An Enhanced Empirical Mode Decomposition Technique for Rotor Fault Detection in Induction Motors

Induction motors or machines (IMs) are the driving force in various industries such as manufacturing, transportation, and wind power generation. Hence it is essential to detect faults in IMs reliably so as to enhance the production quality and avoid operational degradation. However, it is still challenging to detect faults in IMs reliably as fault feature properties could change under variable IM operating conditions. The objective of this article is to propose an enhanced empirical mode decomposition (EEMD) technique to detect the IM broken rotor bar (BRB) fault based on motor current signature analysis (MCSA). In the proposed EEMD technique, first, a phase-insensitive similarity function is suggested to determine the representative intrinsic mode function (IMF). Second, an optimized adaptive multiband filter (OAMF) is proposed to recognize the fault characteristic features from the spectrum. Third, a modified whale optimization (MWO) algorithm is suggested to optimize the parameters in the adaptive multiband filter. A reference function is also proposed to enhance feature properties and IM fault detection. The effectiveness of the proposed EEMD technique is verified experimentally under different IM conditions.