Human knowledge centered maintenance decision support in digital twin environment

Abstract

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.