Estimation of the Surface Roughness on the Race or Balls of Ball Bearings by Vibration Analysis

This paper describes a vibration-based diagnostic method by estimating the surface roughness on the rotating ring or balls in ball bearings. The surface roughness has been measured by a stylus that directly traverses the surface of the ring or balls obtained by taking apart the ball bearing. We developed a new method to estimate accurately the surface roughness by analyzing the short-length vibration signal that is excited when balls encounter flaws on the rotating ring or when races encounter flaws on the balls in a ball bearing. Our experimental results confirm that the roughness estimated by the proposed method agrees with that measured directly by using a stylus even in the case of crack μm wide. We applied this new method to the diagnosis of surface roughness in small-sized ball bearings and inferior samples were detected with a 95.3 percent accuracy rate.

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Study of ball bearing fatigue damage using vibration analysis: application to thrust ball bearings

STRUCTURAL ENGINEERING AND MECHANICS ◽

10.12989/sem.2015.53.2.325 ◽

2015 ◽

Vol 53 (2) ◽

pp. 325-336 ◽

Cited By ~ 3

Author(s):

Toumi M. Yessine ◽

Bolaers Fabrice ◽

Bogard Fabien ◽

Murer Sebastien

Keyword(s):

Fatigue Damage ◽

Vibration Analysis ◽

Ball Bearing ◽

Ball Bearings ◽

Analysis Application

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Analysis of Roller/Ball Bearing Vibrations

Journal of Mechanical Design ◽

10.1115/1.3454009 ◽

1979 ◽

Vol 101 (1) ◽

pp. 118-125 ◽

Cited By ~ 68

Author(s):

S. Braun ◽

B. Datner

Keyword(s):

Spectral Analysis ◽

Diagnostic Method ◽

Search Strategy ◽

Ball Bearing ◽

Ball Bearings ◽

Periodic Functions ◽

Localized Defects

Described is a vibration based diagnostic method aimed at detecting localized defects developing in roller/ball bearings. The signature is decomposed into generalized periodic functions and a search strategy for the various components developed and implemented. The method is shown to be applicable for cases where spectral analysis fails to give results.

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Application of Vibration Analysis in Ball Bearing Fault Detection

Volume 1: 20th Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C ◽

10.1115/detc2005-84544 ◽

2005 ◽

Author(s):

F. Bakhtiary-Nejad ◽

A. H. Nayeb ◽

S. E. Yeganeh

Keyword(s):

Fault Detection ◽

Computer Program ◽

Time Domain ◽

Vibration Analysis ◽

Ball Bearing ◽

Detection Method ◽

Ball Bearings ◽

Test Results ◽

Localized Defects ◽

Set Up

In this paper, existence of localized defects in a ball bearing has been diagnosed using vibration analysis. First, different kinds of faults which occur in ball bearings have been investigated. Then an analytical model has been proposed for determining the damaged ball bearing vibrations due to a localized defect. Also various methods of fault detection have been evaluated. Next, in order to examine the ball bearings, a testing set-up has been designed and constructed. Then by preparing a computer program, which calculates defect frequencies, some ball bearings have been tested. The test results were originally derived in time-domain. Then by using vibration analysis of healthy and damaged ball bearings in frequency-domain, a fault detection method for ball bearings has been proposed.

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Automated vibration-based fault size estimation for ball bearings using Savitzky–Golay differentiators

Journal of Vibration and Control ◽

10.1177/1077546317723227 ◽

2017 ◽

Vol 24 (18) ◽

pp. 4297-4315 ◽

Cited By ~ 2

Author(s):

Mohamed AA Ismail ◽

Andreas Bierig ◽

Nader Sawalhi

Keyword(s):

Ball Bearing ◽

Research Effort ◽

Axial Loading ◽

Vibration Signal ◽

Accelerometer Data ◽

Ball Bearings ◽

Size Estimation ◽

Detection Stage ◽

German Aerospace ◽

Processing Techniques

Vibration-based fault diagnosis has been utilized as a reliable method for identifying ball bearings health since the 1970s. Recently, there has been an increased research effort to develop methods for fault quantification with the aim of estimating the fault size to allow the service life of a ball bearing to be extended beyond the detection stage. These studies have shown that the vibration signal from a localized spall (e.g. fatigue defect) in a ball bearing exhibits features corresponding to two main events, namely, the entry into and the exit from the spall. The time span between these two events is correlated with the spall size. Studies have shown that the entry into the spall is the more challenging event to identify, which often requires extensive signal processing techniques. This paper introduces an automated vibration-based technique for estimating the size of a spall in a ball bearing under axial loading conditions similar to those of linear electro-mechanical actuators. This technique is based on the extraction of the entry/exit events from the vibrational jerk, which are numerically determined from accelerometer data. The differentiation of the acceleration data to estimate jerk signal is performed using a variant of Savitzky–Golay (SG) differentiators, which provide enhancement for the detection of the entry and exit points. Sensible spall size estimations have been achieved for 24 different scenarios of fault sizes, rotor speeds and loads measured on a test rig provided by DLR (German Aerospace Center).

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BEARING FAULT DETECTION USING VIRTUAL–BASED ICA ALGORITHM

Fluctuation and Noise Letters ◽

10.1142/s0219477508005173 ◽

2008 ◽

Vol 08 (03n04) ◽

pp. L423-L433

Author(s):

K. REZAEI MOGHADAM ◽

A. MOHAJERIN ARIAEI ◽

M. H. KAHAEI ◽

J. POSHTAN

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Fault Detection ◽

Independent Component Analysis ◽

Vibration Signal ◽

Independent Component ◽

New Method ◽

Ball Bearings ◽

Bearing Fault ◽

Ica Algorithm

This paper presents a new method for fault diagnosis in ball-bearings using a combination of the Independent Component Analysis (ICA) and the Wavelet Transform. In the ICA, the number of sensors should be equal to the number of independent sources. We introduce a new method to replace the second vibration signal required by the ICA by a virtual one in order to increase the accuracy of the diagnosing system and also to simplify the system hardware. Using real and simulated signals, it is shown that the proposed algorithm outperforms the HFD algorithm.

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Accurate estimation of the surface roughness on the rolling ring in a ball bearing by vibration analysis.

Journal of the Acoustical Society of Japan (E) ◽

10.1250/ast.7.343 ◽

1986 ◽

Vol 7 (6) ◽

pp. 343-352 ◽

Cited By ~ 2

Author(s):

Hiroshi Kanai ◽

Masato Abe ◽

Ken'iti Kido

Keyword(s):

Surface Roughness ◽

Vibration Analysis ◽

Ball Bearing ◽

Accurate Estimation ◽

Rolling Ring

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Research on a Numerical Calculation for Ball Bearings Based on a Finite Initial Value Search Method

Mathematical Problems in Engineering ◽

10.1155/2021/6617131 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jing Hu ◽

XiaoLi Qiao ◽

QiongYing Lv ◽

XinMing Zhang ◽

XiaoPing Zhou

Keyword(s):

Ball Bearing ◽

New Method ◽

Ball Bearings ◽

Initial Value ◽

Double Decker ◽

Strategy Model ◽

Marquardt Algorithm ◽

Finite Solution ◽

Value Strategy

To address the disadvantages of the traditional solution method of the quasistatic ball bearing model, which requires the acquisition of the initial value with experience, this paper proposes a new method for a finite initial value optimization to solve the quasistatic ball bearing model. A bilevel solution model is established; the first layer is the initial value strategy model, and the second layer is the numerical optimization model. The Levenberg–Marquardt algorithm and the Armijo algorithm are adopted to solve the model. The correctness of the new method is proved by a case study of calculating a parameter of the bearing compared with the traditional method. The experiment results show that the proposed method can realize the fast and finite solution of the quasistatic ball bearing model. Additionally, the new method can be extended to the calculation of the double-decker ball bearings. It provides a reasonable and effective way for the exploration of the initial value problem of the solution of a quasistatic ball bearing model.

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Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797612666190530115739 ◽

2019 ◽

Vol 12 (3) ◽

pp. 248-261

Author(s):

Baomin Wang ◽

Xiao Chang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Temperature Rise ◽

High Speed ◽

Ball Bearing ◽

Influence Factors ◽

Ball Bearings ◽

Ann Model ◽

Angular Contact Ball Bearing ◽

Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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Friction Tests of Ball Bearings with Comparison of Lubricants

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.866.375 ◽

2017 ◽

Vol 866 ◽

pp. 375-378

Author(s):

Sathitbunanan Sumate ◽

Wirote Ritthong

Keyword(s):

Energy Efficiency ◽

Electric Power ◽

Ball Bearing ◽

Maximum Load ◽

Engine Oil ◽

Ball Bearings ◽

Hydraulic Oil ◽

Proper Size ◽

Transmission Fluid ◽

Moving Parts

The ball bearings are the rotating components which are widely spread to moving parts for all machinery operation in general industry. This paper presents the ball bearing resistance tool which has a proper size and can handle the maximum load of 300 kg by using an electric power. The ball bearing resistance tool was used to test the bearings No. 6011 cm. The series of tests was performed in the ball bearings lubricated; engine oil SAE 10W/40, auto transmission fluid Dexron, and hydraulic oil. The rotational speeds for testing were vary; 500, 600 and 700 rpm respectively. At each speed, there were various weight; 50, 70, 90,110,130,150, and 170 kg respectively. The results show that the hydraulic oil generated the smallest coefficients of friction and energy efficiency for ball bearing operation.

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