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Digital twins exemplify a crucial application in modern industries by forecasting equipment maintenance requirements. Through ongoing data collection from the physical asset and the utilization of predictive analytics and machine learning, they enable the anticipation of potential failures or breakdowns, guaranteeing timely interventions.
Rather than merely responding to equipment failures, digital twins empower manufacturers to foresee them. Through constant monitoring of equipment conditions via sensors, potential problems can be detected and resolved proactively, thereby reducing downtime.
