# A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives
## 4 Fault Diagnosis in Railway Traction Drives
### Fault Diagnosis in Electric Machines  
Induction motors are the most usual electric machines in railway applications, although recently permanent magnet motors have been utilized too for railway applications [82]. The main faults in induction motors are rotor faults, stator windings faults and mechanical faults [30]. The rotor faults are mainly due to broken rotor bars or core damage. Stator winding faults are given by inter-turn short-circuits. On the other hand, there is a variety of mechanical faults, such as eccentricity and bearing faults. Bearing faults are the most usual faults in electric machines according to [83].  
Despite the variety of fault diagnosis approaches for induction machines presented in the literature [84; 85; 86; 87], the application to Railway traction drives is more limited. Thus, in [63] the rotor bars faults are detected by Motor current signature analysis (MCSA) for an induction motor used for a high speed railway application. MCSA is one of the most usual signal-based FDI approach for fault detection in electric machines. MCSA is based on stator current frequency spectrum analysis for faulty induction machine in comparison to a fault-free induction machine and its main drawback is that it can be only applied in steady state. An example for an induction motor with unbalanced phases is shown in Figure 11. Faults such as rotor bars breakage, eccentricity or inter-turn short-circuits can be detected [30] with this strategy. On the other hand, in [64] the oscillation that arises in the flux when a rotor fault occurs is analysed. The oscillation frequency is twice the slip frequency. This approach, called Virtual Current Technique (VCT), can be used in transient and steady states. Furthermore, compared to other techniques as Vienna Monitoring Method (VMM), VCT allows linking the oscillation and the number of broken rotor bars. FFT is used too in [65] to detect dirt in motors, as the accumulation of electromagnetic material generates changes in the flux harmonics amplitudes. Finally, in [66] the motor winding insulation degradation is detected. Inverter applies a step voltage to each motor phase and transient current are analysed. An insulation degradation is revealed in a parasitic capacitor change, being the capacitor the main factor for the current transient behaviour.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FDI Approach** & **Reference** & **Electric Machine** & **Sensors** & **Power Electronics** \\ \hline MCSA & [63] & \multicolumn{2}{c}{Rotor bars} & & \\ VCT & [64] & \multicolumn{2}{c}{Rotor bars} & & \\ Flux FFT & [65] & \multicolumn{2}{c}{Dirt} & & \\ Current transient FFT & [66] & \multicolumn{2}{c}{Insulation degradation} & & \\ Observer-based & [31; 32; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73] & Voltage and current \\ ToMFIR & [72; 73] & \multicolumn{2}{c}{Speed and current} \\ PCA, ICA & [74; 75] & \multicolumn{2}{c}{Voltage, current and speed} \\ Model-based estimator & [32; 76] & \multicolumn{2}{c}{Power switches} \\ Wavelet entropy & [77] & \multicolumn{2}{c}{Power switches} \\ Wavelet energy and SVM & [78] & \multicolumn{2}{c}{Power switches} \\ Current FFT & [79] & \multicolumn{2}{c}{Power switches} \\ Fault signatures analysis & [80] & \multicolumn{2}{c}{Power switches} \\ Least squares & [81] & \multicolumn{2}{c}{Capacity} \\ \hline \hline \end{tabular}
\end{table}
Table 2: FDI approaches in Railway Traction drives.