In this paper, a nonlinear dynamic model of the single-stage reciprocating compressor system with a rub-impact fault caused by subsidence is developed, considering the piston rod flexibility. Meanwhile, different rub-impact scenarios of the crosshead including no corner, a single corner, two opposite corners, and two adjacent corners of the crosshead impacting on the slide, are considered in this model. Numerical simulation results show that the subsidence, time-varying load, and piston rod flexibility have significant effects on the vibration response of the reciprocating compressor system with rub-impact, where the larger the subsidence and time-varying load, the more intense the vibration response. Moreover, compared to the rigid piston rod, the flexible piston rod causes the system to generate stronger shock. In the rub-impact, the crosshead appeared in three different motion modes: noncontact; permanent contact; and collision – and most of the collisions occur in the form of two adjacent corners impacting on the slide. In addition, strange attractors are observed in the phase space trajectories, and the existence of chaotic behavior is verified using the Poincaré section method and maximum Lyapunov exponent method.
Precast concrete structure is the building industrialization of the sure route. It can realize the construction process of low energy consumption and low emission and effectively meet the green development requirements of the construction industry. Based on prestressing technique, the connections of the precast concrete structure obtain prestress producing integrate joints and continuous frames, which improve the seismic safety and are applied widely in the earthquake area. To study seismic behavior of prestressed fabricated concrete frame structure, the experiments on the concrete frame under dynamic loading and low reversed cyclic loading were carried out. The single-span three-story prestressed fabricated concrete frame can accurately represent the load-carrying capability and the failure mechanism of multistory frame. Results of the study show that experimental specimens have good behaviors such as full hysteresis curves, proper displacement restoring capacity, and energy dissipation; the maximum interlayer drift ratio arrives 0.27% which has no damage to the frame in small earthquakes subjected to the 102 gal peak ground acceleration; the frame is repairable in moderate earthquakes when the maximal interlayer drift ratio arrives 0.73% subjected to the 204 gal peak ground acceleration; plastic hinges appeared at the ends of beam under low reversed cyclic loading firstly where the section curvature ductility factor ranges from 3.64 to 5.62; biaxial compression is acquired at beam-column joints with the help of column axial force and horizontal prestressing force; the beam fails before the column in the prestressed fabricated concrete frame at interlayer drift ratio between 1.56% and 2.56%.
With the development of machine learning in recent years, the application of machine learning to machine fault diagnosis has become increasingly popular. Applying traditional feature extraction methods for complex systems will weaken the characterization capacity of features, which are not conducive to subsequent classification work. A reciprocating compressor is a complex system. In order to improve the fault diagnosis accuracy of complex systems, this paper does not use traditional fault diagnosis methods and applies deep convolutional neural networks (CNNs) to process this nonlinear and non-stationary fault signal. The valve fault data is obtained from the reciprocating compressor test bench of the Daqing Natural Gas Company. Firstly, the single-channel vibration signal is collected on the reciprocating compressor and the one-dimensional CNN (1-D CNN) is used for fault diagnosis and compared with the traditional model to verify the effectiveness of the 1-D CNN. Next, the collected eight channels signals (three channels of vibration signals, four channels of pressure signals, one channel key phase signal) are applied by 1-D CNN and 2-D CNN for fault diagnosis to verify the CNN that it is still suitable for multi-channel signal processing. Finally, further study on the influence of the input of different channel signal combinations on the model diagnosis accuracy is carried out. Experiments show that the seven-channel signal (three-channel vibration signal, four-channel pressure signal) with the key phase signal removed has the highest diagnostic accuracy in the 2-D CNN. Therefore, proper deletion of useless channels can not only speed up network operations but also improve diagnosis accuracy.
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