An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors

• Main Authors: Erinc Karatoprak, Serhat Seker
• Format: Article
• Language: English
• Published: Hindawi Limited 2019-01-01
• Series: Mathematical Problems in Engineering
• Online Access: http://dx.doi.org/10.1155/2019/8015295
• ISSN: 1024-123X; 1563-5147

This paper proposes an improved Empirical Mode Decomposition (EMD) method by using variable window size median filters during the Intrinsic Mode Functions (IMFs) generation. Compared to the traditional EMD, the improved EMD, namely, Median EMD (MEMD), helps to reduce mode-mixing providing an improvement in terms of separating the fundamental frequencies per IMF. The MEMD method applies the EMD to the signal and then applies a variable window size median filter to the resulting IMFs. A narrow window is used for high frequency components where a broader window is used for the lower frequency components. The filtered IMFs are then summed again and another round of EMD is applied to yield the improved MEMD IMFs. A test setup for accelerated aging of bearings in induction motors is used for the comparison of the traditional and the improved EMD methods with the goal of finding potential bearing defects in an induction motor. The potential defect at the early stage is compared with the faulty state and is used to extract the characteristics of the bearing damage that develops gradually. Comparing the EMD and MEMD, it is seen that MEMD is an improvement to EMD in terms of mode-mixing problem. The MEMD method demonstrated to have better performance compared to the traditional EMD for the extraction of the fault features from the healthy operational state of the motor.