In this paper, a suitable method for the on-line detection of the airgap
mixed eccentricity fault in a three-phase cage induction motor has been
proposed. The method is based on a Motor Current Signature Analysis (MCSA)
approach, a technique that is often used for an induction motor condition
monitoring and fault diagnosis. It is based on the spectral analysis of the
stator line current signal and the frequency identification of specific
components, which are created as a result of motor faults. The most commonly
used method for the current signal spectral analysis is based on the Fast
Fourier transform (FFT). However, due to the complexity and memory demands,
the FFT algorithm is not always suitable for real-time systems. Instead of
the whole spectrum analysis, this paper suggests only the spectral analysis
on the expected airgap fault frequencies employing the Goertzel?s algorithm
to predict the magnitude of these frequency components. The method is simple
and can be implemented in real-time airgap mixed eccentricity monitoring
systems without much computational effort. A low-cost data acquisition
system, supported by the LabView software, has been used for the hardware and
software implementation of the proposed method. The method has been validated
by the laboratory experiments on both the line-connected and the inverter-fed
three-phase fourpole cage induction motor operated at the rated frequency and
under constant load at a few different values. In addition, the results of
the proposed method have been verified through the motor?s vibration signal
analysis.
Modeling of three-phase electric motor operation by the MATLAB system with deteriorated power quality in the 0.38 kV distribution networks