**3.1 Multiple fault injections**

To obtain the validation data sets, four kinds of faults with various severity levels are injected to the dSPACE Control Desk. For the unified modeling regarding to the

**Figure 3.** *HST traction system experimental setup for agile fault diagnosis.*

*High-Speed Train Traction System Reliability Analysis DOI: http://dx.doi.org/10.5772/intechopen.111911*

normal and fault conditions, the experiments are executed under the steady-operation speed 110 km/h. Based on the motor model in [16], the electromagnetic torque signal sensitive to the faults can be easily reconstructed with the measurable weak current and speed signal. Considering the agile diagnosis requirements, the sampling frequency is 200 *μs* and the simulation time is 1 *s*. In order to explore the torque dynamics with various severity levels, **Figures 4**–**8** show the fault scenarios with a 50% ratio injecting at 0.6 *s*. From **Figures 4**–**8**, the incipient faults can be agilely detected after the fault injections, which means the torque signal is suitable for fault feature extraction and keeps consistent with the theoretical description in [17, 18].

## **3.2 Fault feature extraction**

Based on the fault severity attribute knowledge in terms of (20–30%, 30–40%, 40– 50%, and 50–60%), each group contains 5000 samples. To test the robustness of the proposed fault coding based attribute transfer method, 60 coding groups of train/test split have been collected. Then, EMD and IMF energy entropy are utilized to determine the fault feature set. By testing a large amount of the torque signal data, the

**Figure 4.** *Bearing fault dynamics with the electromagnetic torque signal.*

**Figure 5.** *Rotor fault dynamics with the electromagnetic torque signal.*

**Figure 6.** *Stator fault dynamics with the electromagnetic torque signal.*

**Figure 7.** *Air-gap fault dynamics with the electromagnetic torque signal.*

**Figure 8.** *Rotor and stator fault dynamics with the electromagnetic torque signal.*

*High-Speed Train Traction System Reliability Analysis DOI: http://dx.doi.org/10.5772/intechopen.111911*

**Figure 9.** *The EMD of Air-gap fault signal.*

default components are seven. As an example, the EMD for the electromagnetic torque signal corresponding to the air gap eccentricity fault is shown in **Figure 9**. Compared with the original signal in **Figure 4**, it can be observed that the signal part mostly lies in the first five IMFs and the dominant part of the noise exist in the sixth and seventh IMFs. Based on the coding scheme, the first five IMFs are provided in **Table 2**. **Table 2** shows that these encoded feature matrix dynamics are consistent with the fault attribute knowledge in **Table 1**. As a result, the fault attribute transfer can be achieved.

To explore the feasibility of the sharing of attribute learners, 30 coded groups in each fault mode are randomly selected to train the SVM model, and the remaining 30 groups for each fault category are treated as the test data. For the training and testing of the SVM model, fault type 1 represents the normal state while the 2, 3, 4, 5 and 6 indicate air gap eccentricity fault, stator interturn short circuit fault, broken rotor bars fault, bearing fault and compound fault, respectively. The test samples are stored in the following order: 1–30 for the normal state (label 1), 31–60 for the air gap


**Table 2.**

*Energy entropy with various fault types.*

eccentricity fault (label 2), 61–90 for the stator interturn short circuit fault (label 3), 91–120 for the broken rotor bars fault (label 4), 121–150 for the bearing fault (label 5) and 151–180 for the compound fault (label 6).
