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Motor current signal analysis has been an effective way for many years to monitor electrical machines. However, little research work has been reported in using this technique for monitoring their downstream equipment. The research investigating the characteristics of electrical current signals measured from an induction motor for monitoring faults from a downstream gear transmission system by a novel modulation signal (MS) bispectrum method. Both analytic analysis and experimental study are conducted based on a motor drive system and have found that the increase of bispectral peaks can be the basis of mechanical fault diagnosis. Particularly, a fault on a gear will causes a larger increase of the bispectral peak at both the gear shaft frequency and accompany with a smaller increase in the relating shaft frequency. However, a shaft misalignment only leads to a bispectrum peak increase at the shaft bifrequency along.

Motor current signal analysis has been an effective way for many years of monitoring electrical machines themselves. However, little work has been carried out in using this technique for monitoring their downstream equipment because of difficulties in extracting small fault components in the measured current signals. This paper investigates the characteristics of electrical current signals for monitoring the faults from a downstream gearbox using a modulation signal bispectrum (MSB), including phase effects in extracting small modulating components in a noisy measurement. An analytical study is firstly performed to understand amplitude, frequency and phase characteristics of current signals due to faults. It then explores the performance of MSB analysis in detecting weak modulating components in current signals. Experimental study based on a 10kw two stage gearbox, driven by a three phase induction motor, shows that MSB peaks at different rotational frequencies can be based to quantify the severity of gear tooth breakage and the degrees of shaft misalignment. In addition, the type and location of a fault can be recognized based on the frequency at which the change of MSB peak is the highest among different frequencies.