A self-organizing map and a normalizing multi-layer perceptron approach to baselining in prognostics under dynamic regimes

Marcia Lourenco Baptista*, Elsa M. Elsa, Kai Goebel

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

When the influence of changing operational and environmental conditions, such as temperature and external loading, is not factored out from sensor data it can be difficult to observe a clear deterioration path. This can significantly affect the task of engineering prognostics and other health management operations. To address this problem of dynamic operating regimes, it is necessary to baseline the data, typically by first finding the operating regimes and then normalizing the data within each regime. This paper describes a baselining solution based on neural networks. A self-organizing map is used to identify the regimes, and a multi-layer perceptron is used to normalize the sensor data according to the detected regimes. Tests are performed on public datasets from a turbofan simulator. The approach can produce similar results to classical methods without the need to specify in advance the number of regimes and the explicit computation of the statistical properties of a hold-out dataset. Importantly, the techniques can be integrated into a deep learning system to perform prognostics in a single pass.

Original languageEnglish
Pages (from-to)268-287
Number of pages20
JournalNeurocomputing
Volume456
DOIs
Publication statusPublished - 2021

Keywords

  • Baselining
  • Normalizing multi-layer perceptron
  • Prognostics
  • Self-organizing map
  • Turbofan sensor data

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