# Impact Features Extracting Method for a Reciprocating Compressor Based on the ABC-SGMD Model
## 4 Fault Simulation Experiment  
To further validate the effectiveness of the aforementioned method in processing normal and faulty data signals from reciprocating air compressor valves under actual operating conditions, the reciprocating air compressor valve experimental simulation bench in our laboratory was used, as shown in Figure 6. At a speed of 20 revolutions per second, three operating conditions of the valve were simulated (normal valve, intake valve wear, and exhaust valve spring failure), as illustrated in Figure 7.  
The vibration signals from the intake valve under different operating conditions are collected and analyzed in the time domain, resulting in the time domain plot shown in Figure 8a. This plot reveals the changing patterns and characteristics of the vibration signals: compared to signals under normal conditions, the signals under spring failure conditions exhibit more pronounced impact characteristics in each cycle due to the spring's reduced ability to absorb vibration and impact energy, resulting in larger amplitude fluctuations. In contrast, with the wear effect of the intake valve, the irregular entry of gas into the cylinder results in denser, lower-amplitude noise signals under intake valve wear conditions. At the same time, the potential leakage may result in some signal amplitudes being lower than the original signal. To gain a deeper understanding of the frequency domain characteristics of the vibration signals, Fourier transform is performed on the collected signals, as shown in Figure 8b. Here, the horizontal axis represents the frequency, and the vertical axis represents the signal amplitude. In this paper, the frequency distribution of the signals is analyzed to understand the operational state and vibration characteristics of the gas valve. Figure 6: Fault simulation test bench. (1—three-phase motor; 2—frequency converter; 3—acceleration sensor; 4—reciprocating air compressor; 5—signal collector; 6—PC). Figure 7: Simulated faults. First, the ABC-SGMD method is utilized to decompose the vibration signals under three working conditions into a series of Intrinsic Mode Function (IMF) components, each group of which corresponds to a local vibration component. With the proposed method, different frequency and amplitude components from the signal could be distinguished, facilitating a more detailed analysis of its frequency domain characteristics. The IMF component graph from the ABC-SGMD procedure is shown in Figure 9, from which the local vibration characteristics and frequency components of the vibration signals under each working condition are illustrated. By analyzing these components, a deeper understanding of the complex frequency domain structure of the vibration signals under each working condition could be gained, thereby providing a robust basis for further signal processing and feature extraction. Here, the signal is decomposed into various components, and the kurtosis value and correlation coefficient with the original signal are calculated from each component. According to the selection criteria, components with a kurtosis value greater than 3 and a correlation coefficient greater than 0.6 are retained [28], and their union for the reconstructed signal is taken as the object of further analysis in this study. Based on a previous analysis, it was concluded that the impact characteristics resulting from air reduction could be effectively captured by properly selecting RCMDE, ydz, and Vpp from the decomposition results of the initial vibration signal. To observe the differences in characteristic values under varying working conditions, three sets of data were randomly selected, and their characteristic values are compared in Table 3. This analysis of the signal component characteristics under different working conditions provides a solid foundation for the subsequent fault diagnosis.