The integrity of electric motors in work and passenger vehicles can best be maintained by frequently monitoring its condition. In this paper, a signal processing-based motor fault diagnosis scheme is presented in detail. The practicability and reliability of the proposed algorithm are tested on rotor asymmetry detection at zero speed, i.e., at startup and idle modes in the case of a vehicle. Regular rotor asymmetry tests are done when the motor is running at a certain speed under load with stationary current signal assumption. It is quite challenging to obtain these regular test conditions for long-enough periods of time during daily vehicle operations. In addition, automobile vibrations cause nonuniform air-gap motor operation, which directly affects the inductances of electric motors and results in a noisy current spectrum. Therefore, it is challenging to apply conventional rotor fault-detection methods while examining the condition of electric motors as part of the hybrid electric vehicle (HEV) powertrain. The proposed method overcomes the aforementioned problems by simply testing the rotor asymmetry at zero speed. This test can be achieved at startup or repeated during idle modes where the speed of the vehicle is zero. The proposed method can be implemented at no cost using the readily available electric motor inverter sensors and microprocessing unit. Induction motor fault signatures are experimentally tested online by employing the drive-embedded master processor (TMS320F2812 DSP) to prove the effectiveness of the proposed method.