Knowledge reuse for ontology modelling and application of maintenance motion state sequence

Abstract

With the current digital transformation and the development of complex manufacturing systems, advanced maintenance is proposed to improve the competitiveness of complex products, generating a large amount of heterogeneous maintenance data and information. There is a lack of standardized representations of motion-centred maintenance knowledge which leads to semantic ambiguity and poor intertranslatability. In addition, it causes subjective deviations and human resource investments in related time prediction applications. Therefore, a knowledge reuse method for ontology modelling and the application of maintenance motion state sequences is proposed. First, a framework for reusing maintenance motion state sequence (MMSS) knowledge is established, which is defined as the state sets of time-sequence maintenance motion. Second, maintenance motion state sequence ontology (MMSSO) is constructed to standardize the definition of MMSS, as a supplement to the current maintenance ontologies. Third, an MMSSO application for automatic maintenance time prediction is proposed by incorporating the standardized specifications of MMSSO and improving the MODAPTS method. Finally, using aviation equipment as an example, the rationality and superiority of MMSSO in real applications are verified. MMSSO is a new practice of integrating multi-source information in advanced maintenance. It can also provide predicted time as an iterative reference for industrial practitioners in the digital design stage.