## 5 Experimental Case Planning, Results, and Analysis
### Experimental Case  
#### 5.2.1 Case Study 1--Signal Processing Techniques  
1. Case design and dataset planning  
In this experimental case, a 2 HP induction motor was used to perform load tests under different operating conditions using its vibration signals. The training dataset was well-balanced, providing ample data for each category. The same model architecture was employed to compare different signal processing techniques. The collected data underwent processing using CWT and the two-dimensional time-domain signal transformation. CWT transforms the original vibration signal into a time-frequency image, where the x-axis denotes time, the y-axis denotes frequency, and color changes indicate the magnitude of energy. The two-dimensional time-domain signal transformation converts the original vibration signal into a two-dimensional time signal image by mapping the maximum and minimum values of the vibration signal in a given time segment to a scale between 255 and 0. The AI model's training dataset was segmented into training, validation, and test sets, as detailed in Table 4. Load testing was conducted, and classification metrics were calculated to assess the impact of different signal processing techniques on the fault diagnosis of induction motors under varying load conditions. 2. Analysis of experimental results  
In the induction motor faults diagnosis, signal preprocessing methods involve important information such as frequency components, microscopic vibrations, and signal-to-noise ratio. These pieces of information are closely related and jointly affect the effectiveness of fault diagnosis. Time-domain signals have limitations in the analysis of induction motor faults, making it difficult to determine operational conditions. This is because time-domain signals often contain multiple frequency components, and different frequencies interfere  
Figure 6: Induction motor data measurement platform. \begin{table}
\begin{tabular}{c c} \hline \hline
**Device No.** & **Device Name** \\ \hline
1 & Tachometer \\
2 & Torque meter \\
3 & Dynamometer control box \\
4 & Induction motor \\
5 & Coupling \\
6 & Load \\
7 & Adjustable platform \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Device item. with each other, making it hard to identify fault characteristic signals. Although analysis of low-frequency components can diagnose motor faults, low-frequency components can be disturbed by the resonance generated by faulty mechanical elements and other components, leading to a decline in diagnostic effectiveness. However, high-frequency components can effectively observe the microscopic vibrations of mechanical components, as the signal-to-noise ratio is typically high. In high signal-to-noise ratio conditions, this helps to minimize interference from vibrations caused by other mechanical components, making fault features in the signals more distinct and easier to detect and analyze across various faulty elements. In addition, different faulty mechanical elements will generate impacts within different time intervals. Therefore, the time resolution within the high-frequency range has significant implications for diagnosing the operational status of the motor. By performing spectral analysis of microscopic structural vibrations, one can more accurately assess the operational status of the motor, and timely discover and prevent potential faults. However, the mother wavelet function of CWT possesses scaling and translation characteristics, making the spectrum of CWT have high resolution in the time-frequency domain. It allows visualizing the signal's frequency distribution changes over time and uses different colors to represent the amplitudes of different frequencies. Thus, the spectral map represents the energy distribution of the signal under different frequencies, which aids in analyzing signal characteristics and changes, and effectively provides an AI model for diagnosing the operational status of the motor.