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

Mohammadhossein Ghahramani; Mengchu Zhou

cs.LGMar 6, 2026

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

Authors: Mohammadhossein Ghahramani, Mengchu Zhou

Organizations: Birmingham City University, UK · the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA

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 & tables

Panel of Figure 1 — Fig. 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.

Method Category	Objective and Key Characteristics
Standard Autoencoder (AE)	Learn compact representations by minimizing reconstruction error; denoising AEs improve robustness but latent space geometry is not explicitly constrained for discriminative separability or compactness.
Variational AE	Introduce probabilistic latent variables with KL-based regularization to enforce prior matching and promote disentanglement; primarily designed for generative modeling rather than fault-specific discrimination.
Information Bottleneck AE	Optimize a trade-off between reconstruction and information compression; focus on minimal sufficient representations rather than explicit latent compactness or class separation.
Self-supervised and Contrastive Learning	Learn representations via instance-level contrastive objectives and data augmentations; often require structured inputs or domain-specific augmentation strategies, limiting applicability to tabular industrial sensor data.
CLAIRE (Proposed)	Jointly optimizes denoising reconstruction and latent variance regularization to explicitly shape compact and stable latent embeddings for fault discrimination, complemented by kernel-based classification and latent-level interpretability.

TABLE I : Conceptual comparison of representative deep representation learning frameworks for industrial fault detection.

Panel of Figure 3 — Fig. 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 $\mathbf{z}$ .

\text{Define: }f_{\text{enc}}(\mathbf{x})\leftarrow\text{Encoder module from trained CLAIRE}

Algorithm 2 Latent Space Interpretation

Fig. 3 : Loss convergence over training epochs on the SECOM dataset.

SECOM Dataset
Model	Accuracy	F1 Score
SVM (RBF kernel)	0.84	0.84
AE	0.86	0.86
VAE	0.85	0.84
$\beta$ -VAE	0.83	0.82
CLAIRE (Proposed)	0.94	0.93

TABLE II : Performance comparison of all baseline models and the proposed method (CLAIRE) on the SECOM and TEP datasets.

Fig. 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 $d^{\prime}$ values quantify the separation between classes, highlighting the superior discriminability of CLAIRE compared to baseline models.

Fig. 5 : LDA one-dimensional projections of the latent representations obtained for TEP. CLAIRE demonstrates a substantially larger separation compared to the baseline models.

Panel of Figure 9 — Fig. 6 : SHAP plots for the SECOM dataset, illustrating the most influential input features across four selected latent dimensions.

Rank	Index	SHAP Dim. 0	SHAP Dim. 1	…	SHAP Dim. 64	MSV
1	Feature 13	0.0801	0.0283	…	0.0302	0.0380
2	Feature 5	0.0712	0.0558	…	0.0310	0.0368
3	Feature 24	0.0510	0.0212	…	0.0291	0.0356
4	Feature 12	0.0604	0.0273	…	0.0306	0.0286
5	Feature 15	0.0583	0.0241	…	0.0148	0.0282
6	Feature 39	0.0470	0.0207	…	0.0201	0.0267

TABLE III : Top 10 globally important input features for the SECOM dataset, ranked according to their mean SHAP values (MSV) across all 64 latent dimensions.

Rank	Index	SHAP Dim. 0	SHAP Dim. 1	…	SHAP Dim. 32	MSV
1	Feature 17	0.0593	0.1040	…	0.1002	0.1004
2	Feature 49	0.0543	0.0768	…	0.0746	0.0752
3	Feature 51	0.0821	0.0614	…	0.0622	0.0641
4	Feature 50	0.0422	0.0483	…	0.0510	0.0479
5	Feature 12	0.0272	0.0414	…	0.0415	0.0424
6	Feature 16	0.0796	0.0409	…	0.0401	0.0417

TABLE IV : Top 10 globally important input features for the TEP dataset, ranked based on their MSVs across all 32 latent dimensions.

Fig. 7 : SHAP dependence plot for Feature 13 on the SECOM dataset, with color coding based on Feature 26. The plot demonstrates how changes in Feature 13 influence the model’s latent representation, while also revealing interaction effects that are modulated by Feature 26.

Fig. 8 : SHAP dependence plot for Feature 17 on the TEP dataset, with color coding by Feature 18. The figure highlights interaction effects that are further modulated by Feature 18.

Industrial Fault Diagnosis

See all 27 papers

S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis
Mar 9, 2026Baoxue Li, Chunhui ZhaoFault Diagnosis Industrial Fault Diagnosis
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China · School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, China
Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis
Aug 14, 2026Yicheng Kang, Yuling Jiao, Xin Geng +1Fault Diagnosis Industrial Inspection
Farmer School of Business, Miami University, OH. USA · School of Mathematics & Statistics, Wuhan University, Hubei, China · Miami Herbert Business School, University of Miami, FL. USA +1
Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis
Apr 8, 2026Yu Sha, Shuiping Gou, Bo Liu +8Fault Diagnosis Industrial Fault Diagnosis
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China · Frankfurt Institute for Advanced Studies, Frankfurt am Main 60438, Germany · School of Artificial Intelligence, Xidian University, Xi’an 710126, China +5

Mar 9, 2026J/K move · Enter open · S save

S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis

Baoxue Li, Chunhui Zhao

State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, China · School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, China

Fault Diagnosis Industrial Fault Diagnosis

Open paper AskPDF arXiv

Fault diagnosis is critical for the safe operation of industrial systems. Conventional diagnosis models typically produce abstract outputs such as anomaly scores or fault categories, failing to answer critical operational questions like "Why" or "How to repair". While large language models (LLMs) offer strong generalization and reasoning abilities, their training on discrete textual corpora creates a semantic gap when processing high-dimensional, temporal industrial signals. To address this challenge, we propose a Signals-to-Semantics fault diagnosis (S2S-FDD) framework that bridges high-dimensional sensor signals with natural language semantics through two key innovations: We first design a Signal-to-Semantic operator to convert abstract time-series signals into natural language summaries, capturing trends, periodicity, and deviations. Based on the descriptions, we design a multi-turn tree-structured diagnosis method to perform fault diagnosis by referencing historical maintenance documents and dynamically querying additional signals. The framework further supports human-in-the-loop feedback for continuous refinement. Experiments on the multiphase flow process show the feasibility and effectiveness of the proposed method for explainable zero-shot fault diagnosis.

Abstract

Industrial Fault Diagnosis

Explore similar work

S2S-FDD: Bridging Industrial Time Series and Natural Language for Explainable Zero-shot Fault Diagnosis

Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis