This paper proposes a load monitoring algorithm that exploits the observed time frequency characteristics of a laboratory pulsed load current and uses it to detect events and characterize them into desirable transitions or faults. An electromagnetic gun is assembled at a low voltage lab setup to provide multiple iterations of pulsed load events with a few instances of faults. Detailed analysis of the load profile is followed by a simulation using measured data to demonstrate the effectiveness of the Short Time Fourier Transform based algorithm to identify key events in the current profile and detect faults.