Transient Detection of Close Components through the Chirplet Transform: Rotor Faults in Inverter-Fed Induction Motors

Up to now, detection of rotor faults in inverter-fed induction motors has received very little attention. This fault is difficult to be detected, since the fault-related components are too close to the fundamental (the inverter usually operates at low slip). Moreover, classic techniques cannot be applied since steady states are not common. The causes of this type of fault are analyzed in the paper, showing its importance. Particularly, cases of real faults in electric traction are exposed. Then, the paper explores the use of linear time-frequency transforms to detect the time-frequency evolution of the fault-related components. It is shown how the most common linear transforms, such as the Short Time Fourier Transform, do not enable the fault detection. The Chirplet Transform (which has never been used for diagnosing purposes), is proposed to obtain the components evolutions, even if they are too close in the time-frequency plane. The technique is validated through startup tests, in which the presence of the fault is quantified when analyzing the stator current.