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.
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.
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.
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.
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.
Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance…
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.
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.
Survival models predict component failures using neural networks and resampled data.
problem Accurately predicting component failure times for maintenance planning.
method Neural network-based survival models trained on non-independent, homogeneously sampled data.
result Random resampling during training reduces dataset size and improves efficiency.
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.
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.
Parking, matching, scheduling, and routing are common problems in train maintenance. In particular, train units are commonly maintained and cleaned at dedicated shunting yards. The planning problem that results from such situations is referred to as the Train Unit Shunting Problem (TUSP). This problem involves matching…
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…
The increasing penetration level of energy generation from renewable sources is demanding for more accurate and reliable forecasting tools to support classic power grid operations (e.g., unit commitment, electricity market clearing or maintenance planning). For this purpose, many physical models have been employed, and…
Bayesian method models multivalued power data from wind farms.
problem Accurate modeling of power curves with multivalued relationships.
method Overlapping mixture of probabilistic regression models.
result Model accurately represents practical power data.
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.
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.
Large speech dataset for commercial use with 9.98% word error rate.
problem Creating a diverse speech recognition dataset for commercial purposes.
method Internet search for licensed audio data with transcriptions, training model on the dataset.
result Model trained on dataset achieves 9.98% word error rate on Librispeech's test-clean test set.
In modern building infrastructures, the chance to devise adaptive and unsupervised data-driven health monitoring systems is gaining in popularity due to the large availability of big data from low-cost sensors with communication capabilities and advanced modeling tools such as Deep Learning. The main purpose of this pa…
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%.
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.
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…
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.
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
Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in In…
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.
Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this wa…
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.
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.
Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilu…
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.
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an …
Aircraft engine manufacturers collect large amount of engine related data during flights. These data are used to detect anomalies in the engines in order to help companies optimize their maintenance costs. This article introduces and studies a generic methodology that allows one to build automatic early signs of anomal…
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. Th…