## 5 Results and Discussion  
This section principally validates the proposed fault identification framework constructed in Section 4 for an MDTF gearbox under inconsistent rotational speeds based on the data collected from a real-world testing platform. The effectiveness of this model is entirely evaluated based on the following operations: LADT, visual enriching feature configuration (WVI's), and fault identification based on DCNA. Experimental Verification of the Effective Performance of LADT and Wealthy Feature Pool Configuration Created by WVI  
As explained in the introduction section, the real-world gearbox vibration signals originally contain informative components and random background noise. The disturbance noises appear randomly, and they can affect the informative components. Thus, in the raw form of the vibration signal, it is very difficult to separate the original informative components from the background noise. Furthermore, the operation behaviors of MDTF gear faults reflected in the vibration signal are too similar. In other words, to discriminate these kinds of faults, the use of enhanced techniques is required. The LADT approach is the key technique of this study for effective noise cancellation and for separating the original fault-related components from the high noise vibration signals. Before being fed to LADT, the raw vibration signals, gathered from the experimental gearbox testbed, were processed by down-sampling and low-pass filtering to attain vibration signals with the frequency range of 0-10,922 Hz, according to the real frequency working range of the acceleration sensor for removing the redundancy fractions. These output signals are named raw-filtered vibration signals. LADT tries to divide each raw-filtered vibration signal into many sub-signals, so that their frequency spectrums are principal frequency segments, by \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **The Experiment** & **Number of Samples** & **The RPM of Data Samples** \\ \hline \multirow{3}{*}{Scenario 1} & \multirow{3}{*}{Experiment 0} & Training samples: 3360 & 60\% of All four speeds dataset \\ \cline{3-4}  & & Testing samples: 2240 & 40\% of All four speeds dataset \\ \hline \multirow{6}{*}{Scenario 2} & \multirow{3}{*}{Experiment 1} & Training samples: 2800 & The shaft speeds: 300 RPM, 600 RPM \\ \cline{3-4}  & & Testing samples: 1400 & The shaft speed: 900 RPM \\ \cline{2-4}  & & Training samples: 2800 & The shaft speeds: 600 RPM, 900 RPM \\ \cline{2-4}  & & Testing samples: 1400 & The shaft speed: 1200 RPM \\ \cline{2-4}  & & Training samples: 2800 & The shaft speeds: 900 RPM, 1200 RPM \\ \cline{2-4}  & & Testing samples: 1400 & The shaft speed: 300 RPM \\ \cline{2-4}  & & Training sample: 2800 & The shaft speeds: 1200 RPM, 300 RPM \\ \cline{2-4}  & & Testing samples: 1400 & The shaft speed: 600 RPM \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of the dataset for training and testing with RPM in the experiment setup. applying the series of the non-overlapped band-pass filters along the frequency spectrum of the vibrations signal (0-10,922 Hz). Next, the ANR-GRS technique [22] is applied on each principle frequency segment to achieve a locally optimized sub-band from each input sub-signal based on the localized optimal parameters. The final optimized output of the LADT module is a summation of all locally optimized sub-bands corresponding to each input vibration signal. The visual analysis of frequency spectrums of three vibration signals (a raw-filtered vibration signal, the output signals of the ANR-GRS module, and optimized sub-bands from the LADT module) are illustrated in Figure 10.  
Figure 10: The frequency spectrum analysis of the input and output signals of LADT in comparison with the performance of ANR-GRS for an example vibration signal of DT3 at 900 RPM. As shown in Figure 10, the superiority of the localized adaptive process of the LADT module for denoising is proven.