scholarly journals Acoustic emission based condition monitoring study of piston rod seals by varying speed and pressure parameters

2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Vignesh Vishnudas Shanbhag ◽  
Thomas Meyer ◽  
Leo Caspers ◽  
Rune Schlanbusch
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Median Frequency ◽  
Test Rig ◽  
Mean Frequency ◽  
Emission Signal ◽  
Seal Failure ◽  
Power Spectral ◽  
Hydraulic Cylinders ◽  
Rod Seals

Hydraulic cylinders are used in a wide range of applications such as oil drilling equipment, construction vehicles and manufacturing machines. Seal failure is one of the primitive causes of failure in hydraulic cylinders, possibly leading to fluid spill, unscheduled maintenance, reduced availability and thus leading to lower productivity. Regular visual inspection of seals without affecting the productivity is difficult as the seals are placed internally in the hydraulic cylinder requiring disassembly of the piston. Therefore, condition monitoring is required to assess the current health of the seals. There have been successful attempts made in literature for the assessment of seal quality using acoustic emission-based condition monitoring. However, there have been very few studies performed to diagnose the seal failure under varying speed and pressure parameters. Therefore, this study aims at increasing the understanding of seal failure under varying speed and pressure conditions through correlation with the acoustic emission signal. Experiments were performed on a hydraulic test rig using unworn, semi-worn and worn piston rod seals. For each seal wear condition, experiments were performed for five strokes at pressure conditions of 10, 20, 30 and 40 bar and speeds of 50 mm/s and 100 mm/s.  Continuous acoustic emission data were acquired during all the tests. The acoustic emission signal of each piston rod stroke was analyzed using different acoustic emission features such as power spectral density, root mean square, peak, mean frequency, median frequency and band power. From the acoustic emission analysis, by using power spectral density, mean frequency and median frequency feature it is possible to identify and segregate unworn seal, leakage due to semi-worn seal and leakage due to worn seal in the test rig. The acoustic emission-based condition monitoring methodology developed in this study lays a strong foundation for further research to develop real-time monitoring of the piston rod seal in hydraulic cylinders that are used in the offshore industry.

scholarly journals Defining acoustic emission-based condition monitoring indicators for monitoring piston rod seal and bearing wear in hydraulic cylinders

2021 ◽  
Author(s):  
Vignesh V. Shanbhag ◽  
Thomas J. J. Meyer ◽  
Leo W. Caspers ◽  
Rune Schlanbusch
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Acoustic Emissions ◽  
Hydraulic Cylinder ◽  
Test Phase ◽  
Median Frequency ◽  
Test Rig ◽  
Hydraulic Test ◽  
Fluid Leakage ◽  
Hydraulic Cylinders

AbstractFluid leakage from hydraulic cylinders is a major concern for the offshore industries as it directly affects hydraulic cylinder energy efficiency and causes environmental contamination. There have been attempts made in literature to develop robust condition monitoring techniques for hydraulic cylinders. However, most of these studies were performed to identify degradation of single components. Therefore, in this study, the aim is to monitor degradation of multiple components simultaneously in hydraulic cylinders using acoustic emissions. Experiments performed consist of three test phases and were performed using a hydraulic test rig. In the first test phase, the study is performed to identify acoustic emission features that can be used to monitor piston rod seal wear. In the second test phase, acoustic emission features are identified that can be used to understand bearing wear when unworn, semi-worn or worn piston rod seals are used in hydraulic test rig. In the third test phase, a run-to-failure test is conducted to identify acoustic emission features that can indicate fluid leakage initiation due to piston rod seal wear. The median frequency feature showed good repeatability in all the three test phases to identify piston rod seal wear, bearing wear and fluid leakage initiation during the initial stages in the hydraulic test rig. The proposed acoustic emission-based condition monitoring technique is robust and can be used for the hydraulic cylinders in the industries, as it identifies acoustic emission features based on particular frequency bands associated to specific components, making it less susceptible to noise from other components.

scholarly journals Acoustic Emission-Based Condition Monitoring and Remaining Useful Life Prediction of Hydraulic Cylinder Rod Seals

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6012
Author(s):  
Jørgen F. Pedersen ◽  
Rune Schlanbusch ◽  
Thomas J. J. Meyer ◽  
Leo W. Caspers ◽  
Vignesh V. Shanbhag
Keyword(s):  
Acoustic Emission ◽  
Condition Monitoring ◽  
Hydraulic Cylinder ◽  
Remaining Useful Life ◽  
Test Rig ◽  
Operational Conditions ◽  
Hydraulic Cylinders ◽  
Ae Signal ◽  
Useful Life ◽  
The Impact

The foremost reason for unscheduled maintenance of hydraulic cylinders in industry is caused by wear of the hydraulic seals. Therefore, condition monitoring and subsequent estimation of remaining useful life (RUL) methods are highly sought after by the maintenance professionals. This study aimed at investigating the use of acoustic emission (AE) sensors to identify the early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF) in a test rig. In this study, the impact of sensor location and rod speeds on the AE signal were investigated using both time- and frequency-based features. Furthermore, a frequency domain analysis was conducted to investigate the power spectral density (PSD) of the AE signal. An accelerated leakage initiation process was performed by creating longitudinal scratches on the piston rod. In addition, the effect on the AE signal from pausing the test rig for a prolonged duration during the RTF tests was investigated. From the extracted features of the AE signal, the root mean square (RMS) feature was observed to be a potent condition indicator (CI) to understand the leakage initiation. In this study, the AE signal showed a large drop in the RMS value caused by the pause in the RTF test operations. However, the RMS value at leakage initiation is seen to be a promising CI because it appears to be linearly scalable to operational conditions such as pressure and speed, with good accuracy, for predicting the leakage threshold.

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.

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.

Investigation of Stamping Tool Wear Initiation at Microscopic Level Using Acoustic Emission Sensors

2019 ◽  
Vol 794 ◽  
pp. 285-294 ◽  
Author(s):  
Vignesh V. Shanbhag ◽  
Bernard F. Rolfe ◽  
Narayanan Arunachalam ◽  
Michael P. Pereira
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Adhesive Wear ◽  
Microscopic Level ◽  
Mean Frequency ◽  
Severe Wear ◽  
Emission Signal ◽  
Early Stages ◽  
Acoustic Emission Sensors

Stamping tools are prone to an adhesive wear mode called galling. Adhesive wear on stamping tools can degrade the product quality and can affect the mass production. Even a small improvement in the maintenance process is beneficial for the stamping industry. Therefore, this study will focus on understanding and detecting the initiation of tool wear at the microscopic level in sheet metal stamping using acoustic emission sensors. Stamping tests were performed using a semi-industrial stamping process, which can perform clamping, piercing, stamping and trimming in a single cycle. The stamping test was performed using a high strength low alloy sheet steel and D2 tool steel for dry and lubricated conditions. The acoustic emission signal was recorded for each stamped part until severe wear on the dies was observed. These acoustic emission signals were later analyzed using time and frequency domain features. The time domain features such as peak, RMS, kurtosis and skewness could identify significant changes in the acoustic emission signal only when the severe wear was observed on the stamped parts for both dry and lubricated conditions. However, this study has identified that a frequency feature – known as mean-frequency estimate – could identify early stages of wear initiation at the microscopic level. Evidence of this early stage of wear on the part surfaces was not clearly visible to the naked eye, and could only be clearly observed via surface measurement instruments such as an optical profilometer. The sidewalls of the stamped parts corresponding to the initial change in AE mean-frequency trend were qualitatively correlated with 3D profilometer scans of the stamped parts, to show that AE mean-frequency can indicate the initial minor scratches on the sidewalls of the stamped parts due to the galling wear on the die radii surfaces. The results from this study can be used to develop a methodology to determine the very early stages of stamping tool wear, providing a strong basis for condition monitoring in the stamping industry.

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