scholarly journals Comparison of Off-line and On-line Measurement of Partial Discharges for Hydrogenerator Stator Windings using Acoustic Emission Sensors

2006 ◽  
Vol 126 (6) ◽  
pp. 578-585
Author(s):  
Tadamitsu Kaneko ◽  
Takashi Ueda ◽  
Osamu Takenouchi ◽  
Masahisa Otsubo ◽  
Chikahisa Honda ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Partial Discharges ◽  
Stator Windings ◽  
Line Measurement ◽  
Acoustic Emission Sensors ◽  
On Line

scholarly journals On-line Partial Discharge Measurement of Hydrogenerator Stator Windings using Acoustic Emission Detection Techniques

2004 ◽  
Vol 124 (2) ◽  
pp. 274-280 ◽  
Author(s):  
Tadamitsu Kaneko ◽  
Akito Takemura ◽  
Osamu Takenouchi ◽  
Youl-Moon Sung ◽  
Masahisa Otsubo ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Partial Discharge ◽  
Discharge Measurement ◽  
Detection Techniques ◽  
Stator Windings ◽  
On Line ◽  
Emission Detection

scholarly journals Automatic PRPD Image Recognition of Multiple Simultaneous Partial Discharge Sources in On-Line Hydro-Generator Stator Bars

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 326
Author(s):  
Ramon C. F. Araújo ◽  
Rodrigo M. S. de Oliveira ◽  
Fabrício J. B. Barros
Keyword(s):  
Partial Discharge ◽  
Classification Problem ◽  
Input Pattern ◽  
Partial Discharges ◽  
Data Set ◽  
Stator Windings ◽  
Unknown Sample ◽  
On Line ◽  
A New Technique ◽  
Generator Stator

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

Contribution of Surface Finish Monitoring Signals in CNC Taper Turning

2014 ◽  
Vol 797 ◽  
pp. 41-46 ◽  
Author(s):  
Eustaquio García Plaza ◽  
Pedro José Núñez ◽  
David Rodríguez Salgado ◽  
Inocente Cambero Rivero ◽  
José María Herrera Olivenza ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Surface Finish ◽  
Mechanical Vibration ◽  
Systems Design ◽  
System Optimization ◽  
Post Process ◽  
Acoustic Emission Sensors ◽  
On Line ◽  
Cutting Processes ◽  
Machining Operations

On-line monitoring systems eliminate the need for post-process evaluation, reduce production time and costs, and enhance automation of the process. The cutting forces, mechanical vibration and acoustic emission signals obtained using dynamometer, accelerometer, and acoustic emission sensors respectively have been extensively used to monitor several aspects of the cutting processes in automated machining operations. Notwithstanding, determining the optimum selection of on-line signals is crucial to enhancing system optimization requiring a low computational load yet effective prediction of cutting process parameters. This study assess the contribution of three types of signals for the on-line monitoring and diagnosis of the surface finish (Ra) in automated taper turning operations. Systems design were based on predictive models obtained from regression analysis and artificial neural networks, involving numerical parameters that characterize cutting force signals (Fx, Fy, Fz), mechanical vibration (ax, ay, az), and acoustic emission (EARMS).

scholarly journals Estimation of Position of On-line Partial Discharge on Hydrogenerator Stator Windings using Acoustic Emission Detection Techniques

2004 ◽  
Vol 124 (7) ◽  
pp. 534-540 ◽  
Author(s):  
Tadamitsu Kaneko ◽  
Akito Takemura ◽  
Osamu Takenouchi ◽  
Youl-Moon Sung ◽  
Masahisa Otsubo ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Partial Discharge ◽  
Detection Techniques ◽  
Stator Windings ◽  
On Line ◽  
Emission Detection

Multi-Sensor Detection and Estimation of Gaps When Drilling CFRP Composite Stacks

2014 ◽  
Author(s):  
Eshetu D. Eneyew ◽  
M. Ramulu
Keyword(s):  
Acoustic Emission ◽  
Thrust Force ◽  
Line Detection ◽  
Audio Signals ◽  
Detection And Estimation ◽  
Novel Approach ◽  
Cfrp Composite ◽  
Acoustic Emission Sensors ◽  
On Line ◽  
Sensor Detection

To avoid the uncertainty of the presence of gaps between CFRP stacks and to eliminate the inspection for gaps after every drilled hole, a novel approach for on-line gap detection and estimation when drilling CFRP composite stacks through various signal profiles was proposed. Experimental investigation of the proposed method was conducted using thrust force, vibration, acoustic emission, and audio signals, which were collected using dynamometer, accelerometer, acoustic emission sensors, and an air-coupled audio microphone respectively. A predetermined gap was introduced between the stacks of CFRP plates in the investigation. The results from this experimental study reveal the potentials of the proposed approach for on-line detection and estimation of gap when drilling CFRP composite stacks.

On-line monitoring of partial discharges on stator windings of large rotating machines in the petrochemical environment

1998 ◽  
Vol 34 (6) ◽  
pp. 1359-1365 ◽  
Author(s):  
A. Bethge ◽  
P.K.-W. Lo ◽  
J.T. Phillipson ◽  
J. Weidner
Keyword(s):  
Partial Discharges ◽  
Rotating Machines ◽  
Stator Windings ◽  
On Line
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