Experimental Investigation for Distributed Defects in Ball Bearing Using Vibration Signature Analysis

Wadkar, S.B.

doi:10.1016/j.proeng.2016.05.086

Cited by 20 publications

(10 citation statements)

References 10 publications

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“…As the development of new and advanced materials are continuous process and the application for such type of material by replacing the existing materials are the key concern at present scenario for design and development of any mechanical elements. [3,4]. M.S.…”

Section: Figure: 1 Radial and Thrust Bearings [1]mentioning

confidence: 99%

Comparative Analysis of Sn–Pb–Sb Babbitt bearing alloys material with and without copper

2021

IJATCSE

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The most frequently used bearing is the deep groove ball bearing. It is found in almost all kinds of products in general mechanical engineering. Babbitts, also known as white metals, are either tin or lead-base alloys having excellent embed abilityand conformability characteristics. The Babbitts,are among the most widely used materials for lubricated bearings.The development of new advanced material is an important activity for continues progress in science and technology. Considerable research and development efforts are underway towards the development of tin-based bearing alloys. The effect of Copper in Babbittmaterial is considered as the Bearing Materials for comparative analysis in between the Babbittshaving copper and doesn’t. A Numerical analysis has been carried out for constant speed and at different bearingloads. The comparative analysis is based on the parameters like Elastic Strain, Maximum Principal Elastic Strain, Maximum Shear Elastic Strain, Equivalent Stress (MPa), Maximum Principal Stress (MPa), Maximum Shear Stress (MPa), Stress Ratio, Strain Energy (J) and Safety Factor. A structural analysis has been carried out. The design considers for the study of SKF6003 Bearings with the maximum load carrying capacity of 35MPa.As the results it has been find out that the copper mixing of 5% in Babbitt consider for study is profitable for enhancing the material property for the bearing applications

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Section: Figure: 1 Radial and Thrust Bearings [1]mentioning

confidence: 99%

Comparative Analysis of Sn–Pb–Sb Babbitt bearing alloys material with and without copper

2021

IJATCSE

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“…Failure analysis of the bearing can be investigated by making artificial defects on various elements of the bearing and analyzing it with a vibration signature tool for monitoring its condition [15]. Usually, life testing under normal operating conditions of mechanical parts with high reliability is expensive in terms of both capital and time.…”

Section: Literature Review and Background Studymentioning

confidence: 99%

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Gopikuttan

Veettil²,

Govindan³

2022

Adv. technol. innov.

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As per ISO-10816, electric motors up to 15 kW are classified as Class I machines, and the major reason for their failure is that the vibrations in them are above the alert limit. This study presents a new model for predicting the condition-based maintenance (CBM) initiation points through vibration measurement in a system of Class I machines. The proposed model follows the accelerated life testing (ALT) procedure. ALT includes the formation of an artificial wear environment in bearings to analyze the resultant system vibrations on system availability. The artificial wear environment created is close to the real industrial situation. The results show that the prediction of the CBM initiation points is based on the established relation between the system availability and vibrations. Furthermore, a relation between the available time for maintenance initiation and different vibration velocities is demonstrated.

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“…Defective bearings cause vibrations, therefore these vibration signals can be used to evaluate them. The first defect detection approaches consisted of the analysis of vibration signals extracted from the machine [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling element bearings using artificial neural network

Souad

Bendiabdellah

Meradi

2020

IJECE

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Bearings are essential components in the most electrical equipment. Procedures for monitoring the condition of bearings must be developed to prevent unexpected failure of these components during operation to avoid costly consequences. In this paper, the design of a monitoring system for the detection of rolling element-bearings failure is proposed. The method for detecting and locating this type of fault is carried out using advanced intelligent techniques based on a Perceptron Multilayer Artificial Neural Network (MLP-ANN); its database uses statistical indicators characterizing vibration signals. The effectiveness of the proposed method is illustrated using experimentally obtained bearing vibration data, and the results have shown good accuracy in detecting and locating defects.

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Experimental Investigation for Distributed Defects in Ball Bearing Using Vibration Signature Analysis

Cited by 20 publications

References 10 publications

Comparative Analysis of Sn–Pb–Sb Babbitt bearing alloys material with and without copper

Comparative Analysis of Sn–Pb–Sb Babbitt bearing alloys material with and without copper

Maintenance Initiation Prediction Incorporating Vibrations and System Availability

Fault diagnosis of rolling element bearings using artificial neural network

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