# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 4 Experimental Verification  
To verify the feasibility of the proposed method in engineering application, it is planned to use the gearbox fault test bench for experiments. The experiment bench is shown in Figure 12, which includes a triple-phase asynchronous motor, connector, gear speed increase, torque and rotational speed sensor, planetary reducer, triaxial acceleration sensor, and magnetic powder loader. The magnetic powder loader is used to adjust the load. The triaxial acceleration sensor model is YD77 SA, the sensitivity is 0.01 V/ms\({}^{-2}\), and the sensor placement is shown in Figure 12.  
During the test, the original bearing was replaced by a bearing with cracks in the inner ring and flaking of the inner ring. The bearing types are NJ210 and NJ405, as shown in Figure 13. The bearing speed is 1500 rmp. According to the calculation formula of the bearing inner ring fault frequency, the fault frequencies of NJ405 and NJ210 are 147.66 Hz and 231 Hz, respectively, as shown in Table 1.  
Figure 11: MSE value of the component signal (reconstructed signal is decomposed by CEEMD). Figure 12: Experiment bench, 1—Magnetic Powder Loader; 2—Gear speed Increaser; 3—Torque and Rotational Speed Sensor; 4—Planetary Reducer; 5—Triaxial Acceleration Sensor #1; 6—Triaxial Acceleration Sensor #2; and 7—Triple-phase Asynchronous Motor. The time and frequency domain of vibration signals in the experiment are shown in Figure 14. In Figure 14a, the periodic impact of the collected vibration signal is not very obvious. Figure 14b is the frequency domain diagram obtained by the FFT transformation of the vibration signal. It can be seen that the fault period is submerged by noise and it failed to judge the existence of the fault. The vibration signals are processed by CEEMD and the method proposed in this article, and the effects of each method are compared. Figure 15a is the time domain diagram of the vibration signal decomposed by CEEMD, and Figure 15b is the frequency domain diagram of the vibration signal obtained by FFT. The signal in the figure is decomposed into 15 layers in total, and from the time domain diagram and frequency domain diagram in the corresponding diagram, we can observe that the component signals, IMF1-IMF5, are high-frequency \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rotating Speed n** & **NJ405** & **NJ210** \\ \hline
1500 rpm & 147.66 Hz & 231.2 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Failure frequency. Figure 14: Time-domain and frequency-domain graphs of experimentally acquired signals. (**a**) Time domain. (**b**) Frequency domain. Figure 13: Faulty bearing. (**a**) NJ405 bearing. (**b**) NJ210 bearing. noise signals. Bearing failure signals are not obtained in the IMF6-IMF15 component signals, so the vibration signal fails to extract the bearing failure signal through traditional CEEMD decomposition. Employing the method proposed in this paper to decompose the vibration signal. The threshold needs to be determined, so this paper uses the scale factor \(Q=10\) to calculate the MSE value of 10,000 sets of random noise. The signal length of each set of random noise is consistent with the vibration signal. The result is shown in Figure 16. The MSE value of the noise signal in Figure 16 varies from 0.705 to 0.745. In this paper, the scale factor \(Q=10\) is used to calculate the MSE values of IMFs and 10,000 sets of random noise, and the minimum value of 10,000 sets of random noise is used as the threshold, and Figure 17 is drawn. The pink line in Figure 17 is the minimum value of 10,000 sets of random noise, and the red bar is the MSE value of each IMF. In this paper, IMFs signals larger than the threshold are eliminated, so the IMF1-IMF3 signals in the figure below are eliminated. Reconstruct the remaining IMF4-IMF15 signals and perform FFT transformation to obtain Figure 18. In Figure 18, red is the reconstructed signal and blue is the original signal.