Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus
The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant econom...
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doaj-4ea26996a9d74f3fbd060fe544f9184c2020-11-25T03:00:58ZengMDPI AGEnergies1996-10732020-03-01135110910.3390/en13051109en13051109Unsupervised Monitoring System for Predictive Maintenance of High Voltage ApparatusChristian Gianoglio0Edoardo Ragusa1Andrea Bruzzone2Paolo Gastaldo3Rodolfo Zunino4Francesco Guastavino5Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, ItalyElectrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, ItalyElectrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, ItalyElectrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, ItalyElectrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, ItalyElectrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, ItalyThe online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices.https://www.mdpi.com/1996-1073/13/5/1109predictive maintenanceembedded systemspartial discharges |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Christian Gianoglio Edoardo Ragusa Andrea Bruzzone Paolo Gastaldo Rodolfo Zunino Francesco Guastavino |
spellingShingle |
Christian Gianoglio Edoardo Ragusa Andrea Bruzzone Paolo Gastaldo Rodolfo Zunino Francesco Guastavino Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus Energies predictive maintenance embedded systems partial discharges |
author_facet |
Christian Gianoglio Edoardo Ragusa Andrea Bruzzone Paolo Gastaldo Rodolfo Zunino Francesco Guastavino |
author_sort |
Christian Gianoglio |
title |
Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus |
title_short |
Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus |
title_full |
Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus |
title_fullStr |
Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus |
title_full_unstemmed |
Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus |
title_sort |
unsupervised monitoring system for predictive maintenance of high voltage apparatus |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2020-03-01 |
description |
The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices. |
topic |
predictive maintenance embedded systems partial discharges |
url |
https://www.mdpi.com/1996-1073/13/5/1109 |
work_keys_str_mv |
AT christiangianoglio unsupervisedmonitoringsystemforpredictivemaintenanceofhighvoltageapparatus AT edoardoragusa unsupervisedmonitoringsystemforpredictivemaintenanceofhighvoltageapparatus AT andreabruzzone unsupervisedmonitoringsystemforpredictivemaintenanceofhighvoltageapparatus AT paologastaldo unsupervisedmonitoringsystemforpredictivemaintenanceofhighvoltageapparatus AT rodolfozunino unsupervisedmonitoringsystemforpredictivemaintenanceofhighvoltageapparatus AT francescoguastavino unsupervisedmonitoringsystemforpredictivemaintenanceofhighvoltageapparatus |
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1724695788718653440 |