"Health Monitoring and Fault Detection of Claw-pole Generators"

Siwei Cheng
School of Electrical and Computer Engineering
Georgia Institute of Technology

Abstract
The "claw-pole" type synchronous generator is the heart of virtually all automotive electric power systems. The failure of such a generator is considered to be a so-called "walk-home" incident leaving a motorist stranded. Timely and accurate detection of generator problems will not only decrease "walk-home" incidences but will also significantly decrease misdiagnoses, thereby resulting in significant warranty cost savings for US auto manufacturers.

This seminar will present methods to detect common incipient faults of claw-pole generator using only sensors that are already available in the automotive electric power system. A commercial claw-pole generator has been reverse-engineered and modeled with finite-element software. Spectral analyses are performed on the measured generator voltages and currents. Several important fault signatures in the voltage and current spectra are identified and discussed.

Incipient faults that are successfully detected include: serpentine belt slip, serpentine belt defect, stator turn-to-turn fault, rotor eccentricity, and worn bearing.

Although the proposed condition monitoring technique is originally designed for claw-pole generators, it may be extended to other synchronous generators with a rectified DC link.

Biography
Siwei Cheng received the BS degree with the highest distinction from Tsinghua University, Beijing, China, in 2007, and the MS degree from Georgia Institute of Technology, Atlanta, in 2009, both in electrical engineering. He is currently working toward the PhD degree in the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta.

Since July 2007, he has been working on condition monitoring and design of electric machines as a graduate Research Assistant in the electric power group at Georgia Tech. His research interests include condition monitoring, design, and control of electric machines, computational intelligence and signal processing techniques applied to energy system, and the electrification of power train in modern vehicles.