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The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification. [ABSTRACT FROM AUTHOR]

Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise. [ABSTRACT FROM AUTHOR]