Improving Machine-Learning Diagnostics with Model-Based Data Augmentation Showcased for a Transformer Fault

Jannis N. Kahlen, Michael Andres and Albert Moser
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Jannis N. Kahlen: Fraunhofer Institute for Applied Information Technology, Digital Energy, 53757 Sankt Augustin, Germany
Michael Andres: Fraunhofer Institute for Applied Information Technology, Digital Energy, 53757 Sankt Augustin, Germany
Albert Moser: Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics, RWTH Aachen University, 52062 Aachen, Germany

Energies, 2021, vol. 14, issue 20, 1-20

Abstract: Machine-learning diagnostic systems are widely used to detect abnormal conditions in electrical equipment. Training robust and accurate diagnostic systems is challenging because only small databases of abnormal-condition data are available. However, the performance of the diagnostic systems depends on the quantity and quality of the data. The training database can be augmented utilizing data augmentation techniques that generate synthetic data to improve diagnostic performance. However, existing data augmentation techniques are generic methods that do not include additional information in the synthetic data. In this paper, we develop a model-based data augmentation technique integrating computer-implementable electromechanical models. Synthetic normal- and abnormal-condition data are generated with an electromechanical model and a stochastic parameter value sampling method. The model-based data augmentation is showcased to detect an abnormal condition of a distribution transformer. First, the synthetic data are compared with the measurements to verify the synthetic data. Then, ML-based diagnostic systems are created using model-based data augmentation and are compared with state-of-the-art diagnostic systems. It is shown that using the model-based data augmentation results in an improved accuracy compared to state-of-the-art diagnostic systems. This holds especially true when only a small abnormal-condition database is available.

Keywords: electrical power equipment; small sample size; data augmentation; diagnostics; fault detection; machine 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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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