# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 4 Fault Diagnosis in Railway Traction Drives
### Fault Diagnosis in Sensors  
Several sensors are located in a Railway traction drive. A review is presented in [21]. Current and voltage sensors, such as catenary current, motor phase current and DC-link voltage, are based on Hall-Effect. In Hall-effect sensors, offset and gain faults can arise due to magnetic core degradation, magnetic properties variation with temperature or change of the magnetic field orientation [88]. Speed sensors in Railway applications are normally variable reluctance sensors, or Hall-Effect sensors. Commonly, sensors take part in the control strategy, so hard faults, such as sensor disconnection; have as consequence the protections activation. Thus, early diagnosis is not possible in these cases, although it can help for a faster corrective maintenance. Among the sensor fault diagnosis articles in Railway traction drives, in [67] a Luenberger observer based approach for DC-link voltage and catenary current sensor fault diagnosis is presented The observer design is based on the rectifier model. A bank of residuals, taken from Luenberger observer errors and open loop estimations, are used for fault isolation. The FDI approach is validated in an experimental test bench. Similar to this, in [31] another approach is presented, using the input filter model for a DC supplied traction drive. In this case, the approach is validated in a Hardware-in-the-loop platform (HIL) with a commercial TCU for a tram. Furthermore, a fusion of different FDI approaches is proposed to improve the reliability of the fault isolation. A unknown input observer (UIO) is proposed in [68], using the inverter model instead of the rectifier model and in order to get a FTC. UIO is implemented due to the unknown inputs in the inverter model. A sliding mode observer (SMO) is proposed in [69], again with the rectifier model. Compared to Luenberger observer, SMO can be applied to non-linear systems and it has a better robustness under modelling uncertainties and measurement noises. In [70] a bank of SMO observers is proposed for DC-link voltage and catenary current sensors. Later, in [33] a SMO in combination with adaptive techniques in order to detect the different fault modes for a DC-link voltage sensor is presented. In [69] a SMO-based sensor FDI for incipient faults is presented. Adaptive thresholds are proposed in order to improve detectability. Finally, [71] presents the DC-link and catenary current sensors fault reconstruction based on a SMO and the equivalent injection signal. Equivalent injection signal represents the average value to maintain sliding motion. The application of equivalent injection signal requires the treatment of sensors as actuators, so a new augmented system should be designed from original system and sensors to be detected. The SMO for the augmented system is shown in Figure 12, being \(\mathbf{G_{1}}\) and \(\mathbf{G_{n}}\) feedback matrix gains, whereas \(\mathbf{v}\) is the nonlinear term responsible for sliding motion. Figure 11: Comparison of stator current frequency spectrums for balanced and unbalanced motor phases. Moreover, this approach is validated in a HIL platform too, composed of a Real Time simulator and a commercial TCU. The fault reconstruction for a gain fault injected in DC-link voltage sensor is shown in Figure 13.  
On the other hand, in [72] another approach is presented for a nonlinear system, presented as a Takagi-Sukeno Fuzzy model. This approach is based on the total measurable fault information residual (ToMFIR) for fault isolation, whereas a bank of observers is used for residuals generation. ToMFIR technique is presented in [73] too. This article affirms that the solution proposed can achieve a better solution for sensor fault detection than observer-based approaches, as well as a more general FDD framework. A HIL platform is used for validation, composed of a Real Time Simulator, an induction motor, a rectifier and a current sensor.