Bearing Faults Diagnosis Based on Teager Energy Operator Demodulation Technique

2009 ◽  
Author(s):  
Hui Li ◽  
Lihui Fu ◽  
Yuping Zhang
Keyword(s):  
Energy Operator ◽  
Bearing Faults ◽  
Teager Energy Operator ◽  
Faults Diagnosis ◽  
Teager Energy

Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Zhipeng Feng ◽  
Ming J. Zuo ◽  
Rujiang Hao ◽  
Fulei Chu ◽  
Jay Lee
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
Characteristic Frequency ◽  
Energy Operator ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Teager Energy

Periodic impulses in vibration signals and its repeating frequency are the key indicators for diagnosing the local damage of rolling element bearings. A new method based on ensemble empirical mode decomposition (EEMD) and the Teager energy operator is proposed to extract the characteristic frequency of bearing fault. The signal is firstly decomposed into monocomponents by means of EEMD to satisfy the monocomponent requirement by the Teager energy operator. Then, the intrinsic mode function (IMF) of interest is selected according to its correlation with the original signal and its kurtosis. Next, the Teager energy operator is applied to the selected IMF to detect fault-induced impulses. Finally, Fourier transform is applied to the obtained Teager energy series to identify the repeating frequency of fault-induced periodic impulses and thereby to diagnose bearing faults. The principle of the method is illustrated by the analyses of simulated bearing vibration signals. Its effectiveness in extracting the characteristic frequency of bearing faults, and especially its performance in identifying the symptoms of weak and compound faults, are validated by the experimental signal analyses of both seeded fault experiments and a run-to-failure test. Comparison studies show its better performance than, or complements to, the traditional spectral analysis and the squared envelope spectral analysis methods.

scholarly journals Rolling Bearing Fault Detection Based on the Teager Energy Operator and Elman Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hongmei Liu ◽  
Jing Wang ◽  
Chen Lu
Keyword(s):  
Neural Network ◽  
Energy Operator ◽  
Elman Neural Network ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Teager Energy Operator ◽  
Teager Energy ◽  
Impact Characteristics ◽  
Frequency Band Energy ◽  
The Impact

This paper presents an approach to bearing fault diagnosis based on the Teager energy operator (TEO) and Elman neural network. The TEO can estimate the total mechanical energy required to generate signals, thereby resulting in good time resolution and self-adaptability to transient signals. These attributes reflect the advantage of detecting signal impact characteristics. To detect the impact characteristics of the vibration signals of bearing faults, we used the TEO to extract the cyclical impact caused by bearing failure and applied the wavelet packet to reduce the noise of the Teager energy signal. This approach also enabled the extraction of bearing fault feature frequencies, which were identified using the fast Fourier transform of Teager energy. The feature frequencies of the inner and outer faults, as well as the ratio of resonance frequency band energy to total energy in the Teager spectrum, were extracted as feature vectors. In order to avoid a frequency leak error, the weighted Teager spectrum around the fault frequency was extracted as feature vector. These vectors were then used to train the Elman neural network and improve the robustness of the diagnostic algorithm. Experimental results indicate that the proposed approach effectively detects bearing faults under variable conditions.

scholarly journals Operador de energía de teager para la detección de hipernasalidad en niños con labio y paladar hendido

TecnoLógicas ◽  
2011 ◽  
pp. 27 ◽  
Author(s):  
Juan R. Orozco-Arroyave ◽  
Jonny A. Uribe ◽  
Jesús F. Vargas-Bonilla
Keyword(s):  
Energy Operator ◽  
Teager Energy Operator ◽  
Teager Energy

El labio y/o paladar hendido (LPH) es una malformación, que tiene orígenes de tipo genético y ambiental. En Colombia, 6 de cada 10000 niños nacen con esta malformación, mientras en el resto del mundo la proporción se encuentra en 1 de cada 10000. El LPH trae consigo patologías en el habla tales como: hipernasalidad, hiponasalidad, golpe glótico, entre otras. De todas estas patologías, la hipernasalidad es la más recurrente en pacientes con LPH, apareciendo aproximadamente en el 90% de los casos. En este trabajo se hace un análisis, basado en resultados experimentales, del desempeño del Operador de Energía de Teager (TEO, por las siglas en inglés de Teager Energy Operator), para la detección de hipernasalidad en pacientes con LPH. Se analiza una versión generalizada del TEO con el fin de validar su capacidad discriminante en la detección de hipernasalidad, aplicándolo sobre una base de datos con registros de voz reales, de niños con LPH y niños control. Los resultados obtenidos comprueban que el TEO posee gran capacidad discriminante, y puede aportar información relevante en el proceso de detección de hipernasalidad.

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