Predictive Maintenance

Follow this topic Ask about this topic

Predictive maintenance studies how to use sensor readings, operating conditions, and maintenance records to detect faults, track equipment health, and estimate remaining useful life before failure. Common approaches include signal processing and statistical models, machine-learning classifiers, recurrent and Transformer-based sequence models, and physics-informed or survival models; newer work also explores self-supervision, foundation models, and federated learning when labels or shared data are limited. These forecasts can feed maintenance scheduling, risk assessment, and digital-twin or edge-monitoring systems, where uncertainty and decision costs matter alongside predictive accuracy. A central challenge is whether models and benchmarks remain reliable across machines, sites, failure modes, and noisy operating conditions, especially when data are scarce and evaluation protocols can hide leakage or poor calibration.

Latest papers

118 papers9 papers in the last four weeks, down 25% on the four weeks before. 0.1% of all new papers.

PriMa-Causa: Causal Prescriptive Maintenance
Oct 22, 2026Felix SaretzkyCausal Inference Causal Effect Estimation
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance
Oct 10, 2026Prince Kwabena Appiah Boadu, Jeffery Baafi Kyei, Enoch Kofi Koranteng +1Edge Inference Industrial Internet of Things
Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Sera: Semantic Representation Aggregation with Knowledge and LLM for Reliable and Interpretable Battery Health Forecasting
Oct 8, 2026Jiawei Li, Fang Liu, Wei Zhang +3Semantic Representation Learning Time Series Forecasting
Infocomm Technology Cluster, Singapore Institute of Technology, Singapore · School of Science and Technology, Singapore University of Social Sciences, Singapore · College of Smart Energy, Shanghai Jiao Tong University, Shanghai, China +2
Explainable Failure Prediction and Prevention in Maritime
Oct 6, 2026Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis +1Predictive Maintenance Explainable Artificial Intelligence
Dept. of Computer Science and Biomedical Informatics, University of Thessaly
Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics
Oct 5, 2026Abhishek Srinivasan, Juan Carlos Andresen, Sepideh Pashami +1RUL Estimation Predictive Maintenance
Traton AB, Södertälje, Sweden · Halmstad University, Halmstad, Sweden · RISE Research Institutes of Sweden AB, Stockholm, Sweden +1
Do RUL explanations hold up? Faithfulness and stability of attributions on C-MAPSS
Oct 3, 2026Manh Hien Nguyen, Ngoc Thanh Nguyen, Isabella Mendoza Cortes +5Feature Attribution Explanation Stability
AI Lab, Phuong Hai JSC, Vietnam · International College of Management Sydney, Australia · School of Science, Engineering, and Technology, RMIT, Vietnam +1
Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction
Oct 1, 2026Tugrul Cabir Hakyemez, Ener Uras GokhanRUL Estimation Multi-Objective Evolutionary Optimization
Department of Management Information Systems Istanbul Bilgi University Istanbul, Turkey · Beştepe Koleji Ankara, Turkey
CORD: Learning Reusable Degradation Representations Across Heterogeneous Physical Systems
Sep 30, 2026Haibo Li, Zhiguo ZengRepresentation Learning Self-Supervised Time Series Representation Learning
CentraleSupélec, Université Paris-Saclay
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
Sep 29, 2026Marco Veneriano, Ani Gjergji, Sebastiano Bellani +3Time Series Forecasting Predictive Maintenance
MaLGa Center Universit`a degli Studi di Genova Genoa, Italy · Antares Electrolysis S.r.l. Genoa, Italy
Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
Sep 24, 2026Giacomo Arcieri, Gregory Duthé, Christophe Muller +3Predictive Maintenance Multi-Agent Reinforcement Learning
Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, 8093, Switzerland · Department of Statistics, University of Oxford, Oxford, United Kingdom · Dept. of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, 16802, USA +1
When Labels Are Scarce: An Oscillatory State Space Model for Vibration Diagnosis
Sep 23, 2026Mainak Mallick, Seung-Kyum ChoiPredictive Maintenance Tiny ML
Georgia Institute of Technology
Explainable Neuro-Fuzzy Prediction for Trustworthy Decision-Making in Maritime
Sep 21, 2026Dionisis Kalogeropoulos, Georgia Sovatzidi, Dimitris K. IakovidisExplainable Artificial Intelligence Predictive Maintenance
Dept of Computer Science and Biomedical Informatics University of Thessaly Lamia, Greece
Explainable Predictive Condition-based Maintenance of Naval-Propulsion Systems using Fuzzy Logic
Sep 21, 2026Dionisis Kalogeropoulos, Georgia Sovatzidi, Panagiotis G. Kalozoumis +1Predictive Maintenance Decision Tree Learning
Department of Computer Science and Biomedical Informatics, University of Thessaly
Benchmarking Hybrid Deep Learning Architectures for Predictive Maintenance in Industry 4.0
Sep 18, 2026Zhengyang, Gu, Joseph E. Hernandez +4Hybrid Transformer Architectures Predictive Maintenance
Cissy · Data Science, Liveline Technologies Livonia, MI, USA · Engineering, Liveline Technologies Livonia, MI, USA
FreqCondNorm: Towards Cross-domain Predictive Maintenance through a Frequency-Conditioned Transformer Foundation Model
Sep 17, 2026Zaynab Raounak, Camille LHermine, Zhiguo ZengMulti-Source Domain Adaptation Self-Supervised Time Series Representation Learning
Laboratoire Génie Industriel, CentraleSupélec, Université Paris-Saclay, France
Serverless gossip training of LSTM failure detectors: A matched-protocol comparison with federated, local and centralized learning on NASA C-MAPSS
Sep 17, 2026Yusuf Öztürk, Enes Göktekin, Bengisu Atlı +3Decentralized Learning Fault Detection
Department of Electrical and Electronics Engineering, Antalya Bilim University, Antalya, Türkiye · Department of Computer Engineering, Antalya Bilim University, Antalya, Türkiye · Graduate School of Natural and Applied Sciences, Ankara University, Ankara, Türkiye +1
Planning or Learning: Reliability and Cost in Multi-Asset Maintenance
Sep 11, 2026Xian Yeow Lee, Chandrasekar Venkatraman, Ahmed FarahatPredictive Maintenance Model-Based Planning
Industrial AI Lab, Hitachi America Ltd. Santa Clara, U.S.A.
Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems
Sep 7, 2026David J Poland, Daniele Ravi, Na HelianQuantile Regression Predictive Maintenance
Department of Computer Science, University of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom
Long Horizon Transformer Quantile Fault Prediction for Multi Site Industrial Predictive Maintenance
Sep 4, 2026David J Poland, Daniele Ravi, Na HelianQuantile Regression Multivariate Time Series Classification
Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, U.K.
A Synthetic Multivariate Refrigerator Time-Series Dataset for Predictive Maintenance
Sep 3, 2026Islam Benamirouche, Feriel Fass, Djemel ZiouSynthetic Data Generation Time Series Generation
Département d’informatique, Université de Sherbrooke, Sherbrooke, QC, Canada
Geometric Attractor Monitoring: A Robust and Frugal Framework for Multi-modal Industrial Robotic Cycles
Aug 31, 2026Martin Bonsergent-Brachet, Jesse Read, Dany AbboudRobot Failure Detection Industrial Anomaly Detection
LIX, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France · Renault, France
PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics
Aug 28, 2026Sara Sameer, Yunyi Zhao, Wei Zhang +4Predictive Maintenance Physics-Informed ML
Information and Communications Technology Cluster, Singapore Institute of Technology, Singapore 138683
A Deep Learning-Based Stacking Ensemble Framework for Turbofan Engine Remaining Useful Life Prediction
Aug 28, 2026Limon Bin Hossain, Md. Salehin Seyam, Md Rashedul Islam +2Stacked Ensemble Learning RUL Estimation
Department of Industrial and Production Engineering Bangladesh University of Engineering and Technology Dhaka 1000, Bangladesh · Department of Industrial Engineering Clemson University Clemson, SC, USA · College of Engineering and Science Louisiana Tech University Ruston, LA 71270, USA +1
FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation
Aug 26, 2026Amelia Sorrenti, Matteo Pennisi, Concetto Spampinato +1Federated Learning RUL Estimation
University of Catania, Italy
Uncertainty and Business-Aware Remaining Useful Life Estimation for Semiconductor Manufacturing
Aug 26, 2026Davide Frizzo, Francesco Borsatti, Gian Antonio SustoRUL Estimation Predictive Maintenance
Department of Information Engineering, University of Padova
Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling
Aug 19, 2026Artur Sokolovsky, Bhavik Merai, Moe Jafari +1Power Systems Predictive Maintenance
SA Power Networks, 1 Anzac Highway, Keswick SA, Adelaide, 5035, Australia
Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity
Aug 17, 2026Jiaqi Yao, Julia KowalLithium-Ion Batteries Data-Efficient Learning
Department of Electrical Energy Storage Technology (EET), Technische Universität Berlin, Einsteinufer 11, 10587, Berlin, Germany
Federated Learning for Distributed CNC Tool Wear Prediction
Aug 11, 2026Afsana Khan, Morris Stallmann, Marcin Pietrasik +2Manufacturing Predictive Maintenance
Maastricht University, Maastricht, Netherlands
Physics-Informed Machine Learning in Prognostics and Health Management: A Systematic Literature Review
Aug 10, 2026Christopher Braun, Julian Raible, Marco F. HuberPredictive Maintenance Physics-Informed ML
Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Allmandring 35, Stuttgart, 70569, Baden-Württemberg, Germany. · Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Nobelstraße 12, Stuttgart, 70569, Baden-Württemberg, Germany.
Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules
Aug 8, 2026Mattia Scarpa, Evgeny Kusmenko, Francesco Toso +3Domain Generalization Predictive Maintenance
Dept. of Information Engineering University of Padova Padova, Italy · Infineon Technologies Dresden AG & Co. KG Dresden, Germany · Newtwen S.r.l. Padova, Italy +1

Oct 22, 2026J/K move · Enter open · S save

PriMa-Causa: Causal Prescriptive Maintenance

Felix Saretzky

Causal Inference Causal Effect Estimation Causal Foundation Models Predictive Maintenance

Open paper AskPDF arXiv

Automated manufacturing requires data-driven decisions under uncertainty and sparse observational records, and unplanned downtime is a dominant cost of getting them wrong. Prescriptive maintenance recommends a maintenance action against an impending failure rather than merely predicting it. Established reference architectures couple predictive models with rule- or optimisation-based recommendation logic. Selecting a corrective action, however, requires an interventional quantity: how the line behaviour would change if the action were set. Manufacturing data is observational, so a model fitted to predict the outcome estimates an associational quantity and may misdirect the choice of action without the prediction error revealing it. Causal estimators target the interventional quantity but require an expert-led causal study per case, for which the bounded decision window of operation leaves no room. This dissertation analyses how causal reasoning can be embedded into prescriptive maintenance so that actions are ranked by their estimated interventional effect. The result is PriMa-Causa, built from three components: a causal reasoning layer that integrates the estimate into the PriMa reference model, a parametric simulator that supplies the interventional ground truth absent from manufacturing data, and a graph-conditioned tabular causal foundation model that, pre-trained once on this synthetic production-line prior, answers interventional queries in a single forward pass. PriMa-Causa attains a lower prescription error than associational baselines, with the largest margin under weak overlap and sparse context, and a partially disclosed causal graph reduces this error further. The causal inductive bias of the synthetic prior and the graph conditioning can thus replace per-case estimator fitting, a step toward automating causal inference for autonomous and self-healing maintenance.