Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

Christian Gianoglio, Edoardo Ragusa, Paolo Gastaldo, Federico Gallesi and Francesco Guastavino
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Christian Gianoglio: Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy
Edoardo Ragusa: Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy
Paolo Gastaldo: Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy
Federico Gallesi: Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy
Francesco Guastavino: Electrical, Electronics and Telecommunication Engineering and Naval Architecture Department (DITEN), University of Genoa, 16145 Genova, Italy

Energies, 2021, vol. 14, issue 15, 1-23

Abstract: Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors.

Keywords: predictive maintenance; convolutional neural networks; partial discharges (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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