Condition monitoring of internal leakage in modern water hydraulic cylinders using acoustic emission

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.

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An Experimental Study of Monitoring Internal Leakage in Water Hydraulic Cylinders Using Acoustic Emission

Journal of Testing and Evaluation ◽

10.1520/jte12534 ◽

2005 ◽

Vol 33 (6) ◽

pp. 12534 ◽

Cited By ~ 2

Author(s):

DR Petersen ◽

RE Link ◽

P Chen ◽

PSK Chua ◽

GH Lim

Keyword(s):

Experimental Study ◽

Acoustic Emission ◽

Hydraulic Cylinders ◽

Water Hydraulic

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

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07340-8 ◽

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.

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

Sensors ◽

10.3390/s21186012 ◽

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.

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An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06476-3 ◽

2021 ◽

Vol 113 (1-2) ◽

pp. 585-603

Author(s):

Wenderson N. Lopes ◽

Pedro O. C. Junior ◽

Paulo R. Aguiar ◽

Felipe A. Alexandre ◽

Fábio R. L. Dotto ◽

...

Keyword(s):

Acoustic Emission ◽

Fourier Transform ◽

Condition Monitoring ◽

Grinding Wheel ◽

Short Time Fourier Transform ◽

Short Time

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Diamond Grinding Wheel Condition Monitoring Based on Acoustic Emission Signals

Sensors ◽

10.3390/s21041054 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1054

Author(s):

Guo Bi ◽

Shan Liu ◽

Shibo Su ◽

Zhongxue Wang

Keyword(s):

Neural Network ◽

Acoustic Emission ◽

Condition Monitoring ◽

Time Domain ◽

Diamond Wheel ◽

Feature Representation ◽

Grinding Wheel ◽

Time Waveform ◽

Original Time ◽

Whole Life Cycle

Acoustic emission (AE) phenomenon has a direct relationship with the interaction of tool and material which makes AE the most sensitive one among various process variables. However, its prominent sensitivity also means the characteristics of random and board band. Feature representation is a difficult problem for AE-based monitoring and determines the accuracy of monitoring system. It is knottier for the situation of using diamond wheel grinding optical components, not only because of the complexity of grinding process but also the high requirement on surface and subsurface quality. This paper is dedicated to AE-based condition monitoring of diamond wheel during grinding brittle materials and feature representation is paid more attention. AE signal of brittle-regime grinding is modeled as a superposition of a series of burst-type AE events. Theory analysis manifested that original time waveform and frequency spectrum are all suitable for feature representation. Considering the convolution form of b-AE in time domain, a convolutional neural network with original time waveform of AE signals as the input is built for multi-class classification of wheel state. Detailed state division in a wheel’s whole life cycle is realized and the accuracy is over 90%. Different from the overlapping in time domain, AE components of different crack mechanisms are probably separated in frequency domain. From this point of view, AE spectrums are more suitable for feature extraction than the original time waveform. In addition, the time sequence of AE samples is essential for the evaluation of wheel’s life elapse and making use of sequential information is just the idea behind recurrent neural network (RNN). Therefore, long short-term memory (LSTM), a special kind of RNN, is used to build a regression prediction model of wheel state with AE spectrums as the model input and satisfactory prediction accuracy is acquired on the test set.

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Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission

Journal of Vibration and Control ◽

10.1177/10775463211016130 ◽

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.

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Acoustic Emission Studies on Carbon/Phenolic Ablative Composites using PCD and PCBN Tools

Advanced Composites Letters ◽

10.1177/096369359900800301 ◽

1999 ◽

Vol 8 (3) ◽

pp. 096369359900800 ◽

Cited By ~ 3

Author(s):

P. S. Sreejith ◽

R. Krishnamurthy

Keyword(s):

Acoustic Emission ◽

Condition Monitoring ◽

Cutting Tool ◽

Tool Condition Monitoring ◽

Tool Condition ◽

The Status ◽

Emission Studies ◽

Pcbn Tools ◽

Cutting Speeds ◽

Phenolic Composite

During manufacturing, the performance of a cutting tool is largely dependent on the conditions prevailing over the tool-work interface. This is mostly dependent on the status of the cutting tool and work material. Acoustic emission studies have been performed on carbon/phenolic composite using PCD and PCBN tools for tool condition monitoring. The studies have enabled to understand the tool behaviour at different cutting speeds.

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