KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. (April 2022)
- Record Type:
- Journal Article
- Title:
- KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0. (April 2022)
- Main Title:
- KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
- Authors:
- Cao, Qiushi
Zanni-Merk, Cecilia
Samet, Ahmed
Reich, Christoph
Beuvron, François de Bertrand de
Beckmann, Arnold
Giannetti, Cinzia - Abstract:
- Abstract: In the context of Industry 4.0, smart factories use advanced sensing and data analytic technologies to understand and monitor the manufacturing processes. To enhance production efficiency and reliability, statistical Artificial Intelligence (AI) technologies such as machine learning and data mining are used to detect and predict potential anomalies within manufacturing processes. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. This brings the semantic gap issue which stands for the lack of interoperability among different manufacturing systems. Furthermore, as the Cyber-Physical Systems (CPS) are becoming more knowledge-intensive, uniform knowledge representation of physical resources and real-time reasoning capabilities for analytic tasks are needed to automate the decision-making processes for these systems. These requirements highlight the potential of using symbolic AI for predictive maintenance. To automate and facilitate predictive analytics in Industry 4.0, in this paper, we present a novel Knowledge-based System for Predictive Maintenance in Industry 4.0 (KSPMI). KSPMI is developed based on a novel hybrid approach that leverages both statistical and symbolic AI technologies. The hybrid approach involves using statistical AI technologies such as machine learning and chronicle mining (a special type of sequential pattern mining approach) to extract machineAbstract: In the context of Industry 4.0, smart factories use advanced sensing and data analytic technologies to understand and monitor the manufacturing processes. To enhance production efficiency and reliability, statistical Artificial Intelligence (AI) technologies such as machine learning and data mining are used to detect and predict potential anomalies within manufacturing processes. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. This brings the semantic gap issue which stands for the lack of interoperability among different manufacturing systems. Furthermore, as the Cyber-Physical Systems (CPS) are becoming more knowledge-intensive, uniform knowledge representation of physical resources and real-time reasoning capabilities for analytic tasks are needed to automate the decision-making processes for these systems. These requirements highlight the potential of using symbolic AI for predictive maintenance. To automate and facilitate predictive analytics in Industry 4.0, in this paper, we present a novel Knowledge-based System for Predictive Maintenance in Industry 4.0 (KSPMI). KSPMI is developed based on a novel hybrid approach that leverages both statistical and symbolic AI technologies. The hybrid approach involves using statistical AI technologies such as machine learning and chronicle mining (a special type of sequential pattern mining approach) to extract machine degradation models from industrial data. On the other hand, symbolic AI technologies, especially domain ontologies and logic rules, will use the extracted chronicle patterns to query and reason on system input data with rich domain and contextual knowledge. This hybrid approach uses Semantic Web Rule Language (SWRL) rules generated from chronicle patterns together with domain ontologies to perform ontology reasoning, which enables the automatic detection of machinery anomalies and the prediction of future events' occurrence. KSPMI is evaluated and tested on both real-world and synthetic data sets. Highlights: A novel hybrid approach combining statistical and symbolic AI for predictive maintenance is proposed. KSPMI is a novel software for Industry 4.0 predictive maintenance. KSPMI is evaluated and tested on both real-world and synthetic data sets. … (more)
- Is Part Of:
- Robotics and computer-integrated manufacturing. Volume 74(2022)
- Journal:
- Robotics and computer-integrated manufacturing
- Issue:
- Volume 74(2022)
- Issue Display:
- Volume 74, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 74
- Issue:
- 2022
- Issue Sort Value:
- 2022-0074-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-04
- Subjects:
- Industry 4.0 -- Predictive maintenance -- Knowledge-based system -- Chronicle mining -- Ontology reasoning
Robots, Industrial -- Periodicals
Computer integrated manufacturing systems -- Periodicals
Robotics -- Periodicals
Robots industriels -- Périodiques
Productique -- Périodiques
Robotique -- Périodiques
670.285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07365845 ↗
http://www.elsevier.com/journals ↗
http://www.journals.elsevier.com/robotics-and-computer-integrated-manufacturing/ ↗ - DOI:
- 10.1016/j.rcim.2021.102281 ↗
- Languages:
- English
- ISSNs:
- 0736-5845
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8000.453200
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20075.xml