Identification of significant intrinsic mode functions for the diagnosis of induction motor fault

Abstract

For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work.

A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection

“…In [15], the authors compared the spectra of sifted IMFs with the spectrogram of measured vibration response resulting from a swept-sine excitation and the IMF with a concurrent resonance frequency band is selected as relevant. Following such purpose, Cho et al defined an index labeled Power-Harmonic Ratio (PHR) [16]. The higher value of PHR identifies the IMFs with higher average power containing fault related frequency peaks.…”

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

“…These indicators can be used individually but are generally used in combination each other. As a matter of fact, the merit index [15] is a linear combination of the periodicity degree and absolute skewness value and the PHR [16] standing for the power-harmonic ratio employs the energy density of harmonics of both desired frequency peak and the target signal. The confidence index [18] is defined as an arithmetic between the correlation coefficient and specific indexes such as skewness, kurtosis, and impact allowance that applies the periodicity and maximum values.…”

“…Junsheng et al exploited singular values of IMFs as fault feature vectors of support vector machines [14] and Ricci et al presented an automatic IMF selection method using a merit index [15]. Cho et al proposed an IMF selection algorithm based on power-harmonic ratio (PHR) [16] and Lei et al suggested a diagnosis method of rolling element bearings based on CEEMDAN [17]. Yi et al presented an adaptive procedure based on EEMD and Hilbert marginal spectrum for multi-fault diagnostics of axle bearings and introduced the IMFs' confidence index for automatic IMF selection [18].…”

Fast Fault Detection Using Optimal Intrinsic Modes and Energy Deviation Matrix in Distribution Systems

“…In signal processing, the selection of IMFs has been used carefully. Cho et al [17] introduced a power-harmonic ratio (PHR) as an index for selecting the critical IMFs of a given signal. In addition, faulty signals may have more power/energy and harmonic content.…”