to reduce noise effectively Afterwards high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve

A novel approach to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented This method is based on order tracking empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique

An intelligent fault diagnosis method of rolling element bearing based on acoustic imaging and Gabor wavelet transform 25th International Congress on Sound and Vibration Hiroshima Japan 2018 p 2684-2691 Grządziela A Musial J Muślewski Ł Pajak M