scholarly journals Development of an Indicator for the Assessment of Damage Level in Rolling Element Bearings Based on Blind Deconvolution Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Marco Buzzoni ◽  
Elia Soave ◽  
Gianluca D’Elia ◽  
Emiliano Mucchi ◽  
Giorgio Dalpiaz
Keyword(s):  
Blind Deconvolution ◽  
Degradation Process ◽  
Rolling Element Bearings ◽  
Damage Level ◽  
Outer Race ◽  
Detection And Identification ◽  
Rolling Element ◽  
Vibration Signature ◽  
Localized Faults ◽  
Condition Indicator

The monitoring of rolling element bearings through vibration-based condition indicators plays a crucial role in the modern machinery. The kurtosis is a very efficient indicator being sensitive to impulsive components within the vibration signature that often are symptomatic of localized faults. In order to improve the diagnostic performance of the kurtosis, blind deconvolution algorithms can be exploited in order to detect bearing faults and, most importantly, their position. In this scenario, this paper focuses on the development of a novel condition indicator specifically designed for the damage assessment in rolling element bearings. The proposed indicator allows to track the bearing degradation process taking into account three different possible positions: outer race, inner race, and rolling element. This indicator fits real-time monitoring procedures allowing for the automatic detection and identification of the bearing fault. The validation of the proposed indicator has been carried out by means of both simulated signal and a run-to-failure experiment. The results highlight that the proposed indicator is able to detect more efficiently the fault occurrence and, most importantly, quicker than other established techniques.

Dynamic Modeling of Rolling Element Bearings With Surface Contact Defects Using Bond Graphs

2010 ◽  
Vol 133 (1) ◽  
Author(s):  
Mohsen Nakhaeinejad ◽  
Michael D. Bryant
Keyword(s):  
Three Dimensional ◽  
Surface Profile ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Bond Graphs ◽  
Outer Race ◽  
Proposed Model ◽  
Rolling Element ◽  
Type Size ◽  
Localized Faults

Multibody dynamics of healthy and faulty rolling element bearings were modeled using vector bond graphs. A 33 degree of freedom (DOF) model was constructed for a bearing with nine balls and two rings (11 elements). The developed model can be extended to a rolling element bearing with n elements and (3×n) DOF in planar and (6×n) DOF in three dimensional motions. The model incorporates the gyroscopic and centrifugal effects, contact elastic deflections and forces, contact slip, contact separations, and localized faults. Dents and pits on inner race and outer race and balls were modeled through surface profile changes. Bearing load zones under various radial loads and clearances were simulated. The effects of type, size, and shape of faults on the vibration response in rolling element bearings and dynamics of contacts in the presence of localized faults were studied. Experiments with healthy and faulty bearings were conducted to validate the model. The proposed model clearly mimics healthy and faulty rolling element bearings.

Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Time Domain ◽  
Central Moment ◽  
Rolling Element Bearings ◽  
Test Rig ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Common Time ◽  
Rolling Element

Inspired by previous achievements, different time-domain features for diagnosis of rolling element bearings are investigated in this study. An experimental test rig is prepared for condition monitoring of angular contact bearing by using an acoustic emission sensor for this purpose. The acoustic emission signals are acquired from defective bearing, and the sensor takes signals from defects on the inner or outer race of the bearing. By studying the literature works, different domains of features are classified, and the most common time-domain features are selected for condition monitoring. The considered features are calculated for obtained signals with different loadings, speeds, and sizes of defects on the inner and outer race of the bearing. Our results indicate that the clearance, sixth central moment, impulse, kurtosis, and crest factors are appropriate features for diagnosis purposes. Moreover, our results show that the clearance factor for small defects and sixth central moment for large defects are promising for defect diagnosis on rolling element bearings.

Simultaneous Monitoring of Rolling-Element and Journal Bearings Using Analysis of Structure-Born Ultrasound Acoustic Emissions

2010 ◽  
Author(s):  
A. Albers ◽  
M. Dickerhof
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emissions ◽  
Piezoelectric Sensor ◽  
Journal Bearings ◽  
Rolling Element Bearings ◽  
Acoustic Emission Sensor ◽  
Outer Race ◽  
Rolling Element ◽  
Characteristic Values ◽  
Acoustic Emission Technology

The application of Acoustic Emission technology for monitoring rolling element or hydrodynamic plain bearings has been addressed by several authors in former times. Most of these investigations took place under idealized conditions, to allow the concentration on one single source of emission, typically recorded by means of a piezoelectric sensor. This can be achieved by either eliminating other sources in advance or taking measures to shield them out (e. g. by placing the acoustic emission sensor very close to the source of interest), so that in consequence only one source of structure-born sound is present in the signal. With a practical orientation this is often not possible. In point of fact, a multitude of potential sources of emission can be worth considering, unfortunately superimposing one another. The investigations reported in this paper are therefore focused on the simultaneous monitoring of both bearing types mentioned above. Only one piezoelectric acoustic emission sensor is utilized, which is placed rather far away from the monitored bearings. By derivation of characteristic values from the sensor signal, different simulated defects can be detected reliably: seeded defects in the inner and outer race of rolling element bearings as well as the occurrence of mixed friction in the sliding surface bearing due to interrupted lubricant inflow.

Feature Selection for Fault Detection in Rolling Element Bearings Using Mutual Information

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
Karthik Kappaganthu ◽  
C. Nataraj
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Quantitative Measure ◽  
Rolling Element Bearings ◽  
Outer Race ◽  
Time Frequency ◽  
Rolling Element ◽  
Degree Of Confidence ◽  
Hidden Layer ◽  
Optimal Feature

Rolling element bearings are among the key components in many rotating machineries. It is hence necessary to determine the condition of the bearing with a reasonable degree of confidence. Many techniques have been developed for bearing fault detection. Each of these techniques has its own strengths and weaknesses. In this paper, various features are compared for detecting inner and outer race defects in rolling element bearings. Mutual information between the feature and the defect is used as a quantitative measure of quality. Various time, frequency, and time-frequency domain features are compared and ranked according to their cumulative mutual information content, and an optimal feature set is determined for bearing classification. The performance of this optimal feature set is evaluated using an artificial neural network with one hidden layer. An overall classification accuracy of 97% was obtained over a range of rotating speeds.

Bearing Signature Analysis as a Medium for Fault Detection: A Review

2007 ◽  
Vol 130 (1) ◽  
Author(s):  
M. S. Patil ◽  
Jose Mathew ◽  
P. K. RajendraKumar
Keyword(s):  
Fault Detection ◽  
Vibration Measurement ◽  
Signature Analysis ◽  
Rolling Element Bearings ◽  
Bearing Defects ◽  
Rolling Element ◽  
Vibration Signature ◽  
Recent Trends ◽  
Bearing Vibration ◽  
Detection And Diagnosis

Rolling element bearings find widespread domestic and industrial application. Defects in bearing unless detected in time may lead to malfunctioning of the machinery. Different methods are used for detection and diagnosis of the bearing defects. This paper is intended as a tutorial overview of bearing vibration signature analysis as a medium for fault detection. An explanation for the causes for the defects is discussed. Vibration measurement in both time domain and frequency domain is presented. Recent trends in research on the detection of the defects in bearings have been included.

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults

2003 ◽  
Vol 125 (3) ◽  
pp. 282-289 ◽  
Author(s):  
J. Antoni ◽  
R. B. Randall
Keyword(s):  
Spectral Characteristics ◽  
Vibration Signal ◽  
Spectral Signature ◽  
Rolling Element Bearings ◽  
Envelope Analysis ◽  
Analysis Technique ◽  
Proposed Model ◽  
Rolling Element ◽  
Excitation Force ◽  
Localized Faults

This paper addresses the stochastic modeling of the vibration signal produced by localized faults in rolling element bearings and its use for diagnostic purposes. The aim is essentially to provide a better understanding of the recognized “envelope analysis” technique as classically used in the diagnostics of rolling element bearings, and incidentally give theoretical proofs for the specific features of envelope spectra as obtained from experimental data. The proposed model may also prove useful for simulation purposes. First, the excitation force generated by a defect is modeled as a random point process and its spectral signature is derived analytically. Then its transmission through the bearing is investigated in detail in order to find the spectral characteristics of the resulting vibration signal. The analysis finally gives sound justification for “squared” envelope analysis and the type of spectral indicators that should be used with it.

An Automated Edge Computing-Based Condition Health Monitoring System: With an Application on Rolling Element Bearings

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Niklas Tritschler ◽  
Andrew Dugenske ◽  
Thomas Kurfess
Keyword(s):  
Monitoring System ◽  
Health Monitoring ◽  
Vibration Signal ◽  
Vibration Sensor ◽  
Vibration Measurement ◽  
Rolling Element Bearings ◽  
Health Monitoring System ◽  
Vibration Data ◽  
Rolling Element ◽  
Vibration Signature

Abstract A failure of rolling element bearings is a frequent cause of machine breakdowns and results in a production loss due to the sudden failure. A regular condition health monitoring and an associated detection of bearing defects in the early stages can be used to predict such sudden failures. To monitor the bearing's condition, the generated vibration signature can be analyzed, since rotating machines have, in most instances, a unique vibration signature that relates to their health status. Presently, bearing analysis of many machines results in significant cost and complexity due to a large amount of vibration data that must be analyzed. A condition health monitoring system (CMS) was developed to automate and simplify the whole process from the vibration measurement to the analysis results. Additionally, the CMS is embedded into an Internet of Things (IoT) architecture. Thereby, a location-independent control of the CMS, the vibration data, and the analysis results is possible. The embedding of sensors can cause communication problems from the sensor to the cloud due to the low bandwidth of sensors and the amount of data that must be transmitted. To overcome this issue, an edge device that acts as a gateway between the vibration sensor and the cloud is the core of the CMS. It measures the vibration signal locally, analyzes it automatically, and publishes a feedback as to the bearing condition to the cloud.

Cross-fuzzy entropy-based approach for performance degradation assessment of rolling element bearings

2017 ◽  
Vol 232 (2) ◽  
pp. 173-185 ◽  
Author(s):  
Keheng Zhu ◽  
Xigeng Song
Keyword(s):  
Life Time ◽  
Degradation Process ◽  
Performance Degradation ◽  
Fuzzy Entropy ◽  
Rolling Element Bearings ◽  
Global Trends ◽  
Global Trend ◽  
Rolling Element ◽  
Bearing Vibration ◽  
Made In

Exploring effective indicators is significant for the assessment of the bearing performance degradation, which is crucial to realize the condition-based maintenance. In this paper, the cross-fuzzy entropy is introduced and is used to measure the similarity of patterns between normal signals and tested signals of the rolling element bearings, and the degree of similarity is used as an indicator of the bearing performance degradation. The original cross-fuzzy entropy focuses on the local characteristics of the signal and neglects its global trend. However, the global characteristics and global trends of bearing vibration signals may vary as the bearings degrade gradually. Therefore, a change has been made in the implementation of the original cross-fuzzy entropy algorithm to overcome this limitation and the modified cross-fuzzy entropy is more suitable for reflecting the whole degradation process of rolling element bearings. The experimental results demonstrate that the modified cross-fuzzy entropy can assess the bearing performance degradation process over their whole life time clearly and effectively.

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