Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Industry 4.0 and Sustainability: A Bibliometric Literature Review

(2021) Ana Teresa Tavares-Lehmann et al.   Sustainability

Crack Detection Zones: Computation and Validation

(2020) Simon Pfingstl et al.   SENSORS

Characterising the Digital Twin: A systematic literature review

(2020) David Jones et al.   CIRP Journal of Manufacturing Science and Technology

Online anomaly detection with sparse Gaussian processes

(2020) Minghao Gu et al.   NEUROCOMPUTING

SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes

(2018) Ravi Kumar Pandit et al.   IET Renewable Power Generation

An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach

(2018) Poorya Ghafoorpoor Yazdi et al.   Sustainability

An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission

(2017) S.A. Aye et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

(2016) Liansheng Liu et al.   SENSORS

Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

(2016) Lishuai Li et al.   TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES

A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data

(2016) Markus Goldstein et al.   PLoS One

Sequential Bayesian Prediction in the Presence of Changepoints and Faults

(2010) R. Garnett et al.   COMPUTER JOURNAL

Anomaly detection

(2009) Varun Chandola et al.   ACM COMPUTING SURVEYS

Technology readiness assessments: A retrospective

(2009) John C. Mankins   ACTA ASTRONAUTICA