On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter

Abstract

Quadratic Time-Frequency Distributions (TFDs) become a standard tool in many fields producing nonstationary signatures. However, these representations suffer from two drawbacks: First, bad time-frequency localization of the signal's autoterms due to the unavoidable crossterms generated by the bilinear form of these distributions. This results on bad estimation of the Instantaneous Frequency (IF) laws and decreases, in our case, the ability to precisely decide the existence of a motor fault. Secondly, the TFD's parameterization is not always straightforward. This paper deals with faults' detection in two-level inverter feeding induction motors, in particular open-circuit Insulated Gate Bipolar Transistor (IGBT) faults. For this purpose, we propose the use of a recent high-resolution TFD, referred as PCBD for Polynomial Cheriet-Belouchrani Distribution. The latter is adjusted using only a single integer that is automatically optimized using the Stankovic concentration measure, otherwise, no external windows are needed to perform the highest time-frequency resolution. The performance of the PCBD is compared to the best-known quadratic representations using a test bench. Experimental results show that the frequency components characterizing open-circuit faults are best detected using the PCBD thanks to its ability to suppress interferences while maintaining the signal's proper terms.