Predictive maintenance
From reactive to predictive in a machinery rental fleet
How to move from repairing breakdowns to preventing them: which data to capture, which alerts to activate and how to measure operational results.
By Equipo B'Smart7 min read
Most rental fleets still operate reactively: a machine fails at the customer site and the cost starts from there. Moving to a predictive model begins by deciding which critical conditions must be detected before they stop the machine.
Capture real machine condition
Use CAN BUS when the machine exposes it and direct sensors when it does not. The goal is not more data, but enough reliable signal to detect deviations from normal behaviour.
Prioritise costly failures
DPF saturation, excess temperature, fuel shortages, genset overloads and poorly managed traction batteries account for avoidable cost.
Measure operational outcomes
Avoided technician trips, availability and billable machine hours make the improvement visible to the business.