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

TL;DR

CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems and highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.

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-theory-based 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.

Figures (8)

• Figure 1: Encoder--Decoder Structure with Latent Space Regularization. This architecture incorporates reconstruction loss and latent variance loss. Note that, a supervised learning layer, optimized using cross-entropy loss and entropy regularization, and a latent exploration module (both not shown here) follow the encoder. These components are discussed in detail throughout the paper and illustrated in Fig. 2.
• Figure 2: The CLAIRE architecture. The encoder compresses the input $\mathbf{x} \in \mathbb{R}^d$ into a lower-dimensional latent representation $\mathbf{z} \in \mathbb{R}^k$, passing through multiple dense layers. Dropout Regularization (DR) and Batch Normalization (BN) are applied after each layer to improve generalization and training stability. The decoder reconstructs the input, while the latent code is also used by a downstream classifier to predict the label $\hat{y}$. A dedicated Latent Exploration Layer enables interpretability analysis of $\mathbf{z}$.
• Figure 3: Loss convergence over training epochs on the SECOM dataset.
• Figure 4: Visualization of latent representations for SECOM. a) Three-dimensional t-SNE embeddings for four models: (I) CLAIRE, (II) Standard Autoencoder (AE), (III) Variational Autoencoder (VAE), and (IV) $\beta$-VAE. Each point represents a sample colored by its class label (blue: Class 0, red: Class 1). b) LDA one-dimensional projections of latent spaces. Histograms and Gaussian fits illustrate the class distributions, with vertical dashed lines marking class means ($\mu_0$, $\mu_1$) and solid black lines indicating the discriminant threshold $\tau$. The reported $d'$ values quantify the separation between classes, highlighting the superior discriminability of CLAIRE compared to baseline models.
• Figure 5: LDA one-dimensional projections of the latent representations obtained for TEP. CLAIRE demonstrates a substantially larger separation compared to the baseline models.