Loading [a11y]/accessibility-menu.js
Improving Machine Learning Diagnostic Systems with Model-Based Data Augmentation - Part A: Data Generation | IEEE Conference Publication | IEEE Xplore

Improving Machine Learning Diagnostic Systems with Model-Based Data Augmentation - Part A: Data Generation


Abstract:

Various diagnostic systems based on artificial intelligence or machine learning algorithms are already being used today to monitor electrical equipment in power supply sy...Show More

Abstract:

Various diagnostic systems based on artificial intelligence or machine learning algorithms are already being used today to monitor electrical equipment in power supply systems. The challenge of these data-based diagnostic approaches lies in dealing with the limited fault-condition data available. One possible solution to this problem are data augmentation techniques that generate synthetic data from existing data. In this paper, we develop a model-based data augmentation approach that uses computer-implementable, electromechanical models to generate synthetic data. This approach uses statistical information extracted from the available data to sample model parameters and generate synthetic normal- and fault-condition data. It is shown for vibration measurements of a power and distribution transformer that the proposed model-based data augmentation can generate realistic synthetic normal- and fault-condition data.
Date of Conference: 18-21 October 2021
Date Added to IEEE Xplore: 21 December 2021
ISBN Information:
Conference Location: Espoo, Finland

Funding Agency:


References

References is not available for this document.