imbalanced fault diagnosis of rolling bearing based
Rolling bearing fault convolutional neural network
May 30 2020Affected by the transmission path it is very difficult to diagnose the vibration signal of the rolling bearing on the aircraft engine casing A fault diagnosis method based on convolutional neural network is proposed for the weak vibration signal of the casing under the excitation of rolling bearing fault Firstly the processing method of vibration signal is studied Through comparison and
Fault diagnosis of EMU rolling bearing based on EEMD and
May 23 2018Rolling bearing is an important and fragile component in the EMU To give a safe condition assessment of rolling bearing especially for early fault diagnosis is very necessary and become an urgent thing to the EMU A fault detection and diagnosis method based on EEMD sample entropy and SVM is proposed in this paper
Early fault detection and diagnosis in bearings based on
Mech anical wear and defective bearings can cause machinery to decrease its reliability and efficiency Therefore it is very important to detect their faults in an early stage in order to assure a safe and efficient operation We present a new technique for an early fault detection and diagnosis in rolling-element bearings based
Fault diagnosis of rolling bearing vibration based on
Particle swarm optimization is introduced to select the parameters of RBF neural network In the paper particle swarm optimization and RBF neural network method is applied to fault diagnosis of rolling bearing Finally the result of fault diagnosis cases shows high classification diagnostic accuracy in fault diagnosis of rolling bearing
Fault diagnosis for rolling bearing based on VMD
Apr 01 2020Fault diagnosis based on VMD-FRFT has been successfully applied to the rolling bearing of running parts as shown in Fig 26 The proposed approach can also be widely used in the fault diagnosis of rotating machinery in manufacturing industry machinery industry transportation industry and so on
Incipient fault diagnosis of rolling element bearing based
Incipient fault diagnosis of rolling element bearing based on wavelet packet transform and energy operator Zhongqing Wei1 Jinji Gao1 Xin Zhong2 Zhinong Jiang1 Bo Ma1 1Diagnosis and Self-Recovery Engineering Research Center Beijing University of Chemical Technology
Deep neural networks
Deep neural networks-based rolling bearing fault diagnosis Autores: Zhiqiang Chen Shengcai Deng Xudong Chen Chuan Li Ren-Vinicio Sanchez Huafeng Qin Localizacin: Microelectronics reliability ISSN 0026-2714 N 75 2017 pgs 327-333 Idioma: ingls Resumen Abstract Rolling bearing is one of the most commonly used components in rotating machinery
Bearing Problems: Fault Frequency and AI
Bearing Problems – Fault Frequency and Artificial Intelligence-Based Methods The fault diagnosis of rotating machines plays an important role to reduce the catastrophic failures and production lost tonnage Associated with rolling-element bearings are one of the key elements to follow
Weak fault diagnosis of rolling bearing based on FRFT and
the fault diagnosis of rolling bearing and the parame-ters are pre-set by prior knowledge where the quality of the parameter selection often has a great impact on the results The Mahalanobis distance is based on the distri-bution of features throughout the space as a basis for discrimination but the sample classification effect is not
Fault Diagnosis of Rolling Bearing Based on Kurtosis
Theoretical analysis and examination were made and it indicates that present statistical parameters of time domain of rolling bearing vibration like kurtosis index can only provide the type information about a rolling bearing in normal instead of more information The feature spectrum of fault rolling bearing can be obtained by decomposing the vibration signal through wavelet packet analysis
A Rolling Element Bearing Fault Diagnosis Approach Based
Dec 30 2016Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery leading to huge economic loss Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals a novel rolling element bearing fault diagnosis method based on multifractal theory and
Fault diagnosis for rolling bearing based on SIFT
Mar 06 2017The purpose of this paper is to propose a fault diagnosis method for rolling bearings in which the fault feature extraction is realized in a two-dimensional domain using scale invariant feature transform (SIFT) algorithm This method is different from those methods extracting fault feature directly from the traditional one-dimensional domain The vibration signal of rolling bearings is first
Fault Diagnosis of Rolling Bearing Based on Time Waveform
In this paper a fault in rolling bearing is diagnosed using time waveform analysis In order to verify the ability of time waveform analysis in fault diagnosis of rolling bearing an artificial fault is introduced in vehicle gearbox bearing: an orthogonal placed groove on
Weak Fault Diagnosis of Rolling Bearing Based on
Weak Fault Diagnosis of Rolling Bearing Based on Improved 573 using a fully connected neural network achieve fault diagnosis In order to verify the experimental results Western Reserve University bearing vibration failure data was used as a research object the fault diagnosis performance of the extracted feature[Zhao Wu
A Novel GAN
approach we test it on rolling bearing data from Case Western fault diagnosis One is based on the analysis of the failure mechanism which needs one to be familiar with structure of the monitored component vibration mode fault performance To solve the imbalanced industrial data for fault diagnosis
Fault Diagnosis of Rolling Bearings Using Data Mining
Rolling bearings are key components in most mechanical facilities hence the diagnosis of their faults is very important in predictive maintenance Up to date vibration analysis has been widely used for fault diagnosis in practice
Fault Diagnosis Method Based on a New Supervised Locally
Fault Diagnosis Method Based on a New Supervised Locally Linear Embedding Algorithm for Rolling Bearing HONGFANG YUAN 1 XUE ZHANG 2 YANGYANG3 HUAQING WANG 3* 1 College of Information Science and Technology Beijing University of Chemical Technology Chao Yang District Beijing 100029 P R CHINA
A Rolling Element Bearing Fault Diagnosis Approach Based
Dec 30 2016Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery leading to huge economic loss Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals a novel rolling element bearing fault diagnosis method based on multifractal theory and
Automatic Fault Diagnosis of Rolling Element Bearings
Automatic Fault Diagnosis of Rolling Element Bearings Using Wavelet Based Pursuit Features Hongyu Yang Bachelor of Engineering (DUT)* Master of Engineering (DUT) * Dalian University of Technology China Thesis submitted in total fulfilment of the requirements of
Application of Wavelet Analysis and Neural Network in
In this paper a fault-diagnosis method is proposed for generator rolling bearings based on wavelet packet analysis and neural network Acquisition of wind farm rolling bearings real-time signal under different conditions Firstly decomposes vibration acceleration signals use wavelet packets analysis make the original vibration signal decomposed into
(PDF) FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED
A fault identification method of rolling bearing based on depth belief network is proposed which does not need to extract fault features in advance Vibration signal is directly used as the input of the whole system Fault feature extraction and
Fault Diagnosis of Rolling Bearings Based on a Residual
Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention However in actual industrial environments most rolling bearings work under severe working conditions of variable speed and strong noise which makes the performance of many intelligent fault diagnosis methods deteriorate sharply In this regard this paper proposes a new intelligent diagnosis
Fault severity diagnosis of rolling element bearings based
Gu Fengshou Tian Xiange Chen Zhi Wang Tie Rehab Ibrahim and Ball Andrew (2014) Fault severity diagnosis of rolling element bearings based on kurtogram and envelope analysis In: Proceedings of the International conference on advances in civil Structural and Mechanical Engineering Institute of Research Engineers and Doctors
Rolling bearing fault diagnosis of PSO–LSSVM based on
Taking aim at the nonstationary nonlinearity of the rolling bearing vibration signal a rolling bearing fault diagnosis method based on the entropy fusion feature of complementary ensemble empirical mode decomposition (CEEMD) is proposed in combination with information fusion theory
A Compound Fault Diagnosis for Rolling Bearings Method
Oct 07 2014To solve this problem a novel method of compound fault diagnosis for the rolling bearing based on EEMD method and blind source separation is presented in this paper In order to verify the efficiency of the proposed methods several experiments of bearing fault are performed







