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.
In order to explore the failure mechanism of a reciprocating compressor system with clearance fault, we implemented a computational framework whereby a simulation model of the mechanism is established using ADAMS software in this paper, and a typical reciprocating compressor model is introduced to validate the design model. In this work, the joint clearance faults between the crankshaft and linkage, between the linkage and crosshead, and in both locations are taken into account computationally. These faults are one of the major causes of vibration. Through dynamic calculation and analysis of a system with clearance fault, the simulated results show that these clearance faults directly influence the vibration. The larger the gap size, the more severe the vibration and the higher the amplitude of the vibration. Furthermore, the clearance number also affects the vibration greatly.
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