Explore the vast range of titles available.

Reciprocating compressor is the core equipment of petrochemical industry and its stable running is very important for productions in the offshore drilling platform. The reason why it is difficult to extract features from vibration signals to reflect the operating state of the compressor is that its internal structure is complex and there are many excitation sources. To solve this problem, a new fault diagnosis method based on spatio-temporal features fusion based on deep belief network (STF-DBN) was proposed, which comprehensively processes multi-source signal features from dimensions of time and space. The temporal features extraction strategy is designed to reflect the data trend by reconstructing the time series according to different period characteristics of fault-related parameters. And the spatial features are extracted to reflect the non-amplitude characteristic of data by breaking down the raw data trend and considering the importance of reconstructed series to various faults. STF-DBN can overcome the deficiency of traditional unsupervised network DBN that cannot extract periodic features, no longer rely on the number of fault data samples, and construct a more comprehensive health curve representing the operation status of reciprocating compressors for fault diagnosis and early warning. The classic Tennessee Eastman (TE) data set in the control field is used for the diagnosis effect test, and the STF-DBN is applied to the operation status detection of the reciprocating compressor for offshore natural gas extraction of China National Offshore Oil Corporation. The experimental results confirm the effectiveness of the proposed method in fault defection and early warning.

The verification of mathematical models for multistage reciprocating compressors is crucial for ensuring their accuracy and reliability. In this study, we used different machine learning (ML) models to verify the results of MATLAB-based models of single-stage reciprocating compressors, multistage reciprocating compressors without intercoolers, and multistage reciprocating compressors with intercoolers to simulate the real-world operating conditions of a reciprocating compressor. The verification focuses on key performance indicators, such as the pressure–volume (PV) graph, outlet temperature graph, volumetric efficiency, and pressure ratio graph. The MATLAB model computes thermodynamic parameters, such as the power required, outlet pressure, and outlet temperature for various operating conditions. The MATLAB model produced the following results for single-stage compressor: the outlet pressure increased by 1.6 times the inlet pressure of the compressor, the volume reduced by 20% of the volume at the inlet of the single-stage compressor, and the outlet temperature increased by 30% of the inlet temperature. In the case of a multistage compressor without an intercooler, the outlet pressure increased by about 3.3–3.6 times the inlet pressure of the compressor; the volume reduced by 60% of the volume at the inlet, and the outlet temperature increased by 35% in comparison to the inlet temperature of the multistage compressor without an intercooler. Subsequently, in the case of a multistage compressor with an intercooler at the first stage of compression, the pressure increased by three times the inlet pressure; at the second stage of compression, the pressure increased by six times the inlet pressure of the compressor, the volume was reduced by approximately 80%, and the intercooler maintained the increase in outlet temperature by 30%, limiting it and preventing excessive expansion of air in the compressor and increasing the efficiency of the compressor by 12% in comparison to the multistage compressor without an intercooler. In addition, the results generated by all the machine learning models used in the study were in correlation with the results generated by the MATLAB model for all three compressors, with an accuracy of approximately 90% or more for almost all the models implemented for prediction. By comparing the predicted outputs from the ML model with the MATLAB-generated results, the accuracy and consistency of the simulation were assessed. This study aims to bridge the gap between traditional mathematical modeling and modern data-driven validation techniques to ensure robustness in compressor performance predictions.

Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed.

A fault diagnosis method for reciprocating compressor valves suitable for variable operating conditions is presented in this paper. Firstly, a test bench is independently constructed to simulate fault scenarios under diverse operating conditions and with various faults. The two types of p-V diagrams are gathered, and the improved logarithmic p-V diagram acquisition method is used for logarithmic transformation to obtain the multi-conditional logarithmic p-V diagram dataset and the fault logarithmic p-V diagram dataset. Subsequently, to predict the fault-free logarithmic p-V diagram under different operating conditions, a BP neural network is trained with the multi-condition logarithmic p-V diagram dataset. Next, the fault sequence is derived by subtracting the fault logarithmic p-V diagram from the fault-free logarithmic p-V diagram acquired under the same operating condition. Ultimately, the feature extraction of the fault sequence and the fault classification are accomplished by the employment of a triplet Siamese neural network (SNN). The results indicate that the fault classification accuracy of the method presented in this paper can attain 100%, which confirms that differential processing on the logarithmic p-V diagram is effective for fault feature preprocessing. This study not only improves the accuracy and efficiency of valve fault diagnosis in reciprocating compressors but also provides technical support for maintenance and fault prevention.

A method was developed for determining the mass of compressed gas and the supplied indicated work of the compressor and pump during compression, both for regenerative and recuperative heat exchange. On the basis of a certain mass of compressed gas supplied to the consumer and the supplied indicated work, coefficients were proposed that characterize the efficiency and economic efficiency of using regenerative heat exchange in comparison with recuperative heat exchange for cooling the compressor.

Variable-speed reciprocating compressors (VSRCs) have been increasingly used in domestic refrigeration due to their ability to modulate cooling capacity and reduce energy consumption. A detailed understanding of performance-limiting factors such as volumetric and exergetic inefficiencies is essential for optimizing their operation. An experimentally validated simulation model was developed using GT-SUITE to analyze a VSRC operating with R-600a across speeds from 1800 to 6300 rpm. Volumetric inefficiencies were quantified using a stratification methodology, while an exergy-based approach was adopted to assess the main sources of thermodynamic inefficiency in the compressor. Unlike traditional energy analysis, exergy analysis reveals where and why irreversibilities occur, linking them directly to power consumption and providing a framework for optimizing design. Results reveal that neither volumetric nor exergy efficiency varies monotonically with compressor speed. At low speeds, exergetic losses are dominated by the electrical motor (up to 19% of input power) and heat transfer (up to 13.5%). Conversely, at high speeds, irreversibilities from fluid dynamics become critical, with losses from discharge valve throttling reaching 5.8% and bearing friction increasing to 6.5%. Additionally, key volumetric inefficiencies arise from piston–cylinder leakage, which causes up to a 4.5% loss at low speeds, and discharge valve backflow, causing over a 5% loss at certain resonant speeds. The results reveal complex speed-dependent interactions between dynamic and thermodynamic loss mechanisms in VSRCs. The integrated modeling approach offers a robust framework for diagnosing inefficiencies and supports the development of more energy-efficient compressor designs.

The bearing vibration signal of reciprocating compressor has complex, non-stationary, nonlinear, and feature coupling characteristics. A method for sub-health recognition of sliding bearings based on curve adaptive grasshopper optimization algorithm optimize the parameters of variational mode decomposition (CAGOA-VMD) and generalized refine composite multiscale dispersion entropy (GRCMDE) is used. First, the CAGOA was used to search the best influence parameter combination of the VMD algorithm, and determine the bandwidth parameters and the number of decompositions that need to be set by the VMD algorithm, decompose the bearing fault signal to obtain a series of IMF. Then, the kurtosis and correlation coefficient criteria are used to select a group of components that contain the most information, and the fault signal is reconstructed on this component, and then the reconstructed signal is analyzed by GRCMDE to form a fault eigenvector. Finally, KPCA is used for dimensionality reduction to select input features and input into KELM for classification and recognition. The experimental results show that this method can effectively extract the bearing fault features of reciprocating compressors, and the eigenvectors have good separability, and realize the sub-health recognition of bearing fault features of reciprocating compressors.

A simplified method was developed for estimating the cooling time of the cylinder-piston group in a positive displacement hybrid reciprocating power machine with regenerative heat exchange when operating in a pumping mode. Due to the absence of thermal resistance of the partition, it is more efficient than recuperative heat transfer, which is currently implemented in all reciprocating compressors. A numerical experiment was carried out, in which the values of the Reynolds number and the heat transfer coefficient in the working chamber of the machine, the modes of fluid flow along the rotation angle of the crankshaft in the working chamber of the machine, as well as the angular velocities of the crankshaft for the implementation of the turbulent flow, were established. It was found that 95–96% of the heat from the cylinder-piston group was removed by the coolant. The optimum rotation frequency, which provides high energy performance and sufficiently fast cooling of the elements in the cylinder-piston group, is ~ 200 rpm.

This study numerically analyzes the flow-induced noise of a hermetic reciprocating compressor (HRC) using fluid-structure interaction (FSI) analysis, acoustic excitation source modeling, and acoustic-structure interaction (ASI). Acoustic modeling, which calculates acoustic excitation source strength from pressure pulsation of the refrigerant caused by the behavior of the piston and valve, is presented for predicting flow-induced noise. Pressure pulsation is obtained by performing FSI analysis of the internal flow and valve behavior of the compressor. Based on this, acoustic excitation source strength is calculated by applying acoustic modeling such as planar wave conditions. Using the calculated acoustic excitation source, ASI analysis between the refrigerant and the shell housing is performed to predict the radiated flow-induced noise of the hermetic compressor. The numerical noise analysis results showed good agreement with experimental results, especially with high accuracy in the frequency band of cavity resonance. This study reveals that the primary cause of flow-induced noise of the HRC is acoustic waves generated by the structural movement of the piston and valve, providing important implications for future compressor design improvements.

Gas leakage is one of the main sources of inefficiencies in low-capacity reciprocating compressors, undermining the compressor performance by reducing the mass flow rate and increasing the energy consumption. In reciprocating compressors, leakage in the piston–cylinder clearance is driven by the piston motion and pressure difference between the compression chamber and the internal environment of the shell. This paper reports a parametric numerical analysis of leakage in the piston–cylinder clearance of a low-capacity reciprocating compressor. A simulation model based on the Reynolds equation is applied throughout the compression cycle to assess the effect of the compressor operating conditions, clearance geometry, piston velocity and piston secondary motion on the leakage and compressor performance. A 3D CFD model is also developed to validate the Reynolds leakage model and to evaluate the effect of the piston secondary motion on leakage, assuming the piston is fixed with predetermined eccentricities. The results show that the compressibility effects are very relevant to estimate the gas leakage. The simulations also revealed that leakage is more detrimental to the compressor performance when it is operating in low back-pressure conditions. Additionally, the piston secondary motion can intensify the gas leakage in the piston–cylinder clearance by up to 90%. On the other hand, the piston velocity only plays a minor role in assessing the leakage.