We present a coevolutionary optimization approach for the automatic and unsupervised extraction of industrial component degradation indicators from a set of signals collected during operation. It embeds a deep sparse autoencoder (SAE) for the extraction of the degradation indicators, into a multi-objective coevolutionary optimization algorithm, which maximizes the SAE's performance by optimizing its architecture and hyperparameters. The effectiveness of the proposed approach is shown by its application to a synthetic dataset, which mimics the operation of a degrading component in an environment affected by seasonal changes.

A coevolutionary optimization approach with deep sparse autoencoder for the extraction of equipment degradation indicators

Antonello F.;Baraldi P.;Zio E.
2020

Abstract

We present a coevolutionary optimization approach for the automatic and unsupervised extraction of industrial component degradation indicators from a set of signals collected during operation. It embeds a deep sparse autoencoder (SAE) for the extraction of the degradation indicators, into a multi-objective coevolutionary optimization algorithm, which maximizes the SAE's performance by optimizing its architecture and hyperparameters. The effectiveness of the proposed approach is shown by its application to a synthetic dataset, which mimics the operation of a degrading component in an environment affected by seasonal changes.
30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
Coevolutionary optimization algorithm
Degradation indicator
Prognostics and health management (phm)
Sparse autoencoder
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1181259
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