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The integrity of electric motors in work and passenger vehicles can best be maintained by frequently monitoring their condition. In this paper, a signal processing-based motor fault-diagnosis scheme in detail is presented. 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 that 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 sensor and microprocessing unit. Induction motor fault signatures are experimentally tested online by employing the drive-embedded master processor [TMS320F2812 digital signal processor (DSP)] to prove the effectiveness of the proposed method. Index Terms--Digital signal processor (DSP)-based fault detection, hybrid electric vehicle (HEV), induction motor, motor fault diagnosis.

Load variation along with static and dynamic eccentricities degrees is one of the major factors directly affecting the dynamic behaviors of eccentricity signatures as observed in the current spectrum of induction motors. Without taking the effect of load variation into consideration precisely, the change in the static and dynamic related fault signature amplitudes provides misleading information where the eccentricity degree and the load level exhibit similar effects in the current spectrum. In this paper, we address all these factors in a unified framework by analyzing various combinations both theoretically and experimentally. For this purpose, the time-stepping finite element method (TSFEM)-based, load-level-independent method is proposed to determine the static and dynamic eccentricities degrees individually. Index Terms--Fault diagnosis, induction motor, load variation, static and dynamic eccentricities, time-stepping finite element (TSFE).

In this paper, a digital signal processor-based phase-sensitive motor fault signature detection technique is presented. The implemented method has a powerful line current noise suppression capability while detecting the fault signatures. Because the line current of inverter-driven motors involve low-order harmonics, high-frequency switching disturbances, and the noise generated by harsh industrial environment, the real-time fault analyses yield erroneous or fluctuating fault signatures. This situation becomes a significant problem when the signal-to-noise ratio of the fault signature is quite low. It is theoretically and experimentally shown that the proposed method can determine the normalized magnitude and phase information of the fault signatures even in the presence of noise, where the noise amplitude is several times higher than the signal itself. Since it has low computational burden, the developed algorithm is embedded to the motor control program without degrading drive performance. Therefore, it is implemented without any additional cost using readily available drive processor and current sensors. Index Terms--Bearing fault, broken bar fault, condition monitoring, current signature analysis, digital signal processor (DSP)-based fault diagnosis, eccentricity, fault diagnosis, lock-in detector, online monitoring, phase-sensitive detection.

In this paper, the effects of inverter harmonics on motor current fault signatures are studied in detail. It is theoretically and experimentally shown that the fault signatures caused by the inverter harmonics are similar and comparable to those generated by the fundamental harmonic on the line current. Theoretically-derived extended relations including bearing fault, eccentricity, and broken rotor bar relations are found to match experimental results. Furthermore, it is observed and reported that the asymmetries on the rotor caused by broken rotor bars increase the amplitude of even harmonics. To confirm these claims, bearing, eccentricity, and broken rotor bar faults are tested and the line current spectrum of each faulty motor is compared with the healthy one. The proposed additional fault data are expected to contribute positively to the inverter-fed motor fault decision-making algorithms. Index Terms--Bearing faults, condition monitoring, current signature analysis, fault diagnosis, inverter harmonics, spectral analysis.

In a three-phase squirrel-cage induction motor, eccentricity is a common fault that can make it necessary to remove the motor from the production line. However, because the motor may be inaccessible, diagnosing the fault is not easy. We have developed a time-stepping finite-element method (FEM) that identifies mixed eccentricity (a combination of static and dynamic) by analysis, without direct access to the motor. The method overcomes the difficulty of applying FEMs to transient behavior. It simulates the spectrum of the line current of a production-line motor and compares it to the spectrum of a known healthy motor to detect eccentricity. Agreement between the simulation and actual measurements of eccentricity is good. Index Terms--Finite-element method, induction motor, mixed eccentricity, time stepping.