Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure

Loukopoulos, Panagiotis; Zolkiewski, George; Bennett, I.J.; Sampath, Suresh; Pilidis, Pericles; Li, X.; David, Éric

doi:10.1016/j.ymssp.2018.09.033

Cited by 42 publications

(32 citation statements)

References 72 publications

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“…A comprehensive practical study of the operation of piston compressor valves operating in gas lift systems and associated petroleum gas transport is aimed at increasing their tightness, efficiency, maintainability and safe operation [12,13].…”

Section: Discussion Of the Results Of Experimental Studiesmentioning

confidence: 99%

Methodology of Experimental Investigations of Valve Operation

Seyidahmadov¹

2019

EUREKA: Physics and Engineering

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Tightness, as well as the reliability of the valve plate, is a complex property of the effective operation of compressor cylinders of the first stage and, in general, gas-engine reciprocating compressors. The issue of valve plate tightness is a subject of independent study, since technical and economic efficiency depends on their work. In this connection, only some data obtained under operating conditions are presented in this work. As a research result, it is found that, taking into account the identified requirements for the gas lift system, in order to effectively increase the operating hours of valves with increased tightness of the plate, it is necessary to check and purge the valves. Therefore, each valve in the gas lift compressor station, without subjecting them to cleaning, is first recommended to check for leaks. To confirm the feasibility of checking valve tightness, special equipment is offered for each gas-lift compressor station, a purge chamber, on which the tightness of valve plates is checked. The usefulness and importance of the purge chamber is in preparation of the valve at the gas lift compressor station, which contributes to increased efficiency, safe operation, normal tightness and reliability of its operation.

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Section: Discussion Of the Results Of Experimental Studiesmentioning

confidence: 99%

Methodology of Experimental Investigations of Valve Operation

Seyidahmadov¹

2019

EUREKA: Physics and Engineering

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“…However, the vibration characteristics of RC have non-linear, non-stationary, and multi-component coupling factors [3,9], which pose challenges for RC fault diagnosis. Generally, the FDM of RC can be divided into three categories: model-based, data-driven, and a combination of the two [2,10,11].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

et al. 2020

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High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.

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“…The study showed that the three techniques have resulted in significant different RUL prediction results. Loukopoulos et al [9] studied the performance of several machine learning techniques, including linear regression, polynomial Over recent decades, much research has been implemented to develop health monitoring methods for rotating machinery, especially bearings. Compared to fault detection, the literature of prognostics and health management is relatively limited, and effective implementing of prognostic techniques is still lacking.…”

mentioning

confidence: 99%

“…The study showed that the three techniques have resulted in significant different RUL prediction results. Loukopoulos et al [9] studied the performance of several machine learning techniques, including linear regression, polynomial regression and K-Nearest Neighbors Regression. The results showed that an ensemble method based on the weighted average of the predicted RUL of each individual method offers a higher predictive accuracy.…”

mentioning

confidence: 99%

“…For instance, a self-organizing neural network was employed by Zhang and Ganesan [11] for extrapolating the fault progression and estimating the remnant life of a bearing. Loukopoulos et al [9] applied a Self-Organizing Map (SOM) model to predict the RUL of industrial pumps using temperature measurements. Dong et al [12] developed a condition prediction method based on the grey model and back-propagation neural network.…”

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confidence: 99%

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Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Elasha

Shanbr

et al. 2019

Energies

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Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig.

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Abrupt fault remaining useful life estimation using measurements from a reciprocating compressor valve failure

Cited by 42 publications

References 72 publications

Methodology of Experimental Investigations of Valve Operation

Methodology of Experimental Investigations of Valve Operation

Quantitative Evaluation on Valve Leakage of Reciprocating Compressor Using System Characteristic Diagnosis Method

Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

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