CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation — A Deep Learning Framework for Smart Manufacturing

Ghahramani, Mohammadhossein and Zhou, Mengchu (2026) CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation — A Deep Learning Framework for Smart Manufacturing. IEEE Transactions on Systems, Man, and Cybernetics: Systems. ISSN 2168-2216 (In Press)

Text CLAIRE-MG.pdf - Accepted Version Restricted to Repository staff only Download (5MB) | Request a copy

Abstract

Accurate fault detection in high-dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This paper introduces CLAIRE, i.e., a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems. It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features. The learned representations are then fed into a downstream classifier to perform binary fault prediction. Experimental results on a high-dimensional dataset demonstrate that CLAIRE significantly outperforms conventional classifiers trained directly on raw features. Moreover, the framework incorporates a post hoc phase, using a game-theorybased interpretability technique, to analyze the latent space and identify the most informative input features contributing to fault predictions. The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection. The modular and interpretable nature of the proposed framework makes it highly adaptable, offering promising applications in other domains characterized by complex, high-dimensional data, such as healthcare, finance, and environmental monitoring.

Item Type:	Article
Identification Number:	10.1109/TSMC.2026.3671163
Dates:	Date Event 2 March 2026 Accepted
Uncontrolled Keywords:	Explainable AI, Autoencoder, Fault Detection, Deep Learning, High-Dimensional Data, Feature Extraction.
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	10 Mar 2026 13:34
Last Modified:	10 Mar 2026 13:34
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16919

Actions (login required)

View Item