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A methodology for synthetic generation of failure data for data-driven prognostics and health management modeling for digital twins

A methodology for synthetic generation of failure data for data-driven prognostics and health management modeling for digital twins Abstract: Prognostics and Health Management (PHM) is one of the main services encompassed by Industry 4.0. However, the scarcity of...

A Systematic Evaluation of Machine Learning Approaches for Petroleum Production Forecasting

A Systematic Evaluation of Machine Learning Approaches for Petroleum Production Forecasting Abstract: Accurate production forecasting from oil well is an important decision-making variable for the Oil & Gas industry. Conventionally, future production is estimated...

Comparing ARIMA and LSTM models to predict time series in the oil industry

Comparing ARIMA and LSTM models to predict time series in the oil industry (in Portuguese) Abstract: Monitoring and forecasting oil and gas (O&G) production is essential to extend the life of a well and increase reservoirs’ productivity. Popular models for...

Recent Posts

  • Ontological modeling of events based on the notion of systems
  • Ontology-Enhanced Deep Learning Framework for Anomaly Detection in Oil and Gas Production Plants
  • An Ontological Approach to Sensor and Informational Data in Production Plants
  • Event modeling for supporting reasoning of consequences
  • Improving interoperability on industrial standards through ontologies

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