## 3 ITSCF Model and Feature
### The Pre-Processing System Framework  
Figure 1 illustrates the proposed innovative framework for the IPMSM ITSCF feature extraction system developed in this study. This framework aims to extract the ITSCF features in IPMSMs using wavelet transform, providing critical time-frequency analysis information necessary for the big data preprocessing required in the design of deep learning models. This system is capable of performing fault diagnosis analysis on IPMSMs in various application fields, including electric vehicles (EVs), fans (FANs), and pumps (PUMPs), based on operating conditions under different speed commands in IPMSM motors from these diverse applications. The system offers the flexibility to specify a particular range of speed commands or a comprehensive range, such as 1 to 6000 RPM. Based on the given speed command for the IPMSM operation, the framework generates corresponding voltage and current waveform information. Subsequently, a Voltage and Current Waveform Converter, as illustrated in the figure, is employed to transform and analyze these waveforms. Based on the specified speed command for the IPMSM, the framework generates corresponding voltage and current waveforms. These waveforms are transformed and analyzed using the Voltage and Current Waveform Converter depicted in the figure. The innovation of this study lies in utilizing wavelet transform for time-frequency conversion to further analyze and extract potential frequency bands and energy error information from the motor's voltage and current signals.  
The innovative features of this framework are illustrated in the WT process block diagram, where the voltage and current waveform information is simultaneously generated for multiple speed commands corresponding to the motor model. By employing the DWT across multiple frequency bands, this approach allows for a multi-scale decomposition of the signals. Based on computational requirements, each frequency level is processed to further analyze and extract characteristic frequencies of ISCF in the motor. The framework ultimately produces ITSCF time-frequency features and ITSCF energy level information, which can be used not only for motor fault diagnosis but also to determine the range of command failures based on energy percentages. The massive dataset of normal and abnormal waveforms generated by this framework can serve as a valuable reference for designing failure models for various types of IPMSMs. The innovative features of this framework are illustrated in the WT process block diagram, where the voltage and current waveform information is simultaneously generated for multiple speed commands corresponding to the motor model. By employing the DWT across multiple frequency bands, this approach allows for a multi-scale decomposition of the signals. Based on computational requirements, each frequency level is processed to further analyze and extract characteristic frequencies of ISCF in the motor. The framework ultimately produces ITSCF time-frequency features and ITSCF energy level information, which can be used not only for motor fault diagnosis but also to determine the range of command failures based on energy percentages. The massive dataset of normal and abnormal waveforms generated by this framework can serve as a well-defined reference for a preprocessed dataset used in deep learning analysis.  
Figure 1: IPMSM ITSCF feature extraction system framework.