Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring of Critical Transmission Components in Smart Production Line

In response to the lack of a unified cyber–physical system framework, which combined Internet Things, industrial big data, and deep learning algorithms for condition monitoring critical transmission components in smart production line. this study, based on conceptualization layers, novel five-layer systems framework lines is proposed. This architecture integrates physics data-driven. The connection layer collects transmits physical equation modeling converts low-value raw data into high-value feature information via signal processing, machine realizes prediction through algorithm, scientific decision-making predictive maintenance are completed cognition configuration layer. Case studies three components—spindles, bearings, gears—are carried out validate effectiveness proposed hybrid model monitoring. results datasets show that successful distinguishing condition, while short time Fourier transform processing residual network algorithm superior other models. approach scalable generalizable lay foundation extension model.