A method for sensorless on-line vibration monitoring of induction machines

Riley, C.M.; Lin, B.K.; Habetler, T.G.; Schoen, R.R.

doi:10.1109/28.738984

Cited by 56 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As mentioned in the literature, a defect in rolling element-bearing causes an increase in the overall RMS value of stator current for a known frequency range. Hence, the results obtained from the stator current correlates with the results reported in [4,15]. Thus, overall stator current has appreciably increased by 39.79% in case of maximum defect size of 1500 mm with respect to healthy bearing at 15 kg load.…”

Section: Vibration Velocitysupporting

confidence: 90%

See 1 more Smart Citation

A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings

Tandon

Yadava

Ramakrishna

2007

Mechanical Systems and Signal Processing

153

View full text Add to dashboard Cite

Section: Vibration Velocitysupporting

confidence: 90%

“…Since ball bearings support the rotor, any bearing defect will produce a radial motion between the rotor and stator of the machine. Riley et al [4] presented a method for sensorless on-line vibration monitoring of induction machines. This method assumed a linear relationship between the current harmonics and vibration level.…”

Section: Introductionmentioning

confidence: 99%

A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings

Tandon

Yadava

Ramakrishna

2007

Mechanical Systems and Signal Processing

153

View full text Add to dashboard Cite

“…( ) Si se asocia el valor de δ con una variación senoidal (Riley et al, 1997) y se lo reemplaza en la matriz de flujo, los autovalores quedan expresados por la ecuación (12), siendo δ = sen (z) -variación periódica del entrehierro producido por vibración externa. Si se analizan las condiciones de funcionamiento en falla de acuerdo a lo expresado en Henao et al (2003), el flujo de dispersión contiene las frecuencias de falla del rotor (ec.…”

Section: Análisis Y Contribución Teóricaunclassified

Turbinas Eólicas: Optimización en el Pre-Diagnóstico de Fallas en el Generador

et al. 2012

View full text Add to dashboard Cite

ResumenSe propone un método no invasivo y de bajo costo para el pre-diagnóstico de fallas en a máquina eléctrica de grandes aerogeneradores. Esto con el objeto de minimizar el riesgo de falla y la remoción de la máquina con uso de equipo pesado (grúas) para reparaciones. Dado que es usual recurrir a más de un método para detectar fallas incipientes, la predicción requiere corroboración para mayor seguridad. En la industria, esta práctica es normal (medir vibraciones y espectro de corriente, por ejemplo), pero optimizar el mantenimiento predictivo de una máquina eléctrica fijada en una turbina eólica requiere de la menor cantidad posible de dispositivos y de la factibilidad de utilizar monitoreo a distancia con métodos no invasivos. El método propuesto reúne estas condiciones y los ensayos preliminares con sensores de bajo costo muestran buenos resultados para su aplicación experimental. Palabras clave: aerogenerador, diagnóstico de fallas, mantenimiento, método no invasivo, máquina eléctrica. Wind Turbine: Optimization of Failures Pre-Diagnosis in the Power Generator AbstractA non-invasive low cost method for pre-diagnosis faults in electrical machines of large wind turbines is proposed. The objective is to minimize fault risks and the removal of the machine with heavy equipment (such as cranes) for maintenance. Since it is normal to use more than one method to detect incipient faults, prediction requires corroboration for higher security (such as vibration measurement and current spectrum analyses). This practice is usual in industries, but optimum predictive maintenance of electric machines in a wind turbine requires the use of the fewest possible devices and a distance non-invasive monitoring method. The proposed method has all this characteristics and preliminary essays with low cost sensors show good results for experimental applications.

show abstract

“…Because power supply imbalance can also cause the appearance of negative-sequence currents, such methods are sensitive to the impact of imbalanced machine operation. In addition to motor current analysis, vibration-based condition monitoring has attracted the attention of many researchers working in the area of induction machines and has gained industrial acceptance, as vibration analysis techniques are more effective in assessing a machine's health [7]. It is asserted that vibration monitoring is the most reliable method of assessing the overall health of a rotor system [8].…”

Section: Introductionmentioning

confidence: 99%

Vibration signal analysis for electrical fault detection of induction machine using neural networks

Su¹,

Chong

Kumar

2011

Neural Comput & Applic

View full text Add to dashboard Cite

Fault detection is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Many of these faulty situations in three-phase induction motors originate from an electrical source. Vibration signal analysis is found to be sensitive to electrical faults. However, conventional methods require detailed information on motor design characteristics and cannot be applied effectively to vibration diagnosis because of their nonadaptability and the random nature of the vibration signals. This paper presents the development of an online electrical fault detection system that uses neural network modeling of induction motor in vibration spectra. The short-time Fourier transform is used to process the quasi-steady vibration signals for continuous spectra so that the neural network model can be trained. The electrical faults are detected from changes in the expectation of modeling errors. Experimental observations show that a robust and automatic electrical fault detection system is produced whose effectiveness is demonstrated while minimizing the triggering of false alarms due to power supply imbalance.

show abstract

A method for sensorless on-line vibration monitoring of induction machines

Cited by 56 publications

References 6 publications

A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings

A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings

Turbinas Eólicas: Optimización en el Pre-Diagnóstico de Fallas en el Generador

Vibration signal analysis for electrical fault detection of induction machine using neural networks

Contact Info

Product

Resources

About