Toward Explainable AutoEncoder-Based Diagnosis of Dynamical Systems

Autoencoders have been used widely for diagnosing devices, for example, faults in rotating machinery. However, autoencoder-based approaches lack explainability for their results and can be hard to tune. In this article, we propose an explainable method for applying autoencoders for diagnosis, where...

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Bibliographic Details
Published in:Algorithms Vol. 16; no. 4; p. 178
Main Author: Provan, Gregory
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2023
Subjects:
autoencoder
Behavior
Deep learning
diagnosis
Dynamical systems
Fault diagnosis
Fault location (Engineering)
Machinery
principal angle
Rotating machinery
Semantics
subspace projection
System theory
Ireland
ISSN:1999-4893, 1999-4893
Online Access:Get full text
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Summary:Autoencoders have been used widely for diagnosing devices, for example, faults in rotating machinery. However, autoencoder-based approaches lack explainability for their results and can be hard to tune. In this article, we propose an explainable method for applying autoencoders for diagnosis, where we use a metric that maximizes the diagnostics accuracy. Since an autoencoder projects the input into a reduced subspace (the code), we define a theoretically well-understood approach, the subspace principal angle, to define a metric over the possible fault labels. We show how this approach can be used for both single-device diagnostics (e.g., faults in rotating machinery) and complex (multi-device) dynamical systems. We empirically validate the theoretical claims using multiple autoencoder architectures.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a16040178