rolling element bearing weak fault diagnosis based on

Rolling

rolling-element bearing fault diagnosis based on traditional LeNet-5 network this paper proposes a novel rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network which can provide a rolling-element bearing fault diagnosis model with high classification accuracy fast convergence speed and strong generalization ability

1733 Rolling element bearings fault diagnosis based on

Keywords: rolling element bearing fault diagnosis kurtogram correlated kurtosis kurtosis envelope analysis 1 Introduction As a hot research topic condition based maintenance (CBM) [1] attracted more and more researchers It mainly contains fault diagnosis fault prognosis (remaining useful lifetime prediction) and maintenance decision

Rolling Element Bearing Fault Diagnosis Based on Adaptive

The fault feature of rolling element bearings in early failure period is so weak and susceptible to random noise that it is very difficult to be extracted so combined adaptive local iterative filtering decomposition (ALIFD) with Teager–Kaiser energy operator (TKEO) for rolling element bearings fault diagnosis This experiment provides access to bearing test data for faulty bearings and

Mechanical Systems and Signal Processing

Rolling element bearing Fault diagnosis Wavelet packet transform Low signal-to-noise ratio abstract The Kurtogram is based on the kurtosis of temporal signals that are filtered by the short-time Fourier transform (STFT) and has proved useful in the diagnosis of bearing faults

Research on rolling bearing fault diagnosis based on multi

After fault feature extraction a pattern recognition technique is required to achieve the rolling element bearing fault diagnosis automatically Nowadays a variety of pattern recognition methods have been used in mechanical fault diagnosis of which the most widely used are the support vector machines (SVMs) [ 21 ] and artificial neural

Research on rolling element bearing fault diagnosis based

In order to solve the problem of slow computation speed matching pursuit algorithm is applied to rolling bearing fault diagnosis and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms To be specific Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary and the

Fault feature extraction of rolling element bearings using

Mar 31 2016Based on sparse representation theories a new approach for fault diagnosis of rolling element bearing is proposed The over-complete dictionary is constructed by the unit impulse response function of damped second-order system whose natural frequencies and relative damping ratios are directly identified from the fault signal by correlation

Research on rolling bearing fault diagnosis based on multi

After fault feature extraction a pattern recognition technique is required to achieve the rolling element bearing fault diagnosis automatically Nowadays a variety of pattern recognition methods have been used in mechanical fault diagnosis of which the most widely used are the support vector machines (SVMs) [ 21 ] and artificial neural

Rolling Element Bearing Fault Diagnosis

This example shows how to perform fault diagnosis of a rolling element bearing based on acceleration signals especially in the presence of strong masking signals from other machine components The example will demonstrate how to apply envelope spectrum analysis and spectral kurtosis to diagnose bearing faults and it is able to scale up to Big

Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal

Rolling bearing faults appear with a great incidence due to its complexity and poor working conditions and bearing signals are usually drowned by noises in practice which make fault diagnosis difficult [1 2] Therefore it becomes an important point to reduce the interferences of noise more effectively which has attracted great attention

Rolling Element Bearing Fault Diagnosis Using Laplace

May 24 2007Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum Al-Raheem Khalid Roy Asok Ramachandran K Harrison D Grainger Steven 2007-05-24 00:00:00 The bearing characteristic frequencies (BCF) contain very little energy and are usually overwhelmed

Research on Rolling Element Bearing Fault Diagnosis Based

In order to extract the faint fault information from complicated vibration signal of bearing a new feature extraction method based on singular value decomposition (SVD) and kurtosis criterion is proposed in my work According to the method a group of component signals are obtained firstly using SVD then component signals with equal kurtosis are selected to be summed together and the weak

The Fault Diagnosis of Rolling Bearing Based on Ensemble

Jan 01 2017[2] X Zhang and J Zhou Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines Mechanical Systems and Signal Processing vol 41 no 1-2 pp 127-140 2013

Rolling Element Bearing Fault Diagnosis under Impulsive

Rolling element bearings are widely used in various industrial machines Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency Although proved to be a powerful method in detecting the resonance band excited by faults the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise

Experimental

Apr 27 2016In this paper an innovative system for condition-based monitoring (CBM) using model-based estimation (MBE) and artificial neural network (ANN) is proposed Fault diagnosis of deep groove ball bearings (DGBB) is a key machine element for stability of rotating machinery

Weak Fault Feature Extraction of Rolling Bearings Based on

1 Introduction Rolling bearings are one of the most common but the most vulnerable parts in mechanical systems In order to ensure uninterrupted operation and avoid unnecessary losses caused by sudden failure extraction of weak fault failures of rolling bearings has become a key factor to condition monitoring and fault diagnosis concerning mechanical systems [1 2]

Fault Diagnosis Based on Acoustic Emission Signal for Low

The fault of a bearing may cause the breakdown of a rotating machine leading to serious consequences A rolling element bearing is an important part of and is widely used in rotating machinery Therefore fault diagnosis of rolling bearings is important for guaranteeing production efficiency and plant safety Although many studies have been carried out with the goal of achieving fault

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

Incipient fault diagnosis of rolling element bearing based

Incipient fault diagnosis of rolling element bearing based on wavelet rolling element bearing is weak and is often submerged in noise it is usually difficult to find out the signal energy based on mechanical and physical considerations It has been successfully used in various applications Energy operator is generally

Weak fault diagnosis for rolling element bearing based on

Weak fault diagnosis for rolling element bearing based on MED-EEMD Download Article: Download (PDF 827 kb) which could be implemented based on minimizing the problems of mode aliasing The analyzed results demonstrate that the proposed method is an effective approach in identifying weak fault feature under strong background noise of

Imbalanced Fault Diagnosis of Rolling Bearing Based on

Due to the real working conditions and data acquisition equipment the collected working data of bearings are actually limited Meanwhile as the rolling bearing works in the normal state at most times it is easy to raise the imbalance problem of fault types which restricts the diagnosis accuracy and stability To solve these problems we present an imbalanced fault diagnosis method based on

Low

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures However under low-speed operating conditions the diagnosis of bearing components remains a problem In this paper the adaptive resilient stacked sparse autoencoder (ArSSAE) is proposed to compensate for the

A fault diagnosis methodology for rolling element bearings

For rolling element bearings vibration-based fault diagnosis is the most popular strategy This strategy is based on the analysis of vibration signals acquired from bearing housings Many techniques have been developed for analysing bearing vibration signals and for the purpose of fault diagnosis

Research on Rolling Element Bearing Fault Diagnosis Based

In order to extract the faint fault information from complicated vibration signal of bearing a new feature extraction method based on singular value decomposition (SVD) and kurtosis criterion is proposed in my work According to the method a group of component signals are obtained firstly using SVD then component signals with equal kurtosis are selected to be summed together and the weak

Rolling element bearing weak fault diagnosis based on

R OLLING ELEMENT BEARING WEAK FAULT DIAGNOSIS BASED ON SPATIAL CORRELATION AND ALIFD L EI Z HAO Y ONGXIANG Z HANG D ANCHEN Z HU 558 JOURNAL OF V IBROENGINEERING M AY 2020 V OLUME 22 ISSUE 3 of MATLAB The time domain waveform of original signal and added noise signal are shown in Fig 1 The signal is decomposited into 5 level to