Condition Monitoring of Rotating Electrical Machines

Tavner, Peter; Li, Ran; Penman, J.; Sedding, H.G.

doi:10.1049/pbpo056e

Cited by 276 publications

(168 citation statements)

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“…From [5][6][7], we know that abnormal temperature changes or rises are an effective indication of incipient gearbox failure. In [17], authors gives a proof that the gearbox temperature rise will be proportional to the power output if the gearbox works normally, that is, the gearbox transmission efficiency has not changes.…”

Section: Gearbox Failure Analysismentioning

confidence: 99%

“…Unfortunately, in the SCADA data we have, there is no gearbox failure record. Based on [5][6][7]17], it is representive to simulate a real incipient gearbox failure by adding extra temperature drift on the initial SCADA data. And these manual drift data is used to test the effectiveness of this AAKR CM method in the flowing two cases.…”

Section: Gearbox Failure Analysismentioning

confidence: 99%

“…Temperatures are important and easily measured indicators of the health of many wind turbine component such as the gearbox and are often recorded automatically by the SCADA system. An unexpected increase in component temperature may indicate an overload, poor lubrication or possibly ineffective passive or active cooling [5]. Previous work used a Back Propagation (BP) neural network to construct the normal behavior temperature models of gearbox based on SCADA data [6].…”

Section: Introductionmentioning

confidence: 99%

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Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Guo

Bai

2011

Energies

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Condition Monitoring (CM) of wind turbines can greatly reduce the maintenance costs for wind farms, especially for offshore wind farms. A new condition monitoring method for a wind turbine gearbox using temperature trend analysis is proposed. Autoassociative Kernel Regression (AAKR) is used to construct the normal behavior model of the gearbox temperature. With a proper construction of the memory matrix, the AAKR model can cover the normal working space for the gearbox. When the gearbox has an incipient failure, the residuals between AAKR model estimates and the measurement temperature will become significant. A moving window statistical method is used to detect the changes of the residual mean value and standard deviation in a timely manner. When one of these parameters exceeds predefined thresholds, an incipient failure is flagged. In order to simulate the gearbox fault, manual temperature drift is added to the initial Supervisory Control and Data Acquisitions (SCADA) data. Analysis of simulated gearbox failures shows that the new condition monitoring method is effective.

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Section: Gearbox Failure Analysismentioning

confidence: 99%

Section: Gearbox Failure Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Guo

Bai

2011

Energies

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“…This is normally achieved through continuous monitoring of the vital machine operational parameters, including electrical, mechanical and thermal signals, to extract indices containing information on the device operating status [1][2][3][4][5]. Thermal monitoring of motor windings is of particular interest in this respect [5,6], as severe winding insulation breakdown and the consequent significant thermal stress have been reported to contribute to as much as ≈30-40% of all failures in conventional induction machinery [7]. Achieving accurate monitoring of the winding hot spot temperature is therefore key in enabling effective machine thermal protection and lifetime extension by prevention of undetected and prolonged thermal overloads.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of fiber-optic sensing performance for embedded thermal monitoring of electric machinery wound components

Anees

Djurović

2016

2016 5th Mediterranean Conference on Embedded Computing (MECO)

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Abstract-This paper investigates the thermal monitoring performance potential of fibre-optic (FO) thermal sensors embedded into the structure of random wound coils. The implications of FO sensor packaging and installation procedure on thermal monitoring performance are assessed on a prototype coil in a series of laboratory experiments in a controlled thermal ambient. It is shown that, while the procedure of embedding the FO sensor into the coil structure alters its operational characteristics to an extent, the resulting in-situ hot spot thermal monitoring provides a reliable output when benchmarked with conventional thermocouple measurements.

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“…The detection and diagnosis of faults in complex machinery is advantageous for economical and security reasons (Tavner et al, 2008). Recent progress in computational intelligence, sensor technology and computing performance permit the use of advanced systems to achieve this objective.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition based Fault Diagnosis in Industrial Processes: Review and Application

Rauber¹,

Nascimento²,

Wandekokem³

et al. 2010

Pattern Recognition Recent Advances

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Condition Monitoring of Rotating Electrical Machines

Cited by 276 publications

References 0 publications

Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Wind Turbine Gearbox Condition Monitoring with AAKR and Moving Window Statistic Methods

Evaluation of fiber-optic sensing performance for embedded thermal monitoring of electric machinery wound components

Pattern Recognition based Fault Diagnosis in Industrial Processes: Review and Application

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