Fault Detection and Control Systems

Latest papers

• Handling Missing Data in Probabilistic Regression Trees

  Taiane Schaedler Prass, Alisson Silva Neimaier, Guilherme Pumi · 7 August 2026

  Probabilistic Regression Trees (PRTrees) are a smooth and consistent alternative to classical regression trees, producing continuous predictions through probabilistic split assignments. This paper extends the PRTree framework to accommodate missing predictor values directly during tree construction,…

• OPERA: Operator-residual feedback for reliable autonomous optical experiments with language-model agents

  Ning Xu, Xiang Zheng, Fuqiang Zhong, Huadong Wang, Xiaolong Wu, Zhiyuan Liu, Hui Ning · 7 August 2026

  Autonomous agents choose actions using scores that may not reflect experimental success. We developed OPERA, an operator-residual framework for optical experiments. It represents experimental actions as optical operators and evaluates their outcomes using physically interpretable residuals. Operator…

• Quality Diversity for Reliable Data Driven Time-Use Optimization

  Aneta Neumann, Ty Stanford, Dorothea Dumuid, Frank Neumann · 7 August 2026

  The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can estimate the relationship between time-use compositions and health outcomes such as body mass index, life satisfaction, and cognition, most optimizat…

• Hybrid Probabilistic Zonotopes for Identifiable and Refinable Predictive Uncertainty

  Zhen Zhang, Amr Alanwar · 7 August 2026

  Probabilistic prediction heads in neural networks typically output either a Gaussian mixture or a single conformal region. Neither separates the distinct sources of uncertainty often present in real prediction tasks: a discrete choice among modes, bounded systematic drift within the chosen mode, and…

• Local Violation Certification for Linear Predict-Then-Optimize Pipelines

  \c{S}. \.Ilker Birbil, Wenhao Chi · 6 August 2026

  Data-driven decision pipelines combining predictive machine learning models with downstream optimization software are increasingly used to make high-stakes operational decisions. Certifying the safety, fairness, and reliability of these decisions is essential, yet traditional scenario generation met…

• When Proxy Prediction Becomes Equation Reconstruction: Diagnostics and Residual Learning for Factor-Derived Proxy Supervision

  Chayan Lahiri, Ahmed Shafee, Cody Fehringer · 6 August 2026

  Scientific machine learning often relies on proxy targets computed from known domain factors when direct observations are limited. When those same factors are used as model inputs, however, high predictive accuracy may reflect reconstruction of the proxy-generating equation rather than robustness to…

• Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model

  Yufei Wu, Shanqing Gao, Andreas Voss, Francis Tuerlinckx · 5 August 2026

  The drift diffusion model (DDM) is a cornerstone of cognitive decision-making research. Although numerous estimation methods exist, researchers continue to seek inference approaches that are both fast and flexible across diverse study designs. Amortized Bayesian inference (ABI) can provide nearly in…

• The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning

  Xizhe Zhang · 4 August 2026

  Machine-learning benchmarks often pair a label that aggregates a long temporal horizon with input observed through one or a few short windows. Their apparent performance ceiling may therefore be an acquisition-protocol ceiling rather than a model-capacity ceiling. We study labels of the form $\Theta…

• Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models

  Yibin Dong · 4 August 2026

  Stochastic world models are usually evaluated by the accuracy and calibration of their predicted futures. These criteria leave a decision-relevant ambiguity: the same conditional future distribution can arise because an observation aliases different physical states, or because the dynamics remain ra…

• BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series

  Zhenya Zhang, Wendi Zhu, Ping Wang, Hongmei Cheng, Shuguang Zhang · 4 August 2026

  In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and metric saturation. This paper proposes BayesSeg, a …

• Augmented Inverse Hybrid Weighting: Robust Inference under Deterministic and Random Distribution Shifts

  Ying Jin, Ying Jin, Dominik Rothenh\"ausler · 4 August 2026

  Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another. This strategy is well suited to deterministic, learnable covariate discrepancies, but can be insufficient when source--target po…

• Optimizing Transformer Neural Network for Real-Time Outlier Detection on FPGAs

  Ilia Sobakinskikh, Paul Alexander Bilokon · 28 July 2026

  In this work, we explore how the inference time of a Transformer Neural Network can be efficiently optimized with applications to real-time anomaly detection in financial time series. The financial time series are price series such as asset prices. Unfortunately, the data is often with errors or out…

• Agentic Root Cause Analysis through Evidence-Grounded Reasoning

  Amaury Wei, Olga Fink · 27 July 2026

  Diagnosing the root cause of anomalies is essential for safe industrial operation. Despite extensive sensor instrumentation, formulating hypotheses and gathering evidence remains a manual process, creating a major operational bottleneck. While existing data-driven approaches aim to automate this, tw…

• Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation

  Kun Fang, Qinghua Tao, Mingzhen He, Kexin Lv, Runze Yang, Haibo Hu, Xiaolin Huang, Jie Yang, Longbing Cao · 15 July 2026

  Out-of-Distribution (OoD) detection is vital for the reliability of deep neural networks, the key of which lies in effectively characterizing the disparities between OoD and In-Distribution (InD) data. In this work, such disparities are exploited through a fresh perspective of non-linear feature sub…

• Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

  Abhijeet Parida, Antonia Alomar, Zhifan Jiang, Pooneh Roshanitabrizi, Austin Tapp, Maria Ledesma-Carbayo, Ziyue Xu, Syed Muhammed Anwar, Marius George Linguraru, Holger R. Roth · 10 July 2026

  Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning. For histopathology, stain normalization techniques can mitigate discrepancies, but they often fall sh…

• Open-Ended Scenario Reasoning for Specialist Model Adaptation

  Youcheng Zong, Runda Jia, Ranmeng Lin, Mingxuan Ren, Dakuo He · 9 July 2026

  Process industries have accumulated validated specialist models, yet sensor drift, feedstock variation, and regime switching cause these models to degrade systematically in new scenarios. Collecting new labeled data and retraining is costly, while continuing with the original model incurs persistent…

• Model-agnostic Mitigation Strategies of Data Imbalance for Regression

  Jelke Wibbeke, Sebastian Rohjans, Andreas Rauh · 25 June 2026

  Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability. This is particularly detrimental in applications aimed at predicting rare events that fall outside of the domain of the bulk of the training data. In th…

• Safe Learning Control with Optimality and Stability Guarantees

  Xinyang Wang, Hongwei Zhang, Shimin Wang, Wei Xiao, Martin Guay · 25 June 2026

  Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to guarantee both safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performan…

• Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment

  Disha Patel · 24 June 2026

  On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardware demands careful tradeoffs between accuracy, latency, and model size. We present a benchmark comparing traditional ML methods (Random Forest, XGBoos…

• Probabilistic Analysis of Least Squares, Orthogonal Projection, and QR Factorization Algorithms Subject to Gaussian Noise

  Ali Lotfi, Julien Langou, Mohammad Meysami · 23 June 2026

  We consider the effect of Gaussian perturbations on least-squares residuals, orthogonal projections, and QR-type algorithms. The problem that motivated our investigations is as follows: suppose that a full column-rank matrix \(B\in\mathbb{R}^{m\times n}\) has already been computed, and suppose that …

• Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems

  Giancarlo Santamato, Andrea Mattia Garavagno, Massimiliano Solazzi, Antonio Frisoli · 19 June 2026

  Deep Transfer Learning (DTL) allows for the efficient building of Intelligent Fault Diagnosis Systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of labelled data. Obtaining such an amount of data can be challenging when dealing with machines or structures faults. This…

• Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors

  Oliver Hennh\"ofer, Christine Preisach · 15 June 2026

  The need for uncertainty quantification in anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates without inflating Type II error rates in these systems can build trust and reduce costs associated with false discoveries. The field of …

• Disentanglement-Based Equivariant Learning for Compositional VQA

  Zhou Du, Zhaoquan Yuan, Xiao Wu, Changsheng Xu · 2 June 2026

  Compositional visual question answering (VQA) represents a challenging yet fundamental task that requires models to comprehend novel combinations of previously learned concepts. The current methods often overlook the disentanglement of underlying concepts and are restricted in terms of their ability…

• Interventional Processes for Causal Uncertainty Quantification

  Hugh Dance, Peter Orbanz, Arthur Gretton · 2 June 2026

  Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar estimand. In this work, we introduce a GP-based approach for uncertainty quantification of interventional functions. The c…

• Technical note on Sequential Test-Time Adaptation via Martingale-Driven Fisher Prompting

  Behraj Khan, Tahir Qasim Syed · 1 June 2026

  We present a theoretical framework for M-FISHER, a method for sequential distribution shift detection and stable adaptation in streaming data. For detection, we construct an exponential martingale from non-conformity scores and apply Ville's inequality to obtain time-uniform guarantees on false alar…