Think of this as a “health tracker and advisor” for bulldozers, excavators, and cranes. It watches how machines are used, learns patterns from past breakdowns, and then tells you the best time to maintain each piece of equipment so you fix problems before they become expensive failures.
Unplanned breakdowns of heavy construction equipment cause costly delays, expensive emergency repairs, and under‑utilized assets. This approach uses data from equipment usage, sensors, and maintenance history to optimize when and what maintenance should be done, reducing downtime and total lifecycle cost while improving asset reliability.
Proprietary operational and maintenance history data for specific fleets, plus domain-specific failure modes and cost models, can create a defensible optimization engine that is hard for generic vendors to replicate quickly.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Data quality and coverage across diverse machine types and operating conditions; integrating heterogeneous sensor/telematics feeds at scale.
Early Majority
Compared with generic predictive maintenance tools, this work is tailored to heavy construction equipment, optimizing not only failure prediction but also maintenance timing and policies under real operational and cost constraints, which can yield more actionable scheduling and fleet-level decisions.