scholarly journals Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jing Tian ◽  
Yanting Ai ◽  
Chengwei Fei ◽  
Ming Zhao ◽  
Fengling Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearings ◽  
Distance Method ◽  
Ring Fault ◽  
Wavelet Space ◽  
Rolling Element ◽  
Coupling Fault ◽  
Inner Race ◽  
Ae Signals ◽  
Space Spectrum

For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, wavelet energy spectrum exergy, and wavelet space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.

scholarly journals Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Yuan ◽  
Yong Lv ◽  
Gangbing Song
Keyword(s):  
Fault Diagnosis ◽  
Numerical Simulations ◽  
Rolling Bearing ◽  
High Order ◽  
Rolling Bearings ◽  
Theoretical Derivation ◽  
Denoising Method ◽  
Vibration Signals ◽  
Local Projection ◽  
Ring Fault

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 complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.

Fault Diagnosis Method for the Train Axle Box Bearing Based on KPCA and GA-SVM

2013 ◽  
Vol 441 ◽  
pp. 376-379 ◽  
Author(s):  
Ci Wang ◽  
Li Min Jia ◽  
Xiao Feng Li
Keyword(s):  
Fault Diagnosis ◽  
Critical Role ◽  
Principal Component ◽  
Nonlinear Process ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Ring Fault ◽  
Rolling Element ◽  
Diagnosis Method ◽  
Train Safety

Online fault diagnosis for the train axle box bearings is a wide and important study topic since it plays a critical role in train safety. Due to the vibration signals nonlinear and non-stationary characteristics, accuracies of the methods such as neural network and hierarchical clustering are less than 90% which are not satisfying. In this paper, kernel principal component analysis (KPCA), a nonlinear process technique, was to tackle each signals 18 feature parameters for extracting the main features to reflect the signal characteristics. Then, in fault pattern recognition, support vector machine (SVM) based on genetic algorithm (GA) was used to identify the current fault type of the bearings, including normal, outer ring fault, inner ring fault and rolling element fault. The results show that the prediction accuracy of GA-SVM method reaches to 96.33%, which is quite effective.

Dynamic asymptotic model of rolling bearings with a single fault in the inner raceway

2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110522
Author(s):  
Yunlong Li ◽  
Zhinong Li ◽  
Danyang Tian ◽  
Junyong Tao
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Analysis ◽  
Reliability Analysis ◽  
Dynamic Characteristics ◽  
Calculation Method ◽  
Asymptotic Model ◽  
Rolling Bearings ◽  
Single Fault ◽  
Theoretical Support ◽  
Rolling Element

In the previous models of rolling bearings with a single fault, the displacement deviation caused by the collision of the fault to the rolling element changes instantly. However, the displacement deviation should change gradually. Here, the asymptotic idea is introduced to describe the change of the displacement deviation. The calculation method of the deviation is given. An asymptotic model of rolling bearings with an inner raceway fault is constructed. Then, the simulation of the SKF6205 bearing with a single fault is carried out. The differences between the previous model and the asymptotic model for the responses and the displacement deviation are compared. The effects of the speed and fault size on the dynamic characteristics are analyzed. Finally, the experiments are carried out to corroborate the rationality of the constructed model. The research results can provide theoretical support for the dynamic analysis, fault diagnosis, and reliability analysis of rolling bearings.

Fault Diagnosis of Bearings Based on Time-Delayed Correlation Demodulation and Fuzzy Clustering

2011 ◽  
Vol 211-212 ◽  
pp. 510-514 ◽  
Author(s):  
Pan Fu ◽  
Wei Lin Li ◽  
Wei Qing Cao
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Failure Diagnosis ◽  
Rolling Element Bearing ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Set Up

As one of the most common parts of various rolling mechanical equipments, rolling element bearing is vulnerable. Therefore, great attentions have been attributed to the theories, failure diagnosis methods and their applications for rolling bearings. Vibration analysis is also a very important means for bearing fault diagnosis. This paper aims at the research on the methods of signal processing and pattern recognition. An experimental platform was set up for the failure diagnosis of rolling bearings, on which we have done a lot of experiments. Then the vibration signals of normal rolling bearings, rolling bearings with failure on the outer and inner race were collected. Time-delayed correlation demodulation was applied and the features of vibration signal were effectively extracted. Fuzzy C-means clustering system was established to carry out the recognition of the fault of bearings. Experimental results have proved the developed fault diagnostic architecture is reliable and effective.

Fault Diagnosis of Ball Bearings Using Soft Computing

2009 ◽  
Author(s):  
Kalyan M. Bhavaraju ◽  
P. K. Kankar ◽  
Satish C. Sharma ◽  
S. P. Harsha
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Vibration Spectrum ◽  
Support Vector ◽  
Outer Race ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Vector Machines ◽  
Rolling Element ◽  
Inner Race

This paper presents the condition monitoring and fault diagnosis of rolling element bearings using Support Vector Machines (SVM). The vibration response of healthy bearings and bearings with various component defects such as outer race, inner race, balls and their combination have been analyzed. From the obtained vibration spectrum, it is clearly seen that a discrete peak of excitation appeared for the specific defect of bearings. In this paper, various faults of the bearings has been simulated and classified. The process includes, data acquisition, feature extraction from time response and a knowledge based system to classify faults. Features defining feature vectors are formed using statistical techniques and are fed as input to the support vector machine (SVM) classifiers. Knowledge based system developed for classification can be used for automatic recognition of machinery faults based on feature vector.

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

2017 ◽  
Vol 17 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Yat-Sze Choy ◽  
Guang-Chen Bai ◽  
Wen-Zhong Tang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Distance Method ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Entropy Distance

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

Research on Fault Diagnosis and Application for Rolling Element Bearing Based on Fuzzy Analysis

2011 ◽  
Vol 189-193 ◽  
pp. 1358-1361 ◽  
Author(s):  
Xiao Guang Yu ◽  
Jian Liu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Amplitude Spectrum ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Ring Fault ◽  
Rolling Element ◽  
Rolling Body ◽  
Element Bearing ◽  
Fuzzy Diagnosis

In order to simulate the fault of a single rolling bearing, this paper used the fault diagnosis lab desk to simulate some representative conditions of 30205 type of rolling element bearing, including regular condition, inner ring fault, outer ring fault and rolling body fault. It also adopted fuzzy theory to analyze signals and diagnose faults on the base of the MATLAB software desk. This paper also adopts the amplitude spectrum, the power spectrum, the envelope demodulated spectrum and the delayed correlation-envelop spectrum to diagnosis and analyze simulating signals. Comparatively, the fuzzy diagnosis theory is dependable．

A Theoretical Model to Predict Vibration Response of Rolling Bearings to Distributed Defects Under Radial Load

1998 ◽  
Vol 120 (1) ◽  
pp. 214-220 ◽  
Author(s):  
A. Choudhury ◽  
N. Tandon
Keyword(s):  
Theoretical Model ◽  
Discrete Spectrum ◽  
Analytical Model ◽  
Relative Frequency ◽  
Vibration Response ◽  
Radial Load ◽  
Rolling Bearings ◽  
Rolling Element ◽  
Amplitude Level ◽  
Inner Race

An analytical model has been presented to predict the vibration response of rolling bearings due to distributed defects under radial load. For bearings without defect and with race defect, the model predicts a discrete spectrum with components at outer and inner race characteristic defect frequencies for the response of the respective races. The amplitude level for race defect significantly increases at the respective frequencies in comparison to the response of a bearing without defect. For a bearing with off-size rolling element, the response is at the relative frequency of cage with respect to the frequency of motion of the corresponding race.

Sign in / Sign up

Export Citation Format

Share Document