Journal
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
Volume 74, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2021.102281
Keywords
Industry 4; 0; Predictive maintenance; Knowledge-based system; Chronicle mining; Ontology reasoning
Funding
- INTERREG Upper Rhine (European Regional Development Fund), Germany
- Ministries for Research of Baden-Wurttemberg, Rheinland-Pfalz (Germany)
- Grand Est French Region
- Engineering and Physical Sciences Research Council (EPSRC), United Kingdom [EPSRC EP/S018107/1]
- EPSRC, United Kingdom [EP/S001387/1]
- EPSRC [EP/S018107/1] Funding Source: UKRI
Ask authors/readers for more resources
In the context of Industry 4.0, smart factories utilize advanced technologies for production monitoring and analysis, but the heterogeneous nature of industrial data leads to complex knowledge extraction. In order to achieve predictive maintenance, symbolic AI technologies are required. KSPMI is a knowledge-based system developed based on a hybrid approach utilizing both statistical and symbolic AI technologies.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available