In power systems, an asset class is a group of power equipment that has the same function and shares similar electrical or mechanical characteristics. Predicting failures for different asset classes is critical for electric utilities towards developing cost-effective asset management strategies. Previously, physical ag…
This research improves asset life prediction by integrating deep learning with mixture distributions.
problem Predicting residual useful life for assets with multiple failure modes.
method Integrates mixture (log)-location-scale distribution with deep learning.
result Proposed models outperform existing methods in predicting residual useful life.
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.
Proposes a federated learning approach for industrial asset failure prediction.
problem Lack of data and privacy concerns in industrial prognostics.
method Two-stage federated learning: dimension reduction and parameter estimation.
result Validated the approach using simulated and real data.
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.
As economic entities become increasingly interconnected, a shock in a financial network can provoke significant cascading failures throughout the system. To study the systemic risk of financial systems, we create a bi-partite banking network model composed of banks and bank assets and propose a cascading failure model …
Complex non-linear interactions between banks and assets we model by two time-dependent Erdős Renyi network models where each node, representing bank, can invest either to a single asset (model I) or multiple assets (model II). We use dynamical network approach to evaluate the collective financial failure---systemic ri…
Model predicts Mozambique bank failures, aiding risk management.
problem Lack of bankruptcy prediction model in Mozambique banking sector.
method Linear Discriminant Analysis method, using financial indicators.
result Model accurately predicted 84% of bank failures 1 year before Central Bank intervention.
A simple banking network model is proposed which features multiple waves of bank defaults and is analytically solvable in the limiting case of an infinitely large homogeneous network. The model is a collection of nodes representing individual banks; associated with each node is a balance sheet consisting of assets and …
Extends CRR model with q-binomial random walks for asset pricing.
problem Asset pricing with time-varying probabilities and trend parameters.
method Introduces a q-binomial extension of the CRR model with non-self-similar binomial trees.
result Convergence to Black-Scholes formula with rate O(N^(-1/2)).
Economics tool predicts failure times in reliability systems.
problem Predicting optimal failure times in weighted k-out-of-n reliability systems with heterogeneous component failure.
method Using rational expectations to analyze and predict failure times in reliability systems with heterogeneous component failure.
result Different measures are optimal for predicting system failure depending on component failure distributions.
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
Paper proposes a method to predict disk failures using multi-layer domain adaptive learning.
problem Traditional machine learning models struggle to predict disk failures due to limited data.
method Multi-layer domain adaptive learning with source and target domains.
result The proposed method improves failure prediction accuracy on disk data with few failure samples.
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evo…
DeepSIP predicts network failures' impact using CNN from syslog and traffic data.
problem Predicting service impact from network failures.
method Temporal multimodal CNN for predicting time to recovery and traffic loss.
result DeepSIP reduced prediction error by approximately 50%.
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
problem Predicting remaining useful life of DSH subsystems with unknown failure modes.
method Unsupervised framework using mixture of Gaussian regressions and Expectation-Maximization algorithm.
result Improved prediction accuracy and interpretability of RUL.
RODMAN improves ML-based disk failure prediction accuracy in cloud environments.
problem Imperfect data quality in real-world cloud environments degrades ML-based disk failure prediction accuracy.
method RODMAN uses three data preprocessing techniques: failure-type filtering, spline-based data filling, and automated pre-failure backtracking.
result RODMAN significantly improves prediction accuracy compared to no preprocessing.
Risk Advisor predicts and mitigates ML deployment failures.
problem Predicting and mitigating test-time failure risks of ML systems.
method Post-hoc meta-learner for estimating failure risks and uncertainties.
result Reliably predicts deployment-time failure risks across various ML models.
In this paper, we consider a problem of failure prediction in the context of predictive maintenance applications. We present a new approach for rare failures prediction, based on a general methodology, which takes into account peculiar properties of technical systems. We illustrate the applicability of the method on th…
System predicts respiratory failure up to 8 hours early.
problem Early detection of respiratory failure in ICU patients.
method Machine learning on ICU patient monitoring data.
result System outperforms traditional clinical decision-making.
Study constructs balanced datasets for seismic failure prediction.
problem Imbalanced datasets limit machine learning performance in seismic failure prediction.
method Framework with three steps: GMF identification, probability density estimation, and sample transformation.
result Framework improves machine learning performance in seismic failure mode prediction.
Study predicts P2P lending platform failures using machine learning.
problem Predicting failures of P2P lending platforms in China.
method Used machine learning models with filter and wrapper methods, forward selection, and backward elimination.
result Identified robust variables for predicting platform failures with high AUC and F1 scores.
Proposes a deep neural network for early disk drive failure prediction.
problem Early prediction of disk drive failure using multivariate time series sensor data.
method Enriched features derived from sensor data through transformations, combined with ensemble learning and deep neural network architecture.
result Significantly improved classification accuracy in predicting disk drive failure.
Machine learning predicts failure in brittle materials with high accuracy.
problem Predicting failure in brittle materials under repetitive loads.
method Phase-field model combined with supervised machine learning.
result Framework predicts failure with acceptable accuracy even in noisy data.
Brain uses synaptic failure to sample from posterior distributions.
problem Bayesian inference in the brain's probabilistic computations.
method Adapting synaptic failure to sample posterior predictive distributions.
result Synaptic failure enables sampling of complete posterior predictive distributions.
Given key performance indicators collected with fine granularity as time series, our aim is to predict and explain failures in storage environments. Although explainable predictive modeling based on spiky telemetry data is key in many domains, current approaches cannot tackle this problem. Deep learning methods suitabl…
SSH-Net: A Deep Neural Network for Predicting Failure Time Distribution Functions under Competing Risks with GPU Data
problem Predicting failure time distribution functions under competing risks
method Structured Segmented Hazard Deep Neural Network (SSH-Net)
result Prediction accuracy validated through simulation studies and GPU data
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
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.
Cryptocurrency markets treat infrastructure failures and regulatory shocks differently, but the effect is not statistically significant.
problem Understanding how cryptocurrency markets differentiate between infrastructure failures and regulatory shocks.
method A multi-moment event study using GJR-GARCH-X model with matched dependence-robust inference.
result The differential impact of infrastructure failures and regulatory shocks on cryptocurrency markets is not statistically significant.
We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of…
Quantum crypto-economics models price risks in blockchain technology.
problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.
Predicting MRI coil failures using time series classification.
problem Early detection of MRI hardware failures to prevent downtime.
method Training a statistical model on sequential image data features over time.
result LSTMs achieved an F-score of 86.43% and 98.33% accuracy in predicting coil damage.
Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.
problem Cost-effective RUL prediction from limited, non-shared CM signals with unknown parametric forms.
method Joint modeling of nonlinear degradation signals and failure events using federated learning.
result Superior RUL prediction compared to alternatives, validated through simulations and real data.
Study predicts heart failure patient survival using stacked ensemble ML.
problem Predicting survival of heart failure patients.
method Collect and analyze patient data, apply SMOTE, use K-Means, Fuzzy C-Means clustering, Random Forest, XGBoost, Decision Tree, and propose a stacked ensemble model.
result Supervised ML algorithms outperform unsupervised models, achieving high accuracy and F1 score.
A trading system predicts stock prices using DNNs for Abercrombie & Fitch Co. shares.
problem Complexity and unpredictability of stock market prices.
method Feed-forward deep neural networks (DNNs) for price prediction, technical indicators for trade generation.
result Increased profitability with high Sharpe, Sortino, and Calmar ratios.
Predicting MRI coil failures using image features and ensemble learning.
problem Ensuring trouble-free operation of MRI systems by predicting hardware failures.
method Two-level data analysis with neural networks and Random Forest ensemble learning.
result Improved prediction results with an F-score of 94.14% and an accuracy of 99.09%.
This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequ…
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.
Interpretable survival analysis improves heart failure risk prediction.
problem Improving heart failure risk prediction using survival analysis.
method Survival stacking, ControlBurn, Explainable Boosting Machines.
result Achieves state-of-the-art performance and provides novel insights.
Active Kriging Monte Carlo simulation method with conformal certification for failure probability estimation
problem Failure probability estimation in structural reliability analysis
method Active learning framework with conformal prediction
result Improved uncertainty quantification and reliability of failure probability estimates
Using the trends of estimated abilities in terms of item response theory for online testing, we can predict the success/failure status for the final examination to each student at early stages in courses. In prediction, we applied the newly developed nearest neighbor method for determining the similarity of learning sk…
Entropy-based GP adaptive design improves failure probability estimation.
problem Limited accuracy in failure probability estimation due to model evaluation costs.
method Entropy-based Gaussian process (GP) adaptive design combined with multifidelity importance sampling (MFIS).
result More accurate failure probability estimates and higher confidence.
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations. Rise of machine learning products in safety-critical industries cause an increase in attention in evaluating model robustness and estimating failure probability in machine learni…
When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more …
Model shows how financial contagion spreads through complex interdependencies.
problem Understanding how banks fail in an interconnected financial system.
method Unified model combining direct and indirect dependencies; three reconstruction methods.
result Hierarchical cascades reveal dominant banks in failures.
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 …
Assessing reliably the confidence of a deep neural network and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP). We show how using the TCP is more suited…