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A novel condition monitoring methodology based on neural network of pump-turbines with extended operating range

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hdl:2117/363200

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Zhao, Weiqiang
Egusquiza Estévez, EduardMés informacióMés informacióMés informació
Valero Ferrando, Ma. del CarmenMés informacióMés informació
Egusquiza Montagut, MònicaMés informacióMés informacióMés informació
Valentín Ruiz, DavidMés informacióMés informacióMés informació
Presas Batlló, AlexandreMés informacióMés informacióMés informació
Document typeConference report
Defense date2019
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
This work is protected by the corresponding intellectual and industrial property rights. Except where otherwise noted, its contents are licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Due to the entrance of new renewable energies, water-storage energy has to be regulated more frequently to keep the stability of power grid. Consequently, pump-turbines have to work under offdesign conditions more than before, which will cause more damage and decrease their useful life. Advanced monitoring methodologies that can balance the degradation of machine and revenues to the power plant has been required. To develop an innovative condition monitoring approach, vibration data was collected from different components of a pump-turbine which is running in an extended operating range. The consequences of operating range extension on the vibration of the pump-turbine have been studied by analysing the vibration signatures. The changing rule of the vibration behavior of the machine with the operating parameters has been obtained. An artificial neural network based model has been applied to build an autoregressive normal behavior model. The results indicated that the normal behavior model based on multi-layer neural net has the ability to predict the vibration characteristics of the machine in different operating conditions. This monitoring method can be adapted to the similar type of hydraulic turbine units.
CitationZhao, W. [et al.]. A novel condition monitoring methodology based on neural network of pump-turbines with extended operating range. A: Workshop on Technical Diagnostics. "16th IMEKO TC10 Conference on Testing, Diagnostics & Inspection as a comprehensive value chain for Quality & Safety". 2019, p. 154-159. ISBN 978-929900841-6. 
URIhttp://hdl.handle.net/2117/363200
ISBN978-929900841-6
Other identifiershttps://www.imeko.org/publications/tc10-2019/IMEKO-TC10-2019-024.pdf
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  • Departament de Mecànica de Fluids - Ponències/Comunicacions de congressos [209]
  • FLUIDS - Enginyeria de Fluids - Ponències/Comunicacions de congressos [46]
  • Departament d'Enginyeria de Projectes i de la Construcció - Ponències/Comunicacions de congressos [192]
  • CDIF - Centre de Diagnòstic Industrial i Fluidodinàmica - Ponències/Comunicacions de congressos [48]
  • Doctorat en Enginyeria Mecànica, Fluids i Aeronàutica - Ponències/Comunicacions de congressos [66]
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