A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments

Maria Rosaria Termite, Piero Baraldi, Sameer Al-Dahidi, Luca Bellani, Michele Compare and Enrico Zio
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Maria Rosaria Termite: Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Piero Baraldi: Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
Sameer Al-Dahidi: Department of Mechanical and Maintenance Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Luca Bellani: Aramis Srl, Via pergolesi 5, 20121 Milano, Italy
Michele Compare: Aramis Srl, Via pergolesi 5, 20121 Milano, Italy
Enrico Zio: Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy

Energies, 2019, vol. 12, issue 24, 1-26

Abstract: Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synthetic case study and a real case study concerning the monitoring of the tank pressure of an aero derivative gas turbine lube oil system. The CM model provides satisfactory performances in terms of classification accuracy, while remarkably reducing the expert efforts for data labeling and model (periodic) updating.

Keywords: condition monitoring; fault detection and diagnostics; energy systems; time series; clustering; classification; never-ending learning (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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