Condition-Based Maintenance of Gensets in District Heating Using Unsupervised Normal Behavior Models Applied on SCADA Data

Valerio Francesco Barnabei (), Fabrizio Bonacina, Alessandro Corsini, Francesco Aldo Tucci and Roberto Santilli
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Valerio Francesco Barnabei: Department of Mechanical and Aerospace Engineering, University of Rome La Sapienza, Via Eudossiana 18, I00184 Rome, Italy
Fabrizio Bonacina: Department of Mechanical and Aerospace Engineering, University of Rome La Sapienza, Via Eudossiana 18, I00184 Rome, Italy
Alessandro Corsini: Department of Mechanical and Aerospace Engineering, University of Rome La Sapienza, Via Eudossiana 18, I00184 Rome, Italy
Francesco Aldo Tucci: Department of Mechanical and Aerospace Engineering, University of Rome La Sapienza, Via Eudossiana 18, I00184 Rome, Italy
Roberto Santilli: ENGIE Servizi S.p.A, District Heating and Power, Viale Avignone 12, I00144 Rome, Italy

Energies, 2023, vol. 16, issue 9, 1-15

Abstract: Increasing interest in natural gas-fired gensets is motivated by District Heating (DH) network applications, especially in urban areas. Even if they represent customary solutions, when used in DH, duty regimes are driven by network thermal energy demands resulting in discontinuous operation, which affects their remaining useful life. As such, the attention on effective condition-based maintenance has gained momentum. In this paper, a novel unsupervised anomaly detection framework is proposed for gensets in DH networks based on Supervisory Control And Data Acquisition (SCADA) data. The framework relies on multivariate Machine-Learning (ML) regression models trained with a Leave-One-Out Cross-Validation method. Model residuals generated during the testing phase are then post-processed with a sliding threshold approach based on a rolling average. This methodology is tested against nine major failures that occurred on the gas genset installed in the Aosta DH plant in Italy. The results show that the proposed framework successfully detects anomalies and anticipates SCADA alarms related to unscheduled downtime.

Keywords: multivariate time series; early fault detection; condition based maintenance; multi-MW gensets SCADA data (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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