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
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
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.
Fig. 2 : The CLAIRE architecture. The encoder compresses the input x∈Rd into a lower-dimensional latent representation z∈Rk , 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 y^ . A dedicated Latent Exploration Layer enables interpretability analysis of z .
Define: fenc(x)←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
β -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) β -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 ( μ0 , μ1 ) and solid black lines indicating the discriminant threshold τ . The reported d′ 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.
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.
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.
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
Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management. In this paper, we introduce MODERN, a deep learning framework for quality monitoring and fault isolation, which integrates these enhanced capabilities into the practice of industrial quality control. Using the architecture of an inception residual neural network, we develop a control chart that monitors the likelihood of a product containing defects. We also propose a faulty region estimator that identifies the defective area using transfer learning. To extend our framework to cases where there are not sufficient training data, we suggest a transfer monitoring technique that requires only a small sample size and a hypothesis testing approach for quantitatively assessing the applicability of our method. Theoretically, we establish the minimax optimal convergence rate for both our defect likelihood estimation and fault diagnosis. Our results lead to a seemingly counter-intuitive managerial implication - it may not always be in a manufacturer's best interests to keep upgrading its monitoring equipment regardless of the cost. Empirically, we demonstrate the superior performance of our method in comparison with a state-of-the-art approach using both simulated experiments and real data.
Yicheng Kang, Yuling Jiao, Xin Geng +1
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
Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing while neglecting dependencies among target classes hinders its practical deployment. This paper introduces a novel and general framework with deep hierarchical knowledge loss (DHK) to achieve hierarchical consistent representation and prediction. We develop a novel hierarchical tree loss to enable a holistic mapping of same-attribute classes, leveraging tree-based positive and negative hierarchical knowledge constraints. We further design a focal hierarchical tree loss to enhance its extensibility and devise two adaptive weighting schemes based on tree height. In addition, we propose a group tree triplet loss with hierarchical dynamic margin by incorporating hierarchical group concepts and tree distance to model boundary structural knowledge across classes. The joint two losses significantly improve the recognition of subtle faults. Extensive experiments are performed on four real-world datasets from various industrial domains (three cavitation datasets from SAMSON AG and one publicly available dataset) for FID, all showing superior results and outperforming recent state-of-the-art FID methods.
Yu Sha, Shuiping Gou, Bo Liu +8
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