Fault detection and diagnosis on the rolling element bearing

Rezaei, Aida

doi:10.22215/etd/2007-08311

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…In the field of abnormalities and fault conditions of bearings, the researches have been conducted for the determination of the state after fault and analysis of fault conditions from the computational analysis [ 34 , 35 ], etc. In the field of bearing fault diagnosis and prognostics, studies have been conducted for fault diagnosis based on the comparison of the experimental data and the physical models [ 36 ], state investigation based on the detected signal analysis [ 37 ], and the diagnosis technology in accordance with fault simulation test data [ 38 ], etc. Finally, in order to diagnose and prognose the abnormal state of the bearing, it is necessary to obtain the result or data of the abnormal or the fault condition.…”

Section: Strategy Of Diagnosis Of Bearing Conditionmentioning

confidence: 99%

An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

Kim¹,

Oh²,

Min³

et al. 2021

Sensors

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In order to utilize wave energy, various wave power systems are being actively researched and developed and interest in them is increasing. To maximize the operational efficiency, it is very important to monitor and maintain the fault of components of the system. In recent years, interest in the management cost, high reliability and facility utilization of such systems has increased. In this regard, fault diagnosis technology including fault factor analysis and fault reproduction is drawing attention as an important main technology. Therefore, in this study, to reproduce and monitor the faults of a wave power system, firstly, the failure mode of the system was analyzed using FMEA analysis. Secondly, according to the derived failure mode and effect, the thrust bearing was selected as a target for fault reproduction and a test equipment bench was constructed. Finally, with the vibration data obtained by conducting the tests, the vibration spectrum was analyzed to extract the features of the data for each operating status; the data was classified by applying the three machine learning algorithms: naïve Bayes (NB), k-nearest neighbor (k-NN), and multi-layer perceptron (MLP). The criteria for determining the fault were derived. It is estimated that a more efficient fault diagnosis is possible by using the standard and fault monitoring method of this study.

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Section: Strategy Of Diagnosis Of Bearing Conditionmentioning

confidence: 99%

An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

Kim¹,

Oh²,

Min³

et al. 2021

Sensors

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show abstract

“…[1] It is usually possible to identify error avoidance as the method of detection and diagnosis of faults. [2] If a fault is found, fixes will be made quickly and complete security features will be restored. For situations where improvements cannot be carried out readily, alternate security is put into service and activities are moved to a secure, safe state until the fixes can be completed.…”

Section: Introductionmentioning

confidence: 99%

“…For situations where improvements cannot be carried out readily, alternate security is put into service and activities are moved to a secure, safe state until the fixes can be completed. [2] Nonetheless, both early detection and correct diagnosis of early defects lead to rapid unplanned maintenance and brief downtime for the system being regarded. [3] We often prevent the negative and sometimes shocking effects of errors or faults.…”

Section: Introductionmentioning

confidence: 99%

“…[3]Fault detection is the method of evaluating and matching calculated device details and system status data with an acceptable range of observable parameters to decide whether certain variables fell outside the scope reflecting the healthy state of the mechanism. [2] Detection of fault identifies the event of failure in the process being tracked. This consists in identifying faults in systems, actuators and sensors through the use of dependencies between various observable signals.…”

Section: Introductionmentioning

confidence: 99%

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Improved Technique to Diagnose Fault in IEEE Standard 14-Bus System

Amer*,

Selmy*,

Mohamed

2019

IJITEE

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This study presents a new technique for fast detecting and diagnosing of power grids faults. Discrete Wavelet transform (DWT) has a major disadvantage of noise sensitivity. The proposed technique solves the problems of DWT, where a highprecision classification of noisy and faulty signals could be obtained. Fusion between voltage and power readings is done to provide a more reliable and accurate decision to determine the exact location of the fault. In this technique, the learner classifier is used,and the system is trained for multiple situations where most faults may occur. All simulations were carried out and performed on the standard IEEE 14 bus system to check the efficiency and performance of the technique proposed. Simulation results demonstrate, as will be discussed, a strong effectiveness of the suggested approach relative to others. The main feature of the proposed technique is that it can differentiate between faulty and noisy signals and recognize the fault's location quickly and with high reliability

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“…Paper Machine Process quality performance and its turbulence root causes identification using online quality control system is presented by Chioua et al [2], while the present study is dealing with vibration measurement and analysis as a powerful tool to increase Machine Health and availability. An intelligent fault diagnosis system starting from data acquisition, followed by feature extraction, fault detection and identification is presented by Aherwar et al [3] , they focused on gearbox fault detection and diagnosis using different vibration analysis techniques, while Rezaei [4] presented bearings failure types, fault detection, diagnosis and analysis techniques. Also Riaz et al [5] presented vibration fault extraction and analysis techniques for rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Machine Health Monitoring Using Vibration Analysis

Nabby

Belal

Gomaa

2018

The International Conference on Applied Mechanics and Mechanica

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Vibration analysis is an effective tool to identify and predict failure using Signal Processing based on condition monitoring. Accordingly, machine faults can be detected and diagnosed using different post processing of vibration signature. The present study is to prove that Machine Health and availability achievement becoming easier by using different vibration analysis techniques. Three different types of nonstationary signals are addressed in this paper to evaluate the technical conditions of Paper Machine Forming Roll. Forming Roll consists of frequency drive motor, gearbox and a symmetric rotor supported on two similar bearings. The present study is focusing on Forming Roll Bearings Condition Monitoring. A cost wise comparison of maintenance strategies is made to confirm that Condition based Maintenance is the most cost-effective strategy. In the past portable data-collectors were used to measure vibration levels, but Condition based Monitoring had taken place to avoid any sudden fault developing before next time schedule. Applying different vibration analysis techniques such as Spectrum, Waveform and Impact Demodulation will support fault detection, diagnosis and decision-making. The present study proved that Impact demodulation is recommended to specify faulty bearing source location and severity, while time waveform or spectrum analysis techniques can detect fault progress but couldn't distinguish severity due to bearings similarity. Based on Impact Demodulation technique decision is made to exchange only the higher severity faulty bearing and postponed the other one for next planned shutdown. Consequently, the machine availability and health can be achieved by reducing vibration impact around 100 % through continuous condition monitoring.

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Fault detection and diagnosis on the rolling element bearing

Cited by 5 publications

References 25 publications

An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

An Experimental Study on Condition Diagnosis for Thrust Bearings in Oscillating Water Column Type Wave Power Systems

Improved Technique to Diagnose Fault in IEEE Standard 14-Bus System

Machine Health Monitoring Using Vibration Analysis

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