Fault Detection and Control Systems

Neueste Paper

• Multivariate Bayesian Last Layer for Regression with Uncertainty Quantification and Decomposition

  Han Wang, Eiji Kawasaki, Guillaume Damblin, Geoffrey Daniel · 2. Februar 2026

  We present new Bayesian Last Layer neural network models in the setting of multivariate regression under heteroscedastic noise, and propose EM algorithms for parameter learning. Bayesian modeling of a neural network's final layer has the attractive property of uncertainty quantification with a singl…

• State Estimation Using Sparse DEIM and Recurrent Neural Networks

  Mohammad Farazmand · 2. Februar 2026

  Sparse Discrete Empirical Interpolation Method (S-DEIM) was recently proposed for state estimation in dynamical systems when only a sparse subset of the state variables can be observed. The S-DEIM estimate involves a kernel vector whose optimal value is inferred through a data assimilation algorithm…

• Characteristic Root Analysis and Regularization for Linear Time Series Forecasting

  Zheng Wang, Kaixuan Zhang, Wanfang Chen, Xiaonan Lu, Longyuan Li, Tobias Schlagenhauf · 28. Januar 2026

  Time series forecasting remains a critical challenge across numerous domains, yet the effectiveness of complex models often varies unpredictably across datasets. Recent studies highlight the surprising competitiveness of simple linear models, suggesting that their robustness and interpretability war…

• SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback

  Yaoning Yu, Ye Yu, Peiyan Zhang, Kai Wei, Haojing Luo, Haohan Wang · 27. Januar 2026

  Prompt quality plays a critical role in the performance of large language models (LLMs), motivating a growing body of work on prompt optimization. Most existing methods optimize prompts over a fixed dataset, assuming static input distributions and offering limited support for iterative improvement. …

• Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation

  Ye Su, Hezhe Qiao, Wei Huang, Lin Chen · 23. Januar 2026

  Semi-supervised regression (SSR), which aims to predict continuous scores of samples while reducing reliance on a large amount of labeled data, has recently received considerable attention across various applications, including computer vision, natural language processing, and audio and medical anal…

• Machine Learning-Based Framework for Real Time Detection and Early Prediction of Control Valve Stiction in Industrial Control Systems

  Natthapong Promsricha, Chotirawee Chatpattanasiri, Nuttavut Kerdgongsup, Stavroula Balabani · 21. Januar 2026

  Control valve stiction, a friction that prevents smooth valve movement, is a common fault in industrial process systems that causes instability, equipment wear, and higher maintenance costs. Many plants still operate with conventional valves that lack real time monitoring, making early predictions c…

• A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing

  Junn Yong Loo, Hwa Hui Tew, Fang Yu Leong, Ze Yang Ding, Vishnu Monn Baskaran, Chee-Ming Ting, Chee Pin Tan · 21. Januar 2026

  Industrial soft sensing is crucial for accurate process monitoring through reliable inference of dominant sensor variables. However, developing effective data-driven soft sensor models presents challenges, such as achieving domain adaptability, addressing incomplete sensor labels, and learning stoch…

• Causal feature selection framework for stable soft sensor modeling based on time-delayed cross mapping

  Shi-Shun Chen, Xiao-Yang Li, Enrico Zio · 21. Januar 2026

  Soft sensor modeling plays a crucial role in process monitoring. Causal feature selection can enhance the performance of soft sensor models in industrial applications. However, existing methods ignore two critical characteristics of industrial processes. Firstly, causal relationships between variabl…

• Uncovering Systemic and Environment Errors in Autonomous Systems Using Differential Testing

  Yashwanthi Anand, Rahil P Mehta, Manish Motwani, Sandhya Saisubramanian · 16. Januar 2026

  When an autonomous agent behaves undesirably, including failure to complete a task, it can be difficult to determine whether the behavior is due to a systemic agent error, such as flaws in the model or policy, or an environment error, where a task is inherently infeasible under a given environment c…

• Deep Learning for Continuous-Time Stochastic Control with Jumps

  Patrick Cheridito, Jean-Loup Dupret, Donatien Hainaut · 16. Januar 2026

  In this paper, we introduce a model-based deep-learning approach to solve finite-horizon continuous-time stochastic control problems with jumps. We iteratively train two neural networks: one to represent the optimal policy and the other to approximate the value function. Leveraging a continuous-time…

• Region of interest detection for efficient aortic segmentation

  Loris Giordano, Ine Dirks, Tom Lenaerts, Jef Vandemeulebroucke · 14. Januar 2026

  Thoracic aortic dissection and aneurysms are the most lethal diseases of the aorta. The major hindrance to treatment lies in the accurate analysis of the medical images. More particularly, aortic segmentation of the 3D image is often tedious and difficult. Deep-learning-based segmentation models are…

• Human-in-the-Loop Feature Selection Using Interpretable Kolmogorov-Arnold Network-based Double Deep Q-Network

  Md Abrar Jahin, M. F. Mridha, Nilanjan Dey, Md. Jakir Hossen · 9. Januar 2026

  Feature selection is critical for improving the performance and interpretability of machine learning models, particularly in high-dimensional spaces where complex feature interactions can reduce accuracy and increase computational demands. Existing approaches often rely on static feature subsets or …

• Causal Invariance Learning via Efficient Nonconvex Optimization

  Zhenyu Wang, Yifan Hu, Peter B\"uhlmann, Zijian Guo · 8. Januar 2026

  Identifying the causal relationship among variables from observational data is an important yet challenging task. This work focuses on identifying the direct causes of an outcome and estimating their magnitude, i.e., learning the causal outcome model. Data from multiple environments provide valuable…

• DeepFilter: A Transformer-style Framework for Accurate and Efficient Process Monitoring

  Hao Wang, Zhichao Chen, Licheng Pan, Xiaoyu Jiang, Yichen Song, Qunshan He, Xinggao Liu · 6. Januar 2026

  The process monitoring task is characterized by stringent demands for accuracy and efficiency. Current transformer-based methods, characterized by self-attention for temporal fusion, exhibit limitations in accurately understanding the semantic context and efficiently processing monitoring logs, rend…

• Multi-output Classification using a Cross-talk Architecture for Compound Fault Diagnosis of Motors in Partially Labeled Condition

  Wonjun Yi, Wonho Jung, Hyeonuk Nam, Kangmin Jang, Yong-Hwa Park · 6. Januar 2026

  The increasing complexity of rotating machinery and the diversity of operating conditions, such as rotating speed and varying torques, have amplified the challenges in fault diagnosis in scenarios requiring domain adaptation, particularly involving compound faults. This study addresses these challen…

• Myopically Verifiable Probabilistic Certificates for Safe Control and Learning

  Zhuoyuan Wang, Haoming Jing, Christian Kurniawan, Albert Chern, Yorie Nakahira · 1. Januar 2026

  This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments, set invariance-based methods that restrict the probability of risk events in infinitesimal time intervals may exhibit s…

• A Unified View of Optimal Kernel Hypothesis Testing

  Antonin Schrab · 30. Dezember 2025

  This paper provides a unifying view of optimal kernel hypothesis testing across the MMD two-sample, HSIC independence, and KSD goodness-of-fit frameworks. Minimax optimal separation rates in the kernel and $L^2$ metrics are presented, with two adaptive kernel selection methods (kernel pooling and ag…

• Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation

  Luobin Cui, Yanlai Wu, Tang Ying, Weikai Li · 30. Dezember 2025

  Fatigue detection for human operators plays a key role in safety critical applications such as aviation, mining, and long haul transport. While numerous studies have demonstrated the effectiveness of high fidelity sensors in controlled laboratory environments, their performance often degrades when p…

• Optimal Model Selection for Conformalized Robust Optimization

  Yajie Bao, Yang Hu, Haojie Ren, Peng Zhao, Changliang Zou · 25. Dezember 2025

  In decision-making under uncertainty, Contextual Robust Optimization (CRO) provides reliability by minimizing the worst-case decision loss over a prediction set. While recent advances use conformal prediction to construct prediction sets for machine learning models, the downstream decisions critical…

• On Agnostic PAC Learning in the Small Error Regime

  Julian Asilis, Mikael M{\o}ller H{\o}gsgaard, Grigoris Velegkas · 22. Dezember 2025

  Binary classification in the classic PAC model exhibits a curious phenomenon: Empirical Risk Minimization (ERM) learners are suboptimal in the realizable case yet optimal in the agnostic case. Roughly speaking, this owes itself to the fact that non-realizable distributions $\mathcal{D}$ are simply m…

• Quantifying Uncertainty in the Presence of Distribution Shifts

  Yuli Slavutsky, David M. Blei · 22. Dezember 2025

  Neural networks make accurate predictions but often fail to provide reliable uncertainty estimates, especially under covariate distribution shifts between training and testing. To address this problem, we propose a Bayesian framework for uncertainty estimation that explicitly accounts for covariate …

• Context-Driven Performance Modeling for Causal Inference Operators on Neural Processing Units

  Neelesh Gupta, Rakshith Jayanth, Dhruv Parikh, Viktor Prasanna · 18. Dezember 2025

  The proliferation of large language models has driven demand for long-context inference on resource-constrained edge platforms. However, deploying these models on Neural Processing Units (NPUs) presents significant challenges due to architectural mismatch: the quadratic complexity of standard attent…

• Group-robust Machine Unlearning

  Thomas De Min, Subhankar Roy, St\'ephane Lathuili\`ere, Elisa Ricci, Massimiliano Mancini · 17. Dezember 2025

  Machine unlearning is an emerging paradigm to remove the influence of specific training data (i.e., the forget set) from a model while preserving its knowledge of the rest of the data (i.e., the retain set). Previous approaches assume the forget data to be uniformly distributed from all training dat…

• Industrial AI Robustness Card: Evaluating and Monitoring Time Series Models

  Alexander Windmann, Benedikt Stratmann, Mariya Lyashenko, Oliver Niggemann · 16. Dezember 2025

  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 (IARC), a lightweight, task agnostic protocol for documenting and evaluating the robustness …

• Safely Learning Controlled Stochastic Dynamics

  Luc Brogat-Motte, Alessandro Rudi, Riccardo Bonalli · 15. Dezember 2025

  We address the problem of safely learning controlled stochastic dynamics from discrete-time trajectory observations, ensuring system trajectories remain within predefined safe regions during both training and deployment. Safety-critical constraints of this kind are crucial in applications such as au…