This work optimizes maintenance schedules using causal machine learning.
problem Challenges in machine maintenance, especially imperfect maintenance policies.
method Causal inference from observational data to learn maintenance effects.
result Novel approach accurately predicts maintenance effects and optimizes schedules.
Transformer model predicts train axle vibrations for safer maintenance.
problem Prevent mechanical failures in railway axles.
method Integrates Deep Autoregressive solution with spectral methods and observation models.
result Transformer model (ShaftFormer) improves predictive maintenance for railway axles.
Modeling coating degradation with fewer data points.
problem Efficiently forecasting material degradation from high-frequency sensor data.
method Discrete degradation events using Hawkes processes.
result Forecasting future coating failure with superior performance.
Detecting early signs of failures (anomalies) in complex systems is one of the main goal of preventive maintenance. It allows in particular to avoid actual failures by (re)scheduling maintenance operations in a way that optimizes maintenance costs. Aircraft engine health monitoring is one representative example of a fi…
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochast…
Mix-up domain adaptation improves dynamic RUL predictions across various conditions.
problem Dynamic RUL predictions under non-i.i.d conditions.
method Three-staged mechanism with mix-up strategy for source and target domains alignment, self-supervised learning.
result MDAN outperforms existing methods in 12 out of 12 cases for dynamic RUL predictions.
Model predicts VKA dosage for Indian patients, aiding in safe medication.
problem Safe and accurate dosing of VKA drugs for Indian patients.
method Support Vector Machine (SVM) Regression model trained on patient data.
result Predicted dosages closely match actual dosages.
The paper proposes an AI and IIoT framework for improved maintenance.
problem Current maintenance practices need improvement with AI and IIoT.
method Review of reliability modeling, introduction of Intelligent Maintenance framework, and novel probabilistic deep learning approach.
result Demonstrated novel probabilistic deep learning reliability modelling in Turbofan Engine Degradation Dataset.
In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model…
Study analyzes cost-benefit of CBM for unmanned systems.
problem Determining ROI for CBM strategies in unmanned systems.
method MDFTA with MCS for assessing maintenance requirements.
result Different CBM strategies can significantly impact ROI.
Deep RL predicts equipment maintenance from sensor data.
problem Equipment downtime due to sensor data overload.
method Model-free Deep Reinforcement Learning for optimal maintenance policy.
result Automatic maintenance policy learning from sensor data.
Deep learning improves oilfield equipment maintenance and reduces downtime.
problem Predicting equipment failure in oilrigs to minimize downtime.
method Developed and tested neural networks on oilfield datasets, using data processing and feature extraction.
result Deep learning can predict oilfield equipment failure with reduced downtime.
Automated suggestions help train technicians diagnose incidents faster.
problem Manual and time-consuming incident diagnosis by train maintenance technicians.
method Developed and deployed a learning machine to suggest diagnostics to technicians.
result The model refines its accuracy through feedback from experts and uses feature engineering.
For predictive maintenance, we examine one of the largest public datasets for machine failures derived along with their corresponding precursors as error rates, historical part replacements, and sensor inputs. To simplify the time and accuracy comparison between 27 different algorithms, we treat the imbalance between n…
Paper predicts bearing degradation stages for pharmaceutical industry maintenance.
problem Predicting when to maintain specific parts of production machines.
method AutoEncoder-based k-means segmentation of high-frequency vibration data.
result Framework generates reliable predictions for bearing degradation stages.
We use a control framework to analyze the digital vendor's profit maximization problem. The vendor captures market share by focusing costly effort on post-launch product maintenance, which influences user perception of the product and drives a revenue stream associated with product use. Our theoretical results show nec…
This paper enhances the Random Survival Forest model for better predictive maintenance.
problem Improving time-to-failure estimation in predictive maintenance with Random Survival Forest.
method A three-level framework for quantifying hyperparameter tunability, including model-level and hyperparameter-level metrics.
result Hyperparameter tuning significantly improves Random Survival Forest model performance in predictive maintenance.
Optimizes train schedules and maintenance using CP and QA.
problem Optimizing train schedules and maintenance considering constraints.
method Used Constraint Programming and Quantum Annealing to model and solve the problem.
result Both CP and QA approaches produce comparable results on real quantum computers.
Bayesian model predicts crack evolution on rails with uncertainties.
problem Predicting crack evolution on railways due to complex interactions and uncertainties.
method Robust Bayesian multi-horizon approach with constraints.
result Trade-off between prediction accuracy and constraint compliance.
The paper develops methods for monitoring TPL machine health.
problem Inaccurate and untimely maintenance of TPL systems leads to poor quality and inefficiencies.
method Physics-informed data-driven predictive models integrated with statistical approaches.
result The methods achieve high accuracy across various scenarios and conditions.
AOI algorithm improved predictive maintenance accuracy.
problem Improving predictive maintenance accuracy in industrial assets.
method Extended AOI algorithm with LSTM for anomaly detection and RUL calculation.
result Methodology accurately detected anomalies and calculated RUL with high precision.
New algorithm detects changes quickly without knowing parameters, near optimally.
problem Quickest change detection with unknown parameters.
method Leverages theoretical asymptotic properties to derive a scalable approximate algorithm with near optimal performance.
result Detects changes in constant complexity with near optimal performance.
SUPAID automates vehicle rollout decisions for fleet managers.
problem Automating supervisor's vehicle rollout decisions to prevent costly mistakes.
method Rule mining approach based on 'one-way efficiency' assumption.
result SUPAID significantly reduces costs in a real transit agency setting.
New measures quantify uncertainty in survival models for maintenance tasks.
problem Uncertainty in survival models for maintenance tasks.
method Formal measures of ambiguity, discrepancy, and obscurity introduced.
result Multiple accurate survival models may yield conflicting risk estimates.
Generative model predicts vehicle faults up to 1000 hours in advance.
problem Forecasting vehicle faults for predictive maintenance.
method Generative model trained on US Army data, incorporating real-world factors.
result Highly accurate predictions of time to first fault.
RAGuard improves safety in LLMs for offshore wind maintenance.
problem Conventional LLMs fail with specialised or unexpected scenarios in offshore wind maintenance.
method Integrates safety-critical documents alongside technical manuals in RAG framework.
result RAGuard increases safety recall from almost 0% to over 50% while maintaining technical recall above 60%.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
New method detects anomalies in computing centers' logs.
problem Anomaly detection in continuously changing log data for predictive maintenance.
method Evolving granular classifiers using Fuzzy-set-Based evolving Modeling and evolving Granular Neural Network.
result Classification model prioritizes maintenance based on anomaly severity.
Many post-disaster and -conflict regions do not have sufficient data on their transportation infrastructure assets, hindering both mobility and reconstruction. In particular, as the number of aging and deteriorating bridges increase, it is necessary to quantify their load characteristics in order to inform maintenance …
A stochastic model helps maintain insufficiently funded pension funds.
problem Maintaining pension funds that are underfunded and require external financing.
method A time-homogeneous diffusion process with a barrier is used to model the unrestricted reserves value, and a renewal-reward process models the financing effort.
result Expected values and cost evaluations of maintenance are derived, and the approach is applied to a generalized Brownian motion process.
This work tackles maintenance planning with deep reinforcement learning under uncertainty.
problem Optimizing inspection and maintenance policies in deteriorating environments with incomplete information and constraints.
method Joint framework of constrained POMDPs and multi-agent DRL addressing challenges of state/action space, history, uncertainty, and constraints.
result The proposed framework outperforms existing methods in resource and risk-aware decision-making.
Quantum Annealing Enhanced Reinforcement Learning for Accurate RUL Prediction
problem RUL estimation in predictive maintenance
method QAQL framework combining quantum annealing and Q-learning
result Outperforms classical and quantum baselines
Bayesian deep learning improves maintenance planning uncertainty quantification.
problem Estimating the remaining useful life of physical systems with uncertainty quantification.
method Stein variational gradient descent for training Bayesian neural networks.
result Bayesian deep learning models trained via Stein variational gradient descent outperform other methods in convergence speed and predictive performance.
Masked LARk prevents cross-site tracking while training models.
problem Cross-site tracking of user data through third-party cookies.
method Secure multi-party compute (MPC) protocol with masking.
result Prevents cross-site tracking and maintains model training flexibility.
Paper uses ML to predict insulator flashover risk.
problem Predicting flashover risk of aging insulators.
method Supervised ML with XGBoost, using LC and voltage features.
result Model accurately estimates insulator flashover probability.
Prognostics or early detection of incipient faults is an important industrial challenge for condition-based and preventive maintenance. Physics-based approaches to modeling fault progression are infeasible due to multiple interacting components, uncontrolled environmental factors and observability constraints. Moreover…
WTNN models survival with neural networks for maintenance data.
problem Modeling survival with limited proxy data and censored observations.
method WTNN integrates neural networks with Weibull distribution for time-dependent covariates.
result WTNN produces robust survival predictions improving on existing methods.
Paper proposes a new KPI for early fault detection in hydropower plants.
problem Early detection and maintenance of faults in hydropower plants.
method Developed and tested a novel Key Performance Indicator (KPI).
result The KPI outperforms conventional multivariable process control charts.
RSM-GAN detects anomalies in noisy, multivariate, seasonal time series.
problem Anomaly detection in complex, noisy multivariate time series with seasonality.
method Convolutional-LSTM layers and attention mechanism in a GAN framework.
result RSM-GAN outperforms existing models in false positive rate and precision.
Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high ri…
Predict water pipe failures using machine learning and survival analysis.
problem Difficulty in accessing water pipes for maintenance.
method Classical and modern classifiers for short-term prediction, survival analysis for long-term forecast, and oversampling technique for imbalanced data.
result Identifies important risk factors for water pipe failures.
Predicting the remaining useful life of machinery, infrastructure, or other equipment can facilitate preemptive maintenance decisions, whereby a failure is prevented through timely repair or replacement. This allows for a better decision support by considering the anticipated time-to-failure and thus promises to reduce…
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned maintenance. One popular means of detecting combustor abnormalities is through continuously monitoring exhaust g…
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spa…
This paper proposes a multi-head attention model for predicting RUL in IIoT environments.
problem Estimating RUL for complex industrial equipment using IIoT data.
method Multi-Head Attention Mechanism combined with LSTM for multi-dimensional time-series data.
result The proposed model outperforms state-of-the-art models on benchmark datasets.
Neural system optimizes glucose levels in diabetics.
problem Limited research on continuous glucose maintenance devices.
method Differential predictive control with neural policy and differentiable modeling.
result Improves glucose level optimization in real-time.
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
problem Imbalanced binary classification tasks where minority class is underrepresented.
method DAMVI algorithm increases positive example weights and optimizes classifier weights using PAC-Bayesian C-Bound.
result DAMVI outperforms state-of-the-art models on various imbalanced datasets.
Discriminative classifiers improve decision-making in SHM systems.
problem Lack of descriptive labels for SHM data.
method Risk-based active learning with discriminative classifiers.
result Discriminative classifiers offer improved robustness and reduced inspection costs.