P5 - Explanatory predictive inference for the maintenance process using a deep learning approach
- Event
- iCCC2024 - iCampµs Cottbus Conference
2024-05-14 - 2024-05-16
Cottbus - Band
- Poster
- Chapter
- Condition Monitoring
- Author(s)
- D. Szarek, A. Wylomanska - Wroclaw University of Technology,Wroclaw (Poland), I. Jablonski - Brandenburgische Technische Universität Cottbus-Senftenberg, Cottbus
- Pages
- 132 - 132
- DOI
- 10.5162/iCCC2024/P5
- ISBN
- 978-3-910600-00-3
- Price
- free
Abstract
Monitoring and management of a complex production system is an engineering and business challenge. The concept of Industry 4.0 requires process automation up to a “zero touch” level, where machines autonomously realize production, monitoring and management actions. This triggers development of sensing technology capable of providing useful information about the system state and processes governing its operation. The knowledge retrieved from raw data can be further enhanced with dedicated algorithms, including deterministic, statistical and artificial intelligence (AI)/machine learning (ML) approaches. The content of data and the efficiency of algorithms enable the exploration of more and more complex signals and realization of not only a post-factum analysis, but also predictive and/or prescriptive inference. All these paves the way for organization of self-aware and automated technical systems, robust in terms of production and self-maintenance...