Bi-Signature optical spectroscopy for online fault detection in electrical machines

A novel bi-signature optical spectroscopy for fault detection in electrical machines is presented. The combined use of long period grating (LPG) and two fibre Bragg gratings (FBG1 and FBG2) is implemented to discriminate between vibration and temperature sensitivity in the detection of machine fault...

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Main Authors: Belema P. Alalibo, Wenping Cao, Zheng Liu
Format: Article
Language:English
Published: Wiley 2019-05-01
Series:The Journal of Engineering
Subjects:
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8062
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spelling doaj-0fe72acd74aa4ec08766524efeec259e2021-04-02T16:52:45ZengWileyThe Journal of Engineering2051-33052019-05-0110.1049/joe.2018.8062JOE.2018.8062Bi-Signature optical spectroscopy for online fault detection in electrical machinesBelema P. Alalibo0Wenping Cao1Zheng Liu2Aston UniversityAston UniversityAston UniversityA novel bi-signature optical spectroscopy for fault detection in electrical machines is presented. The combined use of long period grating (LPG) and two fibre Bragg gratings (FBG1 and FBG2) is implemented to discriminate between vibration and temperature sensitivity in the detection of machine faults. With LPG having higher sensitivity to temperature compared to both FBGs, machine faults are detected through spectral analysis of both signatures; and the optimal detection signature for each fault is consequently analysed. This novel technique utilises the principle of a shift in the wavelengths of the gratings to determine the kind of fault present in an electrical machine as the signature spectroscopy reveals varying amount of Bragg wavelength shifts for various fault types. The use of FBG sensing for fault detection in electrical machines has the potential of revolutionising non-intrusive real-time condition monitoring of future industrial machines with high reliability due to zero electromagnetic interference (EMI) as well as significant low cost of fibre-optic sensors.https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8062condition monitoringBragg gratingsfibre optic sensorsspectral analysisfault diagnosiselectromagnetic interferencetemperature measurementvibration measurementzero electromagnetic interferencenon-intrusive real-time condition monitoringspectral analysisvibration sensitivitytemperature sensitivitybi-signature optical spectroscopyindustrial machineslong period gratingonline fault detectionfibre-optic sensorsBragg wavelength shiftselectrical machineoptimal detection signaturefibre Bragg gratings
collection DOAJ
language English
format Article
sources DOAJ
author Belema P. Alalibo
Wenping Cao
Zheng Liu
spellingShingle Belema P. Alalibo
Wenping Cao
Zheng Liu
Bi-Signature optical spectroscopy for online fault detection in electrical machines
The Journal of Engineering
condition monitoring
Bragg gratings
fibre optic sensors
spectral analysis
fault diagnosis
electromagnetic interference
temperature measurement
vibration measurement
zero electromagnetic interference
non-intrusive real-time condition monitoring
spectral analysis
vibration sensitivity
temperature sensitivity
bi-signature optical spectroscopy
industrial machines
long period grating
online fault detection
fibre-optic sensors
Bragg wavelength shifts
electrical machine
optimal detection signature
fibre Bragg gratings
author_facet Belema P. Alalibo
Wenping Cao
Zheng Liu
author_sort Belema P. Alalibo
title Bi-Signature optical spectroscopy for online fault detection in electrical machines
title_short Bi-Signature optical spectroscopy for online fault detection in electrical machines
title_full Bi-Signature optical spectroscopy for online fault detection in electrical machines
title_fullStr Bi-Signature optical spectroscopy for online fault detection in electrical machines
title_full_unstemmed Bi-Signature optical spectroscopy for online fault detection in electrical machines
title_sort bi-signature optical spectroscopy for online fault detection in electrical machines
publisher Wiley
series The Journal of Engineering
issn 2051-3305
publishDate 2019-05-01
description A novel bi-signature optical spectroscopy for fault detection in electrical machines is presented. The combined use of long period grating (LPG) and two fibre Bragg gratings (FBG1 and FBG2) is implemented to discriminate between vibration and temperature sensitivity in the detection of machine faults. With LPG having higher sensitivity to temperature compared to both FBGs, machine faults are detected through spectral analysis of both signatures; and the optimal detection signature for each fault is consequently analysed. This novel technique utilises the principle of a shift in the wavelengths of the gratings to determine the kind of fault present in an electrical machine as the signature spectroscopy reveals varying amount of Bragg wavelength shifts for various fault types. The use of FBG sensing for fault detection in electrical machines has the potential of revolutionising non-intrusive real-time condition monitoring of future industrial machines with high reliability due to zero electromagnetic interference (EMI) as well as significant low cost of fibre-optic sensors.
topic condition monitoring
Bragg gratings
fibre optic sensors
spectral analysis
fault diagnosis
electromagnetic interference
temperature measurement
vibration measurement
zero electromagnetic interference
non-intrusive real-time condition monitoring
spectral analysis
vibration sensitivity
temperature sensitivity
bi-signature optical spectroscopy
industrial machines
long period grating
online fault detection
fibre-optic sensors
Bragg wavelength shifts
electrical machine
optimal detection signature
fibre Bragg gratings
url https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8062
work_keys_str_mv AT belemapalalibo bisignatureopticalspectroscopyforonlinefaultdetectioninelectricalmachines
AT wenpingcao bisignatureopticalspectroscopyforonlinefaultdetectioninelectricalmachines
AT zhengliu bisignatureopticalspectroscopyforonlinefaultdetectioninelectricalmachines
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