Fault Detection and Control Systems

Neueste Paper

• Graph Autoencoder for Process Monitoring

  Xiangrui Zhang · 21. Mai 2026

  To improve the reliability and interpretability of industrial process monitoring, this article proposes a Causal Graph Spatial-Temporal Autoencoder (CGSTAE). The network architecture of CGSTAE combines two components: a correlation graph structure learning module based on spatial self-attention mech…

• UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction

  Robson W. S. Pessoa, Julien Amblard, Alessandra Russo, Idelfonso B. R. Nogueira · 19. Mai 2026

  Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on single-modality sensor data. This work introduces UTOPYA (Unified Temporal Observation for Physics-Informed Anomaly Detection and Time-Series Prediction), a 15.2M-parameter multimodal framewo…

• Industrial AI Robustness Card for Time Series Models

  Alexander Windmann, Benedikt Stratmann, Mariya Lyashenko, Oliver Niggemann · 13. Mai 2026

  Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation-ready protocols. This paper introduces the Industrial AI Robustness Card for Time Series (IARC-TS), a lightweight protocol for documenting and evaluating industrial …

• Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation

  Caitlin Ho, Andrea Arnold · 4. Mai 2026

  Incorporating a priori physics knowledge into machine learning leads to more robust and interpretable algorithms. In this work, we combine deep learning techniques and classic numerical methods for differential equations to address two challenging missing physics problems in dynamical systems theory…

• A Framework for Variational Inference of Lightweight Bayesian Neural Networks with Heteroscedastic Uncertainties

  David J. Schodt, Ryan Brown, Michael Merritt, Samuel Park, Delsin Menolascino, Mark A. Peot · 1. Mai 2026

  Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications. Often, heteroscedastic aleatoric uncertainties are learned as outputs of the BNN in addition to the predictive means, however doing so may necessitate adding more learnable paramete…

• Orthogonal Representation Learning for Estimating Causal Quantities

  Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal, Stefan Feuerriegel · 28. April 2026

  End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its efficiency remained unclear. Here, we face a central tension: End-to-end representation learning methods often work well in practice but lack asymptotic op…

• Multi-Level Temporal Graph Networks with Local-Global Fusion for Industrial Fault Diagnosis

  Bibek Aryal, Gift Modekwe, Qiugang Lu · 22. April 2026

  Fault detection and diagnosis are critical for the optimal and safe operation of industrial processes. The correlations among sensors often display non-Euclidean structures where graph neural networks (GNNs) are widely used therein. However, for large-scale systems, local, global, and dynamic relati…

• Practical estimation of the optimal classification error with soft labels and calibration

  Ryota Ushio, Takashi Ishida, Masashi Sugiyama · 17. April 2026

  While the performance of machine learning systems has experienced significant improvement in recent years, relatively little attention has been paid to the fundamental question: to what extent can we improve our models? This paper provides a means of answering this question in the setting of binary …

• On an $L^2$ norm for stationary ARMA processes

  Anand Ganesh, Babhrubahan Bose, Anand Rajagopalan · 16. April 2026

  We propose an $L^2$ norm for stationary Autoregressive Moving Average (ARMA) models. We look at ARMA models within the Hilbert space of the past with present of a true purely linearly non-deterministic stationary process $X_t$, and compute the $L^2$ norm based on its Wold decomposition. As an applic…

• Towards Generalized Certified Robustness with Multi-Norm Training

  Enyi Jiang, David S. Cheung, Gagandeep Singh · 15. April 2026

  Existing certified training methods can only train models to be robust against a certain perturbation type (e.g. $l_\infty$ or $l_2$). However, an $l_\infty$ certifiably robust model may not be certifiably robust against $l_2$ perturbation (and vice versa) and also has low robustness against other p…

• A Complete Decomposition of KL Error using Refined Information and Mode Interaction Selection

  James Enouen, Mahito Sugiyama · 14. April 2026

  The log-linear model has received a significant amount of theoretical attention in previous decades and remains the fundamental tool used for learning probability distributions over discrete variables. Despite its large popularity in statistical mechanics and high-dimensional statistics, the majorit…

• A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems

  Maryam Ahang, Todd Charter, Masoud Jalayer, Homayoun Najjaran · 14. April 2026

  Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial condition monitoring. This work develops a hybrid condition monitoring framework that integrates primary sensor measurements, lagged temporal features, and ph…

• Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detection

  Jennifer Werner, Justus Arweiler, Indra Jungjohann, Jochen Schmid, Fabian Jirasek, Hans Hasse, Michael Bortz · 13. April 2026

  Anomaly detection (AD) in chemical processes based on deep learning offers significant opportunities but requires large, diverse, and well-annotated training datasets that are rarely available from industrial operations. In a recent work, we introduced a large, fully annotated experimental dataset f…

• Piecewise Deterministic Markov Processes for Bayesian Neural Networks

  Ethan Goan, Dimitri Perrin, Kerrie Mengersen, Clinton Fookes · 7. April 2026

  Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior. Traditional MCMC approaches avoid these assumptions at the cost of increased computation due to its incompatibility to s…

• Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing

  Ramona Rubini, Siavash Khodakarami, Aniruddha Bora, George Em Karniadakis, Michele Dassisti · 7. April 2026

  Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consistency required in regulated environments. To bridge this gap, we introduce Process-Informed Forecasting (PIF) models for t…

• Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)

  Sze Chai Leung, Di Zhou, H. Jane Bae · 7. April 2026

  Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose a machine-learning-based feature attribution (FA) framework to identify OSP for target predictions. FA quantifies input contributions to a model output; howev…

• Extended Hybrid Timed Petri Nets with Semi-Supervised Anomaly Detection for Switched Systems, Modelling and Fault Detection

  Fatiha Hamdi, Abdelhafid Zeroual, Fouzi Harrou · 7. April 2026

  Hybrid physical systems combine continuous and discrete dynamics, which can be simultaneously affected by faults. Conventional fault detection methods often treat these dynamics separately, limiting their ability to capture interacting fault patterns. This paper proposes a unified fault detection fr…

• Amortized Inference of Causal Models via Conditional Fixed-Point Iterations

  Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon · 6. April 2026

  Structural Causal Models (SCMs) offer a principled framework to reason about interventions and support out-of-distribution generalization, which are key goals in scientific discovery. However, the task of learning SCMs from observed data poses formidable challenges, and often requires training a sep…

• Regularizing Extrapolation in Causal Inference

  David Arbour, Harsh Parikh, Bijan Niknam, Elizabeth Stuart, Kara Rudolph, Avi Feller · 2. April 2026

  Many common estimators in machine learning and causal inference are linear smoothers, where the prediction is a weighted average of the training outcomes. Some estimators, such as ordinary least squares and kernel ridge regression, allow for arbitrarily negative weights, which improve feature imbala…

• Code Comprehension then Auditing for Unsupervised LLM Evaluation

  Bhrij Patel, Souradip Chakraborty, Mengdi Wang, Dinesh Manocha, Amrit Singh Bedi · 2. April 2026

  Large Language Models (LLMs) for unsupervised code correctness evaluation have recently gained attention because they can judge if code runs as intended without requiring reference implementations or unit tests, which may be unavailable, sparse, or unreliable. However, most prior approaches conditio…

• An Information-Theoretic Approach to Understanding Transformers' In-Context Learning of Variable-Order Markov Chains

  Ruida Zhou, Chao Tian, Suhas Diggavi · 1. April 2026

  We study transformers' in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared to fixed-order Markov chains (FOMCs), learning VOMCs is substantially more challenging due to the additional structural…

• Transformers learn variable-order Markov chains in-context

  Ruida Zhou, Chao Tian, Suhas Diggavi · 31. März 2026

  We study transformers' in-context learning of variable-length Markov chains (VOMCs), focusing on the finite-sample accuracy as the number of in-context examples increases. Compared to fixed-order Markov chains (FOMCs), learning VOMCs is substantially more challenging due to the additional structural…

• Density Ratio-based Proxy Causal Learning Without Density Ratios

  Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu, Arthur Gretton · 27. März 2026

  We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accomplish this task using two proxy variables related to the latent confounder: a treatment proxy (related to the treatment)…

• Density Ratio-Free Doubly Robust Proxy Causal Learning

  Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier, Liyuan Xu, Arthur Gretton · 27. März 2026

  We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. Two main approaches have been proposed: outcome bridge-based and treatment bridge-based methods. In this work, we propose…

• Federated Learning for Data-Driven Feedforward Control: A Case Study on Vehicle Lateral Dynamics

  Jakob Weber, Markus Gurtner, Benedikt Alt, Adrian Trachte, Andreas Kugi · 25. März 2026

  In many control systems, tracking accuracy can be enhanced by combining (data-driven) feedforward (FF) control with feedback (FB) control. However, designing effective data-driven FF controllers typically requires large amounts of high-quality data and a dedicated design-of-experiment process. In pr…