Gritli, Yasser
(2014)
Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in
Ingegneria elettrotecnica, 26 Ciclo. DOI 10.6092/unibo/amsdottorato/6238.
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Abstract
In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following:
- Capability of monitoring the fault evolution continuously over time under any transient operating condition;
- Speed/slip measurement or estimation is not required;
- Higher accuracy in filtering frequency components around the fundamental in case of rotor faults;
- Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band);
- Low memory requirement due to low sampling frequency;
- Reduction in the latency of time processing (no requirement of repeated sampling operation).
Abstract
In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following:
- Capability of monitoring the fault evolution continuously over time under any transient operating condition;
- Speed/slip measurement or estimation is not required;
- Higher accuracy in filtering frequency components around the fundamental in case of rotor faults;
- Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band);
- Low memory requirement due to low sampling frequency;
- Reduction in the latency of time processing (no requirement of repeated sampling operation).
Tipologia del documento
Tesi di dottorato
Autore
Gritli, Yasser
Supervisore
Dottorato di ricerca
Scuola di dottorato
Ingegneria industriale
Ciclo
26
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Diagnosis, electrical machines,signal processing, fault detection.
URN:NBN
DOI
10.6092/unibo/amsdottorato/6238
Data di discussione
11 Marzo 2014
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Gritli, Yasser
Supervisore
Dottorato di ricerca
Scuola di dottorato
Ingegneria industriale
Ciclo
26
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Diagnosis, electrical machines,signal processing, fault detection.
URN:NBN
DOI
10.6092/unibo/amsdottorato/6238
Data di discussione
11 Marzo 2014
URI
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