Predictive Maintenance
This application area focuses on predicting equipment and asset failures before they occur so maintenance can be performed proactively rather than reactively or on fixed time intervals. In transportation, it is applied to vehicle fleets, commercial transportation assets, and railway infrastructure by continuously monitoring condition, usage, and performance signals, then turning them into early‑warning alerts and optimized maintenance plans. It matters because unplanned breakdowns cause service disruptions, safety risks, costly emergency repairs, and under‑utilized assets. By forecasting failures in advance, organizations can schedule maintenance during planned downtime, align parts and labor, extend asset life, and reduce total cost of ownership. AI and advanced analytics improve prediction accuracy over traditional rule‑based approaches, enabling more reliable operations, higher asset availability, and better customer service levels across transportation networks.
The Problem
“Unplanned fleet breakdowns are killing availability—and your maintenance plan is blind to failure ri”
Organizations face these key challenges:
Breakdowns happen mid-route, triggering towing, service delays, penalties, and customer churn
Maintenance is either reactive (too late) or time-based (too early), wasting parts, labor, and asset life
Too many low-quality alerts from simple thresholds—teams ignore them until something actually fails