Equipment electrocardiogram (EECG): making intelligent production line more robust.

The simultaneous regulation of production efficiency and equipment maintenance in intelligent production lines poses a challenging problem. Existing approaches addressing this issue often separate the regulation of equipment maintenance and load balancing, lacking dynamic indicators to characterize the operational status and equipment workload. Inspired by the cardiac electrical activity recorded from human electrocardiogram (ECG), the electric drive signal of the equipment is proposed as an analogous measure to monitor equipment performance and workload variations. Thereby, the implementation mechanism and working characteristics of equipment ECG (EECG) are put forward for reconfigurable mixed-model assembly. Moreover, the monitoring of equipment performance based on deep learning is explored, leveraging the EECG features combined with multi-source heterogeneous data. The variations of equipment workload are tracked through the construction of a population difference hash analysis of the ECG data flow, along with the characterization of equipment power through electric signals. Additionally, an EECG-driven synchronous mapping approach is proposed to address steady disturbance, considering both workload imbalance and the degeneracy effect of the equipment. The reconfigurability of the intelligent production line enables the proposed mechanism of similarity matching of EECG features through the reconfiguration of the software manufacturing system and hardware physical equipment. Finally, the EECG-based solution is validated on a laboratory-level prototype platform, demonstrating that the robust running of the assembly process can be ensured even in the presence of internal and external disturbances. [ABSTRACT FROM AUTHOR]