Imagine your construction vehicles and heavy machines could “tell you” when they’re about to break, days or weeks before it happens. This system listens to their sensor data (vibrations, temperatures, usage hours), learns patterns of normal vs. failing behavior, and then recommends the best time to service each machine so you avoid costly breakdowns on the job site.
Unplanned equipment failures on construction sites cause project delays, idle labor, emergency repair costs, and safety risks. This solution uses data and AI to predict failures in advance and optimize maintenance schedules across a fleet, reducing downtime and maintenance spend while improving asset utilization.
Proprietary historical sensor and maintenance data from a specific fleet, integrated into existing construction operations and CMMS/ERP workflows, can create a defensible feedback loop that improves models over time and makes the system sticky.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
High-frequency sensor streams from many machines increase storage and compute needs; as fleets scale, training and scoring time-series models and integrating real-time alerts into operations can become a bottleneck.
Early Majority
Applied specifically to heavy construction equipment and job-site conditions, where duty cycles, environment (dust, load, shocks), and usage patterns differ from factory or fixed-plant predictive maintenance; optimization is tuned for construction project schedules and fleet logistics rather than generic industrial settings.
7 use cases in this application