Self-diagnosis service to support analysis of production performance, monitoring and optimisation activities

Self-diagnosis functionalities, as integral components of advanced manufacturing services within cyber–physical systems (CPSs), are made possible through cloud computing technologies and machine learning techniques. These services play a crucial role in enhancing the autonomy of CPSs and introducing cost-efficient and scalable solutions. Despite the promising outlook, a gap exists in the literature regarding the lack of clear architectural frameworks and requirements for implementing self-diagnosis services in industrial settings. This paper addresses this gap by presenting a comprehensive requirement set and developing a high-level architecture tailored for self-diagnosis services. The proposed approach is validated through a detailed case study of a cloud-based self-diagnosis service, demonstrating alignment with the established architecture and requirements. The anticipated outcome of this research is to offer concrete implementation guidelines to support researchers, engineers, and practitioners in deploying CPS-based self-diagnosis services and improving production processes and system performance. • An architecture and requirement set are proposed for self-diagnosis CPS. • Tiers (Core, Secondary and Advanced) are defined to support varying maturity levels. • A case study protocol is proposed to systematically evaluate self-diagnosis readiness. • The OptiTwin case study demonstrates readiness assessment in practice.