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

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.

Author(s):  
Vignesh V. Shanbhag ◽  
Thomas J. J. Meyer ◽  
Leo W. Caspers ◽  
Rune Schlanbusch

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.


2020 ◽  
Vol 12 (1) ◽  
pp. 9
Author(s):  
Vignesh Vishnudas Shanbhag ◽  
Thomas Meyer ◽  
Leo Caspers ◽  
Rune Schlanbusch

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.


2019 ◽  
Vol 72 (2) ◽  
Author(s):  
Peipei Feng ◽  
Pietro Borghesani ◽  
Wade A. Smith ◽  
Robert B. Randall ◽  
Zhongxiao Peng

Abstract Acoustic emission (AE) techniques play a key role in machine condition monitoring and wear/fault diagnosis. Understanding the impact of friction and wear on the generation of AE signals is essential to building a reliable wear monitoring system. However, existing papers focus on only one or two factors in specific contact conditions. This paper aims at surveying studies related to both theoretical models and experimental investigations to produce a comprehensive picture of the relationship between tribological parameters (e.g., surface roughness, oil film thickness, and friction coefficient), operating parameters (e.g., sliding velocity and load), and AE signal characteristics (e.g., amplitude/energy, frequency, and event count). This result will provide guidance for the development of AE-based condition monitoring approaches and in particular for the establishment of AE-based wear assessment techniques.


2021 ◽  
pp. 107754632110161
Author(s):  
Aref Aasi ◽  
Ramtin Tabatabaei ◽  
Erfan Aasi ◽  
Seyed Mohammad Jafari

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.


2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199691
Author(s):  
Omar AlShorman ◽  
Fahad Alkahatni ◽  
Mahmoud Masadeh ◽  
Muhammad Irfan ◽  
Adam Glowacz ◽  
...  

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.


Author(s):  
Pradeep Lall ◽  
Hao Zhang ◽  
Lynn Davis

The reliability consideration of LED products includes both luminous flux drop and color shift. Previous research either talks about luminous maintenance or color shift, because luminous flux degradation usually takes very long time to observe. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (20%) after a short training of the estimator.


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