Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform

In the intensity hue saturation (IHS) based image fusion, the enhancement of the spatial information often leads to the distortion of the information in the spectral domain. In this paper, a spectral preserve fusion method is developed by introducing a trous wavelet transform (AWT) and empirical mode decomposition (EMD) into one generalized IHS transform, which extends panchromatic sharpening of low-resolution multispectral images (LRMIs) through detail injection to the fusion of LRMIs and synthetic aperture radar imagery (SARI). The original LRMIs are first transformed into one low-resolution intensity component (LRIC) and the details from the SARI are abstracted using AWT. The LRIC and the approximation image of the SARI are fused through an intrinsic mode functions based model. Subsequently, high RIC produced through an inverse AWT is then substituted for the old one. High RMIs are obtained through an inverse IHS transform. Experiments reveal that this approach performs better than the AWT and IHS-AWT-based methods in preserving the spectral information of the LRMIs. The performance of the image fusion is examined both visually and quantitatively.

SAR and Multispectral Image Fusion Using Generalized IHS Transform Based on à Trous Wavelet and EMD Decompositions

Fusing remote sensing images using à trous wavelet transform and empirical mode decomposition

This paper presents a new approach to detect the location of multiple broken rotor bars (MBRBs) in induction motor (IM) drive, running under no load and full load conditions using direct in and variable frequency drives. This technique is based on earlier work of location detection of one broken rotor bar. The techniques are tested for various fault severity levels so the detection of the exact location of the fault at early stage helps to reach sufficient time maintenance. In this paper, the authors used Hilbert Transform to extract the fault signature from the stator current envelope which is the low frequency component. Then statistical analysis is applied which produce a formula that is used to get the exact location of the fault in IM rotor.

Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor

Alcohol is a severe intoxication substance that changes the functionality of brain by disturbing the neuronal process of the central nervous system, which causes mental and behavioural disorders. These disorders can be diagnosed by automatic classification of normal and alcohol electroencephalogram (EEG) signals, because it provides neurophysiology of alcoholic human brain. In this study, EEG rhythms based features are proposed for automatic identification and analysis of alcohol EEG signals. The instantaneous-frequency computed by analytic representation of intrinsic mode functions (IMFs) is used for separation of different frequency ranges known as EEG rhythms. IMFs are obtained by applying empirical mode decomposition on an EEG signal. The variability and complexity of separated EEG rhythms are measured by features namely: mean absolute deviation, inter quartile range, coefficient of variation, entropy, and neg-entropy. The p-value analysis of these features shows that low-frequency (LF)-rhythms based features are statistically significant for identification and neurological analysis of alcohol EEG signals. The LF-rhythms based features are applied as input to an extreme learning machine (ELM) and least squares support vector machine classifiers for classification of normal and alcohol EEG signals. The proposed method with an ELM classifier provides better classification accuracy (97.92%) as compared with other existing methods.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2017.0232

Rhythm-based identification of alcohol EEG signals

Instantaneous frequency (IF) estimate has important physical meaning for non-stationary signal. Traditional IF estimate methods were investigated, their features were compared and each technique had certain weaknesses. On the basis of these, the newly-emerged Hilbert-Huang transform (HHT) method was introduced. The theories and features of HHT were discussed. Its application in IF estimate was presented. It gives accurately the IF estimate for non-stationary signal. The better performance is illustrated by the simulated signal. The results indicate that this method provides an efficient tool for the IF estimation of non-stationary signal.

Yuquan Chen

Papers

Instantaneous frequency estimate for non-stationary signal