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 three-dimensional fluid–structure interaction model is proposed for the discharge valve movement in a rotary compressor. The compression chamber pressure measured by the experiment confirmed that the fluid–structure interaction model is able to predict the valve dynamic behavior precisely. To demonstrate that the fact of the discharge port being covered partly by the cylinder and the roller cannot be ignored in the valve model, another fluid–structure interaction model in which the discharge port is not covered and the cylinder shape is simplified to be cylindrical is also specially presented. In this simplified fluid–structure interaction model, the flow energy loss through the discharge valve is 68.8% of that in the fluid–structure interaction model, and the impact velocity of the valve reed impacting on the retainer is 2.9 m/s higher than that in the FSI model. It is found that the valve reed is obviously inclined because the discharge port is covered partly by the cylinder.

The health management of the reciprocating compressor is crucial for its long term steady operation and safety. Online condition monitoring technology for the reciprocating compressor is almost mature, whereas the fault diagnosis technologies are still insufficient to meet the need. Therefore, in this paper, a novel fault detection method for the reciprocating compressor based on digital image processing and artificial neural network (ANN) was proposed. This method is implemented to the sectionalized pressure–volume (p–V) curves, which are obtained by dividing a working cycle in the cylinder into four thermal processes, including expansion, suction, compression and discharge. Hit-or-miss transform is adopted to extract the comprehensive gradients of expansion and compression curves, and vertical projection transform is applied to extract the vertical projection features. Finally, all of the features are fed to an ANN to do classification. To validate the proposed method, a seeded fault testing was conducted to collect real running data. The results showed that the new approach shows a good performance, with a high classification accuracy of 97.9%.

With the continuous trend toward high speed and large size, the reciprocating compressor crankshaft faces more serious potential threat of crack due to vibration. Therefore, early stress analysis of the crankshaft must be done thoroughly in the design stage. This article introduces a method, which combines flexible body dynamic and finite element analysis to calculate stress of the crankshaft. In this method, the load variation with time is obtained by flexible body dynamics simulation of crank and connecting rod mechanism. After that loads are loaded on finite element model of the crankshaft, and then the stress of the crankshaft is calculated in time domain. This stress can be utilized to do fatigue analysis and predict the life of crankshaft. Using this method, stress of a practical crankshaft, belonging to 6M51 reciprocating compressor, is calculated. The result showed that the maximum von Mises stress is 158 MPa, and the estimated life, which was calculated by Palmgren–Miner linear damage accumulation theory, is 2.0230e + 007 hours.

The damage of vulnerable components inside the cylinder of reciprocating compressor, including the valve, piston ring, packing and piston ring, will cause the unexpected shutdown of the compressor unit. The indicator diagram which reflects the thermodynamic process in the cylinder is suitable for fault diagnosis of vulnerable components. However, most of the published fault diagnosis methods based on indicator diagram are aimed at the fault diagnosis of gas valve. In addition, the extracted features lack physical meaning in most fault diagnosis methods using machine learning algorithm, which is not conducive to be widely applied in practical engineering. In this study, features with definite physical meaning, including average suction pressure, average discharge pressure, area of indicator diagram and centroid coordinates of indicator diagram, are extracted from indicator diagram, and the threshold database of features under normal states and various fault states is established according to the contrast experiment. The results of the experiment show that the thresholds of the extracted parameters are obviously different under normal states and various fault states. During fault diagnosis, several groups of indicator diagrams of the compressor to be diagnosed are collected at first. After feature extraction, the extracted features are compared with the thresholds under different compressor states to obtain the average numbers of features within the threshold range under different compressor states to determine the compressor states. The accuracy of the method for judging whether the compressor is faulty or normal could reach 98.3%. Furthermore, the accuracy of identifying individual faulty components and multiple faulty components could reach 86.86%. The reason for the low overall diagnostic accuracy is that certain faults have similar effects on the features extracted from indicator diagram. The proposed method is believed as an excellent fault diagnosis method for the vulnerable components inside the cylinder of reciprocating compressor.

The demand for high cooling capacity heatsinks has been increasingly promoted due to the fast elevated heat generation of modern electronic devices. High‐performance microscale heat exchangers are often accompanied by considerable pressure drop penalty that limits their applications. In order to avoid large pressure drop while maintaining high heat transfer rate, a macroscale curved channel was proposed with applying the periodical wave structure on the channel sidewalls. A three‐dimensional conjugated numerical model was established, and the effects of wave number and average radius on the hydrothermal performance were investigated. It was found that the heat transfer performance in the smooth‐curve channel is significantly improved due to the wavy sidewalls with a moderate pressure drop penalty. The heat transfer enhancement is more noticeable in the channels with more wave units and smaller average channel radius. Furthermore, it was noted that the periodical thermal entrance effect exists on the inner and the outer convective surfaces of curve‐wave channels, which can be attributed to the variations of the secondary flow with the wave direction along the channels.