High Frequency Resolution Techniques for Rotor Faults Detection of Induction Machines

Abstract—Electrical machines are a critical components of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns. Motor current signature analysis (MCSA) is the reference method for diagnosis of medium-large machines in industrial applications. However MCSA is still an open research topic, as some signatures may be created by different phenomena, and it is not robust with respect to load and inertia variations, and with respect to an oscillating load torque. Recently the topic of diagnostic techniques for drives and low-medium size machines is becoming attractive, as the procedure can be embedded in the drive at no additional cost but the dedicated firmware, provided that a suitable computational cost is required. Several research activities have been dedicated to retrieve algorithms to solve the above issues, while no definite solution has been retrieved yet. In this paper, statistical time domain techniques are used to track grid frequency and machine slip that feature either a lower computational cost or a higher accuracy than traditional discrete Fourier transform techniques. Then, the knowledge of both grid frequency and machine slip are used to tune the parameters of the zoom-FFT algorithm that allows high frequency resolution keeping constant the computational cost or low computational cost keeping constant the frequency resolution. The above results are useful for the implementation of a realtime diagnostic system on a low cost processor. In fact the latency required to realize a traditional FFT algorithm may prevent the effectiveness of a early warning system that includes mandatory overheads to trigger the alarms. These advantages are enhanced in case of transient for which a reduced number of samples is available because of the time varying nature of the signal.