# Mechanical Faults Analysis in Switched Reluctance Motor
## 1 Introduction
### Literature References  
Electric motor diagnostics and fault monitoring have existed since they were invented and are in operation. The defects they may undergo can be divided into electrical andmechanical ones. Typical motor mechanical faults might be bearing failure, rotor eccentricity (air-gap irregularity), broken rotor bars, cracked end rings, bent shaft, bolt loosening, gearbox failure, etc. [2]. Nevertheless, more efficient diagnostic tools have been developed in recent years, such as finite element methods, techniques based on the frequency domain, model-based techniques, and the application of pattern recognition [2].  
Apart from classic spectrum analysis for stationary signals, instantaneous frequency analysis and the Wigner distributions as time-frequency analysis methods can be applied in variable-speed drives, e.g., with faults causing load torque oscillation [3].  
Excluding some specific cases for a particular type of electric motor, the SRM is susceptible to the same types of faults each machine can undergo.  
The SRM can experience a wide range of electrical fault types, such as open- and short-circuit faults in power switches, open-phase winding, and zero-output current sensors faults [4], as in power transistors of the full-bridge power converter (FBPC) [5]. There are also methods of diagnosing lesser-known faults, such as the high-resistance connection (HRC) fault, using a multisensor calibrated transformer with shifted windows (MSwin-T) [6].  
As for the mechanical faults modeling, the dynamic eccentricity in the SRM can be simulated using the finite element method coupled with dynamic equations developed by MATLAB/Simulink(tm) software and then applied, e.g., for torque analysis using the FEA-TFR approach [7].  
The experimental method of SRM eccentricity diagnostics can consist of monitoring the offline and standstill modes by utilizing the fault signature as a differential-induced voltage, achieved by injecting diagnostic pulses into the motor windings [8]. The winding method can also measure and reduce the effects of rotor eccentricity on the SRM. The currents in the windings can be adjusted in parallel paths, reducing unbalanced magnetic forces [9]. The eccentricity fault can also be detected by the measurement of mutual fluxes and mutually induced voltages, as it is observed that mutual fluxes increase with an increase in the eccentricity percent [10]. The degree and location of the dynamic eccentricity fault can also be determined using produced current signature calculation algorithms and detection patterns named ELDP [11].  
Another widely used signal for eccentricity testing is the machine casing's acceleration (vibration) signal. Monitoring it can diagnose inter-turn faults and rotor eccentricity in SRMs. The forces can be detected on the machine by accelerometers [12].  
SRMs are also used as traction drives in electric vehicles. There is a fault diagnosis of the rotor position sensor based on a sliding mode observer (SMO) and an average torque estimator, which can be applied to light electric vehicles (LEV) [13]. Motors installed in-wheel can undergo a coordination control method that is proposed to achieve vehicles' vibration suppression and braking energy recovery, considering motor eccentricity [14]. This fault also causes an unbalanced magnetic pull of the wheel motor on vehicle dynamics, which can be addressed with Maxwell's stress method and the principle of virtual displacement [15]. The air-gap eccentricity also affects the handling stability of electric vehicles (EVs) [16]. SRMs are also being tested as traction motors in EVs for bearing failures in static and dynamic states using the Hilbert transform and the Bayesian optimized neural network (BoNN) [17].  
There are also comprehensive methods for testing the SRM at the motor and generator phases of operation, using diagnostic tools such as the k-out-of-n: G model and the reliability block diagram model [18].