Tesi etd-09052019-145405
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Tipo di tesi
Dottorato
Autore
BARONE, FRANCESCO
URN
etd-09052019-145405
Titolo
SIGNAL AND IMAGE PROCESSING FOR TURBOMACHINERY MONITORING
Settore scientifico disciplinare
ING-INF/04
Corso di studi
INGEGNERIA - Ph.D. Programme in Emerging Digital Technologies (EDT)
Commissione
relatore Prof. DI PASQUALE, FABRIZIO CESARE FILIPPO
Presidente Prof. FERRARA, GIOVANNI
Membro Prof. SOLAZZI, MASSIMILIANO
Membro Dott. FARALLI, STEFANO
Presidente Prof. FERRARA, GIOVANNI
Membro Prof. SOLAZZI, MASSIMILIANO
Membro Dott. FARALLI, STEFANO
Parole chiave
- calibrazione della camera
- camera calibration
- centrifugal compressor
- compressore centrifugo
- deep learning
- Direct Linear Transformation
- DLT
- features extraction
- indici di stallo
- infrared radiation
- infrarossi
- pompaggio
- precursori di stallo
- pressure signal
- reflection compensation
- reflection model
- rilevamento dello stallo
- segnale di pressione
- stall detection
- stall index
- stall precursor
- stima della temperatura
- surge detection
- temperature estimation
- termografia
- thermography
- turbina
- turbine
- wDLT
- weighted Direct Linear Transformation
Data inizio appello
30/04/2020;
Disponibilità
parziale
Riassunto analitico
Oil&Gas industry requires reliable turbomachinery and industry 4.0 relies on sensing and data processing to make this possible. The collected data and its processing can predict failure and improve the health of turbomachines, resulting in a reduction of maintenance cost and increase in performance.
This thesis includes two parts: the first one is about processing of dynamic pressure signals to detect stall and surge in centrifugal compressors; the second one is about gas turbine rotor blade temperature estimation from infrared image.
Stall and surge detection
Pressure signals from a model test of a centrifugal compressor stage are recorded and processed. Relation between instabilities, signal unsteadiness and spectra is investigated. A novel approach, based on deep learning, was proposed and tested.
Gas turbine blade temperature estimation
Accurate mesurements of the surface temperature profile inside a gas turbine from infrared thermographic images requires non trivial models of infrared
emission and reflection. In this section, a novel approach for achieving this goal is presented, which includes a calibration algorithm using reference thermocouples in some positions on the stator.
This thesis includes two parts: the first one is about processing of dynamic pressure signals to detect stall and surge in centrifugal compressors; the second one is about gas turbine rotor blade temperature estimation from infrared image.
Stall and surge detection
Pressure signals from a model test of a centrifugal compressor stage are recorded and processed. Relation between instabilities, signal unsteadiness and spectra is investigated. A novel approach, based on deep learning, was proposed and tested.
Gas turbine blade temperature estimation
Accurate mesurements of the surface temperature profile inside a gas turbine from infrared thermographic images requires non trivial models of infrared
emission and reflection. In this section, a novel approach for achieving this goal is presented, which includes a calibration algorithm using reference thermocouples in some positions on the stator.
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