## 4 Results and Discussion  
### Parametric Analysis  
The parametric analysis method, currently the most commonly used method for analysis of the AE signals, utilizes the characteristics of multiple simplified waveforms to characterize the AE signals. This method has proved that many engineering problems can be solved effectively after decades of research [35]. Based on this method, it is clear to identify the health status of the engine through the AE parameters, such as the amplitude, the signal strength, the RMS, and the average signal level (ASL).  
Table 2 lists the variations of the amplitude, the signal strength, the RMS, and the ASL as \(J\) varies from 0 to 43.64%. As we can see, the parameters of the AE signal increased with the uniform increase of distortion range in real-time.  
Figure 5: Principle of the AE technique.  
For a more intuitive description of the relationship between the AE parameters and the \(J\), the line chart of the amplitude, the signal strength, the RMS, and the ASL relative to the change of \(J\) are plotted in Figure 6. The variations of the AE parameters can be divided into three stages of I, II, and III. In stage I, the AE parameters initially did not show a clear upward trend until \(J\) reached 10%. With the increase of \(J\), the parameters increased rapidly and reached the maximum value when \(J\) was about 30%. There is an exponential relationship between the AE parameters and \(J\). In stage II, the AE parameters did not increase with the increase of \(J\), while a slight fluctuation phenomenon occurred. In stage III, as \(J\) continuously increased above 43.64%, the engine tended to surge, which should be strictly avoided.  
In the same experiment, the vibration monitoring technique was tested to study the inlet distortion of the engine as well. Figure 7 shows the relationship between the parameters of the vibration  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Time s** & \(\frac{H_{1}}{D}\) & \(J\) & **Amplitude** & **Signal Strength \(\times\)** & **RMS** & **ASL** \\  & & & **dB** & **10\({}^{-3}\) Vs** & **V** & **dB** \\ \hline
0 & 0.00\% & 0.00\% & 86 & 3.447 & 0.4282 & 75 \\
1 & 1.80\% & 0.41\% & 86 & 3.441 & 0.4270 & 75 \\
2 & 6.10\% & 2.51\% & 86 & 3.409 & 0.4220 & 75 \\
3 & 10.50\% & 5.59\% & 87 & 3.430 & 0.4302 & 75 \\
4 & 15.00\% & 9.41\% & 87 & 3.491 & 0.4356 & 75 \\
5 & 19.40\% & 13.63\% & 89 & 3.614 & 0.4680 & 76 \\
6 & 23.50\% & 17.19\% & 91 & 4.084 & 0.5482 & 77 \\
7 & 28.30\% & 23.26\% & 96 & 4.751 & 0.6424 & 79 \\
8 & 32.70\% & 28.42\% & 96 & 6.550 & 0.9056 & 82 \\
9 & 37.10\% & 33.76\% & 96 & 6.550 & 0.8636 & 81 \\
10 & 41.90\% & 39.73\% & 96 & 6.307 & 0.8064 & 81 \\
11 & 45.00\% & 43.64\% & 96 & 6.407 & 0.8334 & 81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variations dependency of the parameters of the AE method on \(J\).  
Figure 6: Variation of parameters of AE with \(J\): (**a**) amplitude-\(J\); (**b**) signal strength-\(J\); (**c**) RMS-\(J\); and (**d**) average signal level (ASL)-\(J\).  
monitoring technique (taking the valid values as an example) and \(J\). When \(J\) reached 14%, the parameter of vibration monitoring technique begins to show a clear upward trend, lagging behind the response of the AE monitoring technique, demonstrating that the AE technique is more sensitive to the identification of inlet distortion.  
To avoid serious inlet distortion, the engine was strictly forbidden to operate in stages II and III. Therefore, it is of significance to identify the \(J\) as soon as possible before entering stage II and III. Additionally, the AE monitoring technique is more sensitive to identify the inlet distortion of the engine in comparison with the vibration monitoring technique.