scholarly journals Condition monitoring of roller bearings using acoustic emission

2021 ◽  
Vol 6 (2) ◽  
pp. 367-376
Author(s):  
Daniel Cornel ◽  
Francisco Gutiérrez Guzmán ◽  
Georg Jacobs ◽  
Stephan Neumann
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Roller Bearing ◽  
Surface Damage ◽  
Operating Conditions ◽  
Time To Failure ◽  
Damage Initiation ◽  
Emission Signal ◽  
Repair Costs ◽  
Bearing Failures

Abstract. Roller bearing failures in wind turbines' gearboxes lead to long downtimes and high repair costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context, an acoustic-emission-based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with traditional vibration-based condition monitoring systems, with a focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic-emission system towards lubricating conditions. In addition, the system has shown that a damaged surface can be detected at least ∼ 4 % (8 h, regarding the time to failure) earlier than by using the vibration-based system. Furthermore, significant deviations from the average acoustic-emission signal were detected up to ∼ 50 % (130 h) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.

scholarly journals Condition Monitoring of roller bearings using Acoustic Emission

2020 ◽  
Author(s):  
Daniel Cornel ◽  
Francisco Gutiérrez Guzmán ◽  
Georg Jacobs ◽  
Stephan Neumann
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Roller Bearing ◽  
Surface Damage ◽  
Operating Conditions ◽  
Time To Failure ◽  
Damage Initiation ◽  
Emission Signal ◽  
Bearing Failures ◽  
Condition Monitoring System

Abstract. Roller bearing failures in wind turbines gearboxes lead to long downtimes and high repairing costs, which could be reduced by the implementation of a predictive maintenance strategy. In this paper and within this context an acoustic emission based condition monitoring system is applied to roller bearing test rigs with the aim of identifying critical operating conditions before bearing failures occurs. Furthermore, a comparison regarding detection times is carried out with a traditional vibration based condition monitoring systems, with focus on premature bearing failures such as white etching cracks. The investigations show a sensitivity of the acoustic emission system towards lubricating conditions. In addition, the system has shown, that a damaged surface can be detected at least ~ 4 % (8 hours, regarding the time to failure) earlier than by using the vibration based system. Furthermore, significant deviations from the average acoustic emission signal were detected up to ~ 50 % (130 hours) before the test stop and are possibly related to sub-surface damage initiation and might result in an earlier damage detection in the future.

Pattern recognition approach for acoustic emission burst detection in a gearbox under different operating conditions

2019 ◽  
Vol 25 (17) ◽  
pp. 2295-2304
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Ralph Baltes ◽  
Elisabeth Clausen
Keyword(s):  
Pattern Recognition ◽  
Acoustic Emission ◽  
Condition Monitoring ◽  
Rotational Speed ◽  
Operating Conditions ◽  
Burst Detection ◽  
Threshold Method ◽  
Planetary Gearbox ◽  
Industrial Sectors ◽  
Artificial Neural Network Ann

Diverse machines in the mining, energy, and other industrial sectors are subject to variable operating conditions (OCs) such as rotational speed and load. Therefore, the condition monitoring techniques must be adapted to face this scenario. Within these techniques, the acoustic emission (AE) technology has been successfully used as a technique for condition monitoring of components such as gears and bearings. An AE analysis involves the detection of transients within the signals, which are called AE bursts. Traditional methods for AE burst detection are based on the definition of threshold values. When the machine under study works under variable rotational speed and load, threshold-based methods could produce inadequate results due to the influence of these OCs on the AE. This paper presents a novel burst detection method based on pattern recognition using an artificial neural network (ANN) for classification. The results of the method were compared to an adaptive threshold method. Experimental data were measured in a planetary gearbox test rig under different OCs. The results showed that both methods perform similarly when signals measured under constant OCs are considered. However, when signals are measured under different OCs, the ANN method performs better. Thus, the comparative analysis showed the good potential of the approach to improve an AE analysis of variable speed and/or load machines.

Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Ahmet Soylemezoglu ◽  
S. Jagannathan ◽  
Can Saygin
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Numerical Control ◽  
Rolling Element Bearing ◽  
Time To Failure ◽  
Cnc Machine ◽  
Bearing Failures ◽  
Rolling Element

In this paper, a novel Mahalanobis–Taguchi system (MTS)-based fault detection, isolation, and prognostics scheme is presented. The proposed data-driven scheme utilizes the Mahalanobis distance (MD)-based fault clustering and the progression of MD values over time. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on rolling element bearings inside the spindle headstock of a microcomputer numerical control (CNC) machine testbed. The bearings have been instrumented with vibration and temperature sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for bearing fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that (1) presents a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately and (2) offers a systematic way to determine the key features, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent and can easily be implemented on wireless motes and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments.

Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Artificial Intelligence ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Signal Analysis ◽  
Machine Condition ◽  
Artificial Intelligence Techniques ◽  
Emission Signal ◽  
Machine Condition Monitoring

Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microturning of Titanium Alloy

2014 ◽  
Vol 984-985 ◽  
pp. 31-36 ◽  
Author(s):  
A. Gopikrishnan ◽  
A.K. Nizamudheen ◽  
M. Kanthababu
Keyword(s):  
Acoustic Emission ◽  
Titanium Alloy ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Dominant Frequency ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Nose Radius ◽  
Emission Signal ◽  
Tool Condition

In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.

scholarly journals Correlation of Acoustic Emission with Fractography in Bending of Glass–Epoxy Composites

2020 ◽  
Vol 39 (3) ◽  
Author(s):  
Maciej Panek ◽  
Stanislaw Blazewicz ◽  
Krzysztof J. Konsztowicz
Keyword(s):  
Acoustic Emission ◽  
Glass Fiber ◽  
Amplitude Ratio ◽  
Fiber Reinforced ◽  
Fiber Volume ◽  
Glass Fiber Reinforced Plastic ◽  
Damage Initiation ◽  
Emission Signal ◽  
Micro Cracks ◽  
Macro Scale

Abstract Damage processes in glass fiber reinforced plastic composites have been examined extensively by many analytical and experimental methods, including acoustic emission. While damage phenomena in mezo- and macro-scale are well described, the subtle mechanisms in micro-scale are still under discussion. The goal of this work was to apply the acoustic emission to examine damage initiation in fiber reinforced epoxy resin composites with different continuous glass-fiber architectures. Basic lay-ups were used: unidirectional, cross-ply and angle-ply, each with varying fiber volume content in epoxy matrix. Detection of fine micro-cracks was possible owing to merging the mechanical monotonic and step-load tests in 4-point bending with acoustic emission monitoring and detailed scanning electron microscopy observations. Clear images of macroscopically undamaged samples after interruption of step-loading cycles revealed the initiation of micro-cracks by debonding of fiber-matrix interfaces, in agreement with acoustic emission signal analyses, featuring use of historic index and rise time/amplitude ratio.

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