Fault detection in reciprocating compressor valves under varying load conditions

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

doi:10.1016/j.ymssp.2015.09.005

Cited by 71 publications

(38 citation statements)

References 16 publications

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“…All this is a challenge because the compressors are elements that work in extreme working conditions such as high temperatures, variable pressures, and alternative movements. As consequence, its critical elements such as valves and roller bearings are more likely to failure [1][2][3][4]. In this context, the application of Prognostics and Health Management (PHM) technologies is important to develop intelligent fault diagnosis systems to guarantee high reliability in the operation and avoid catastrophic damages [5].…”

Section: Introductionmentioning

confidence: 99%

“…Several types of signals are useful for fault diagnosis in reciprocating compressors, such as vibration, acoustic emission, electric current, pressure and temperature [6]. Vibration signals have been studied, for both signal analysis to detect faults [4,7] and for fault classification [8]. Compressor are reciprocating machines that works with different mechanical multi-component coupling elements, and the vibration signals have non-linear and non-stationary characteristics [9].…”

Section: Introductionmentioning

confidence: 99%

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Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

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Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set of features was obtained by applying the symbolic dynamics algorithm, which provides the histogram of a set of symbols. This set of symbols was obtained by subdividing a 2D Poincaré plot into angular regions and counting the intersection of the phase trajectories on each of regions. The second type of features corresponds to the complex correlation measure which is calculated as the addition of the areas of triangles belonging to a Poincaré plot. Additionally, a small set of classical statistical features was also used for comparing their classification abilities to the new set of proposed features. The three sets of features enable highly accurate classification of the set of faults when used with random forest classification models. Notably, the ensemble subspace k-Nearest Neighbors algorithm provides classification accuracies higher than 99%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

et al. 2020

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show abstract

“…In these kinds of methods, a computer program is designed to simulate the reasoning and decision-making process of human experts to diagnose faults. Other fault diagnosis methods, such as machine learning [8,9] and signal processing [10][11][12], where there is a comparison of frequency analysis and SVM (support vector machine) classifiers in [11,12], are also widely used in practical applications [13,14]. There are also fault diagnosis methods in multi-sensor networks.…”

Section: Introductionmentioning

confidence: 99%

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

Jiang

Xie

2017

Applied Sciences

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Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster-Shafer (D-S) evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments), and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method.

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“…Because the valve is installed on the outside of the reciprocating compressor, it is convenient to detect and install the valve. Kurt Pichler, et al [1] investigated fault detection in reciprocating compressor valves under varying load conditions and Pichler K., Lughofer E., Pichler M., et al [2] detected cracks in reciprocating compressor valves using pattern recognition in the pV diagram. Reciprocating compressor movement mechanism is mainly slider crank mechanism, for Paulo Flores and his team did a lot of research in the mechanism with joint clearance [3][4][5], which the multi clearance, the clearance of the moving pair and the contact impact detection are considered respectively.…”

Section: Introductionmentioning

confidence: 99%

A parametric study on the dynamic response of reciprocating compressor with a revolute clearance joints

Cheng¹

2017

Vib. proced.

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Abstract. Due to the friction and manufacturing and assembly errors, the clearance of revolution joints is unavoidable. When the clearance value increases to a certain value, the influence of clearance on the performance of the equipment could not be ignored. In this paper, the contact collision model is used to describe the effect of clearance. The model of reciprocating compressor is simplified properly, and the influence of revolution joint clearance on the system is studied by contact impact function. The influence of parameters on the acceleration of piston is studied by changing the size, position of clearance and velocity of crankshaft. The Lyapunov exponent of crosshead acceleration under the condition of load and no load are compared, which lays the foundation for studying the vibration of reciprocating compressor caused by clearance.

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Fault detection in reciprocating compressor valves under varying load conditions

Cited by 71 publications

References 16 publications

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

Reciprocating Compressor Multi-Fault Classification Using Symbolic Dynamics and Complex Correlation Measure

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

A parametric study on the dynamic response of reciprocating compressor with a revolute clearance joints

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