Prediction method of ball valve internal leakage rate based on acoustic emission technology

Author(s):  
Mingjiang Shi ◽  
Yanbing Liang ◽  
Liansheng Qin ◽  
Zhen Zheng ◽  
Zhiqiang Huang
2021 ◽  
Vol 2113 (1) ◽  
pp. 012016
Author(s):  
Fei Song ◽  
Likun Peng ◽  
Jia Chen ◽  
Benmeng Wang

Abstract In order to realize the nondestructive testing (NDT) of the internal leakage fault of hydraulic spool valves, the internal leakage rate must be predicted by AE (acoustic emission) technology. An AE experimental platform of internal leakage of hydraulic spool valves is built to study the characteristics of AE signals of internal leakage and the relationship between AE signals and leakage rates. The research results show the AE signals present a wideband characteristic. The main frequencies are concentrated in 30~50 kHz and the peak frequency is around 40 kHz. When the leakage rate is large, there are significant signal characteristics appearing in the high frequency band of 75~100 kHz. The exponent of the root mean square(RMS) of AE signals is positively correlated with the exponent of the leakage rate only if the leakage rate is greater than 2~3 mL/min. This find could be used to predict the internal leakage rate of hydraulic spool valves.


Author(s):  
A. Albers ◽  
M. Dickerhof

The application of Acoustic Emission technology for monitoring rolling element or hydrodynamic plain bearings has been addressed by several authors in former times. Most of these investigations took place under idealized conditions, to allow the concentration on one single source of emission, typically recorded by means of a piezoelectric sensor. This can be achieved by either eliminating other sources in advance or taking measures to shield them out (e. g. by placing the acoustic emission sensor very close to the source of interest), so that in consequence only one source of structure-born sound is present in the signal. With a practical orientation this is often not possible. In point of fact, a multitude of potential sources of emission can be worth considering, unfortunately superimposing one another. The investigations reported in this paper are therefore focused on the simultaneous monitoring of both bearing types mentioned above. Only one piezoelectric acoustic emission sensor is utilized, which is placed rather far away from the monitored bearings. By derivation of characteristic values from the sensor signal, different simulated defects can be detected reliably: seeded defects in the inner and outer race of rolling element bearings as well as the occurrence of mixed friction in the sliding surface bearing due to interrupted lubricant inflow.


2010 ◽  
Vol 26 (02) ◽  
pp. 106-110
Author(s):  
Ge Wang ◽  
Michael Lee ◽  
Chris Serratella ◽  
Stanley Botten ◽  
Sam Ternowchek ◽  
...  

Real-time monitoring and detection of structural degradation helps in capturing the structural conditions of ships. The latest nondestructive testing (NDT) and sensor technologies will potentially be integrated into future generations of the structural integrity management program. This paper reports on a joint development project between Alaska Tanker Company, American Bureau of Shipping (ABS), and MISTRAS. The pilot project examined the viability of acoustic emission technology as a screening tool for surveys and inspection planning. Specifically, testing took place on a 32-year-old double-hull Trans Alaska Pipeline System (TAPS) trade tanker. The test demonstrated the possibility of adapting this technology in the identification of critical spots on a tanker in order to target inspections. This targeting will focus surveys and inspections on suspected areas, thus increasing efficiency of detecting structural degradation. The test has the potential to introduce new inspection procedures as the project undertakes the first commercial testing of the latest acoustic emission technology during a tanker's voyage.


2018 ◽  
Vol 85 (6) ◽  
pp. 434-442 ◽  
Author(s):  
Noushin Mokhtari ◽  
Clemens Gühmann

Abstract For diagnosis and predictive maintenance of mechatronic systems, monitoring of bearings is essential. An important building block for this is the determination of the bearing friction condition. This paper deals with the possibility of monitoring different journal bearing friction states, such as mixed and fluid friction, and examines a new approach to distinguish between different friction intensities under several speed and load combinations based on feature extraction and feature selection methods applied on acoustic emission (AE) signals. The aim of this work is to identify separation effective features of AE signals to subsequently classify the journal bearing friction states. Furthermore, the acquired features give information about the mixed friction intensity, which is significant for remaining useful lifetime (RUL) prediction. Time domain features as well as features in the frequency domain have been investigated in this work. To increase the sensitivity of the extracted features the AE signals were transformed to the frequency-time-domain using continuous wavelet transform (CWT). Significant frequency bands are determined to separate different friction states more effective. A support vector machine (SVM) is used to classify the signals into three different friction classes. In the end the idea for an RUL prediction method by using the already determined information is given and explained.


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