Modified Empirical Mode Decomposition and Teager–Kaiser Energy Operator-Based Phasor Estimation in Presence of DC Offset for Digital Relaying Application

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

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Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology ◽

10.1109/iembs.2010.5626501 ◽

2010 ◽

Cited By ~ 12

Author(s):

M F Kaleem ◽

L Sugavaneswaran ◽

A Guergachi ◽

S Krishnan

Keyword(s):

Empirical Mode Decomposition ◽

Energy Operator ◽

Mental Task ◽

Eeg Signals ◽

Teager Energy Operator ◽

Mode Decomposition ◽

Task Classification ◽

Teager Energy

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Hybrid data fusion approach for fault diagnosis of fixed-axis gearbox

Structural Health Monitoring ◽

10.1177/1475921717727700 ◽

2017 ◽

Vol 17 (4) ◽

pp. 936-945 ◽

Cited By ~ 7

Author(s):

Vanraj ◽

SS Dhami ◽

BS Pabla

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Nearest Neighbor ◽

Energy Operator ◽

Operating Conditions ◽

Maintenance Cost ◽

K Nearest Neighbor ◽

Forward Selection ◽

Statistical Measures ◽

Mode Decomposition

Intelligent fault diagnosis system based on condition monitoring is expected to assist in the prevention of machine failures and enhance the reliability with lower maintenance cost. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence leads to failure of the whole mechanism. With advancement in technology, various gear fault diagnosis techniques have been reported which primarily focus on vibration analysis with statistical measures. However, acoustic signals posses a huge potential to monitor the status of the machine but a few studies have been carried out till now. This article describes the implementation of Teager–Kaiser energy operator and empirical mode decomposition methods for fault diagnosis of the gears using acoustic and vibration signals by extracting statistical features. A cross-correlation-based fault index that assists the automatic selection of the sensitive Intrinsic Mode Function (IMF) containing fault information has also been described. The features extracted by all combinations of signal processing techniques are sorted by order of relevance using floating forward selection method. The effectiveness is demonstrated using the results obtained from the experiments. The fault diagnosis is performed with k-nearest neighbor classifier. The results show that the hybrid of empirical mode decomposition–Teager–Kaiser energy operator techniques employs the advantages traits of one or the other technique to generate overall improvement in diagnosing severity of local faults.

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