Fault Detection in dynamic conditions by means of Discrete Wavelet Decomposition for PMSM running under Bearing Damage

Rosero, J.; Romeral, L.; Rosero, Esteban; Urresty, J.

doi:10.1109/apec.2009.4802777

Cited by 52 publications

(25 citation statements)

References 22 publications

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“…While the results show the variation of non-stationary frequency content, the spatial information is not presented in its native domain, as a function of position. The same limitation applies to other time-frequency transformation methods, such as the discrete wavelet transform [8] or the Wigner-Ville distribution [9]. Without information in the spatial domain, spatial synchronous averaging is not possible.…”

Section: B Limitations Of Time-frequency Representationsmentioning

confidence: 94%

Using the motor drive as a sensor to extract spatially dependent information during servo operation

Wolf

Lorenz

2010

2010 IEEE Energy Conversion Congress and Exposition

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This paper presents a methodology for extracting useful spatial signals using the motor drive as the sensor during servo operation. Spatially dependent phenomena yield nonstationary frequency content during servo operation. With variable frequency and frequency dependent amplitude in the current and torque signals, the underlying, valuable spatial information is difficult to extract using conventional frequency domain approaches. The methodology presented uses the motor drive to estimate the spatially dependent disturbance torque information during servo operation using spatial domain resampling of temporal domain signals internal to the motor drive. Spatial domain information is speed invariant, making it accessible even during servo operation. Additionally, this paper develops a framework to operate through varying speeds to help isolate the systematic, spatially dependent content using spatial synchronous averaging. These methods and concepts are evaluated through simulation as well as experimentation. This methodology can be utilized to provide robust control of machines through identification of spatial components, as well as improve diagnostics of machine and load degradation.

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Section: B Limitations Of Time-frequency Representationsmentioning

confidence: 94%

Using the motor drive as a sensor to extract spatially dependent information during servo operation

Wolf

Lorenz

2010

2010 IEEE Energy Conversion Congress and Exposition

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“…A fault in a bearing or gears will translate into an electric quantity. In [10], Rosero et al considered an immediate translation of the bearing fault into stator current through rotating eccentricities, and used finite elements to simulate the resulting current and discrete wavelets to detect them. In actuality, this impact on a ball or race will also produce additional vibrations which will translate into torque pulsations.…”

Section: Types Development and Characteristics Of Mechanical Famentioning

confidence: 99%

Response of electrical drives to gear and bearing faults — Diagnosis under transient and steady state conditions

Strangas

2013

2013 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

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Bearing and gear faults in systems using electrical drives share many commonalities and have some differences in they way that they can be detected. They both cause a mechanical impulse at a frequency that is related to the speed of the rotor. For neither is it possible to develop a state-space model relating the fault and its location and severity to the measured outputs. The measurements therefore require extensive processing to extract features that are related to a fault and to categorize it. Measurements physically close to the fault location (e.g. vibrations) are generally more useful to accurately determine this fault than measurements away from it (e.g. currents and voltage at the drive). Models of faults include fatigue (e.g. Paris model) and degradation due to bearing currents.In the recent past the techniques to identify these faults have been refined, tested extensively, and compared. They typically include signal conditioning, feature extraction (in the time and time-frequency domain) and categorization, which includes fault identification. Failure prognosis and use of multiple sensors are possible future directions of research to produce reliable estimated of condition and facilitate health management.

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“…Such methods have been applied to both current and vibration signals [16], with a number of variations. Wigner Distribution and Cohen's class of time-frequency distributions present alternatives in time frequency analysis.…”

Section: ) Time-frequency Domain Analysismentioning

confidence: 99%

Failure prognosis methods in electrical drives - State of the art and future directions

Strangas

Aviyente

2015

2015 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

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As prognosis of failures of critical electric drives is attracting interest, new tools are emerging. Starting from the well established tools for the fault diagnosis, and expanding to the development of trends, and from them determination of the Remaining Useful Life of a drive leads to timely maintenance of fault mitigation, decreased costs and system reliability. The tools used today for failure prognosis ar briefly outlined, and the present needs and challenges, and future research and applications are presented.978-1-4799-8900-3/15/$31.00 ©2015 IEEE

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Fault Detection in dynamic conditions by means of Discrete Wavelet Decomposition for PMSM running under Bearing Damage

Abstract: Abstract-This

Cited by 52 publications

References 22 publications

Using the motor drive as a sensor to extract spatially dependent information during servo operation

Using the motor drive as a sensor to extract spatially dependent information during servo operation

Response of electrical drives to gear and bearing faults — Diagnosis under transient and steady state conditions

Failure prognosis methods in electrical drives - State of the art and future directions

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Fault Detection in dynamic conditions by means of Discrete Wavelet Decomposition for PMSM running under Bearing Damage

Abstract: Abstract-This

Cited by 52 publications

References 22 publications

Using the motor drive as a sensor to extract spatially dependent information during servo operation

Using the motor drive as a sensor to extract spatially dependent information during servo operation

Response of electrical drives to gear and bearing faults &#x2014; Diagnosis under transient and steady state conditions

Failure prognosis methods in electrical drives - State of the art and future directions

Contact Info

Product

Resources

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Response of electrical drives to gear and bearing faults — Diagnosis under transient and steady state conditions