🤖 AI Summary
Early-stage engineering design often neglects sensor integration, treating signal pathways and data flows as secondary considerations rather than core design dimensions. Method: This paper proposes a “sensor-native” Digital Systems Design (DSD) paradigm that intrinsically embeds sensing capabilities at the conceptual design stage. DSD holistically integrates multimodal sensor selection and placement optimization, embedded signal conditioning, edge–cloud collaborative data architecture, and real-time digital twin simulation. Unlike conventional “retrofitted sensing,” DSD treats the signal chain and data flow as first-class design variables, enabling end-to-end data闭环 across design, operation, and maintenance. Contribution/Results: Experiments demonstrate that DSD improves early-design predictability by over 40%, supports dynamic iterative optimization and predictive maintenance, and has been validated in mechanical systems and intelligent equipment education as well as prototype development.
📝 Abstract
This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.