Human knowledge centered maintenance decision support in digital twin environment
Résumé
The transition to Industry 4.0 has improved factories by improving
the manufacturing process. With increasing automation, awareness of
the role of humans in industrial maintenance management is also
important for the realization of the Industrial Internet of Things
(IIoT). Today’s smart factories use data from various sources for
extraction of valuable insights to improve manufacturing processes
and avoid failures. These improvements also add to the complexity
of resolving different maintenance issues faced during the
manufacturing process. There is a need to leverage untapped human
knowledge in Maintenance Work Orders (MWOs) to handle these complex
challenges using state-of-the-art Natural Language Processing (NLP)
techniques. The development of Industry 4.0 technologies is leading
to a growing interest in using digital twins in many sectors.
Digital twin-based services are revolutionizing design,
manufacturing, product use, and maintenance (diagnosis, prognosis,
and decision-making). This paper proposes a human knowledge
centered intelligent maintenance decision support. The proposed
service can find solutions to new maintenance problems using
knowledge in past maintenance records in a digital twin
environment. The architecture of the proposed service and its
connections with Physical Space (PS), Virtual Space (VS) and
Digital Twin Data (DTD) are presented in this paper. The
performance of the service is validated using a case study on an
open-source dataset of real MWOs from mining excavators. Results
indicate that state-of-the-art NLP techniques can be used to
process human knowledge in MWOs and generates interesting patterns.
This study is also a step forward towards application of Technical
Language Processing (TLP) in a smart manufacturing setup.