Elsevier

Neurocomputing

Volume 456, 7 October 2021, Pages 268-287
Neurocomputing

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

https://doi.org/10.1016/j.neucom.2021.05.031Get rights and content
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open access

Highlights

  • Prognostics under dynamic operating regimes pose significant challenges.

  • Baselining consists in detecting and normalizing the data of different regimes.

  • A self-organizing map is proposed to detect the regimes.

  • An unsupervised multi-layer perceptron normalizes the data within each regime.

  • Our findings suggest the proposed combined model can baseline the data effectively.

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.

Keywords

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

Cited by (0)

Marcia L. Baptista (BS and MSc. in Informatics and Computer Engineering. Instituto Superior Tecnico, Lisbon, Portugal, September 2008) holds a PhD from the Engineering Design and Advanced Manufacturing (EDAM) program under the umbrella of MIT Portugal. Her research focuses on the development of prognostics techniques for aeronautics equipment. Her research interests include datadriven modeling, prognostics, and deep learning.

Elsa Maria Pires Henriques has a doctorate in Mechanical Engineering and is associated professor at Instituto Superior Tecnico in the University of Lisbon. She is responsible for the “Engineering Design and Advanced Manufacturing (LTI/EDAM)” post-graduation. During the last fifteen years, she has participated and/or coordinated several national and European R&D projects in collaboration with different industrial sectors, from tooling to automotive and aeronautics, mainly related to manufacturing, life cycle based decisions and management of complex design processes. She has a large number of scientific and technical publications in national and international conferences and journals. She was a national delegate in the 7th Framework Programme of the EU.

Kai Goebel is a Principal Scientist in the System Sciences Lab at Palo Alto Research Center (PARC). His interest is broadly in predictive maintenance and systems health management for a broad spectrum of cyber-physical systems in the manufacturing, energy, and transportation sectors. Prior to joining PARC, Dr. Goebel worked at NASA Ames Research Center and General Electric Corporate Research & Development center. At NASA, he founded and directed the Prognostics Center of Excellence which pioneered our understanding of the fundamental aspects of prognostics. He holds 18 patents and has published more than 375 papers, including a book on Prognostics. He received his Ph.D. in Mechanical Engineering from UC Berkeley in 1990 Dr. Goebel was an adjunct professor at Rensselaer Polytechnic Institute and is now adjunct professor at Lulea Technical University. He is a member of ASME, IEEE, SAE, AAAI; co-founder of the Prognostics and Health Management Society; and associate editor of the International Journal of PHM.