PREMISES, a Scalable Data-Driven Service to Predict Alarms in Slowly-Degrading Multi-Cycle Industrial Processes
| Autor: | Alberto Macii, Daniele Apiletti, Tania Cerquitelli, Enrico Macii, Elena Baralis, Stefano Proto, Francesco Ventura |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Service (systems architecture) Industry 4.0 Computer science business.industry media_common.quotation_subject Big data Predictive Maintenance 020206 networking & telecommunications 02 engineering and technology Industrial Big Data Predictive maintenance Failure prognostics 020901 industrial engineering & automation Software deployment Industrial Machine Learning Spark (mathematics) Scalability 0202 electrical engineering electronic engineering information engineering Systems engineering Quality (business) business media_common |
| Zdroj: | BigData Congress |
| DOI: | 10.1109/bigdatacongress.2019.00032 |
| Popis: | In recent years, the number of industry-4.0-enabled manufacturing sites has been continuously growing, and both the quantity and variety of signals and data collected in plants are increasing at an unprecedented rate. At the same time, the demand of Big Data processing platforms and analytical tools tailored to manufacturing environments has become more and more prominent. Manufacturing companies are collecting huge amounts of information during the production process through a plethora of sensors and networks. To extract value and actionable knowledge from such precious repositories, suitable data-driven approaches are required. They are expected to improve the production processes by reducing maintenance costs, reliably predicting equipment failures, and avoiding quality degradation. To this aim, Machine Learning techniques tailored for predictive maintenance analysis have been adopted in PREMISES (PREdictive Maintenance service for Industrial procesSES), an innovative framework providing a scalable Big Data service able to predict alarming conditions in slowly-degrading processes characterized by cyclic procedures. PREMISES has been experimentally tested and validated on a real industrial use case, resulting efficient and effective in predicting alarms. The framework has been designed to address the main Big Data and industrial requirements, by being developed on a solid and scalable processing framework, Apache Spark, and supporting the deployment on modularized containers, specifically upon the Docker technology stack. |
| Databáze: | OpenAIRE |
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