AI-powered predictive maintenance uses sensor data, machine learning, and equipment history to identify failure precursors days or weeks before they cause downtime — enabling planned maintenance that minimizes disruption and eliminates the emergency repair costs that scheduled maintenance can't prevent.
Traditional maintenance strategies — run-to-failure and time-based scheduled maintenance — both produce excess downtime and cost. Predictive maintenance, enabled by AI, targets maintenance interventions at the moment they're needed — preventing failures before they occur without over-maintaining equipment that doesn't need service.
Every engagement follows a structured process — from discovery and vendor evaluation to pilot design and scale — adapted to the specific constraints and maturity of your organization.
We establish your current maintenance cost baseline — planned maintenance cost, unplanned downtime frequency and cost, emergency repair spend — and model the ROI of predictive maintenance against your specific failure modes.
Predictive maintenance requires sensor data from the equipment being monitored — vibration, temperature, current draw, acoustic emissions, and others. We assess existing sensor coverage and design the instrumentation additions needed for each target failure mode.
We evaluate predictive maintenance platforms — Samsara, SparkCognition, C3.ai, Aspentech, GE Digital Predix, and others — against your equipment types, sensor data sources, and CMMS integration requirements.
Predictive models must be trained and validated on your equipment's specific failure history and operating conditions. We design the model development process that achieves production-grade prediction accuracy.
These are the evaluation dimensions that consistently separate successful deployments from expensive pilots that never reach production scale.
How far in advance does the platform predict failures? The longer the lead time, the more flexibility maintenance teams have to plan interventions. Evaluate against your typical spare parts lead times and maintenance scheduling cycles.
Predictive maintenance that generates too many false alarms creates maintenance backlog without preventing real failures. Evaluate false positive rates and the organizational cost of acting on incorrect predictions.
Different failure modes require different sensor types and detection algorithms. Evaluate how comprehensively the platform covers the specific failure modes most costly in your operation.
Predictive alerts must create work orders automatically in your CMMS — Maximo, SAP PM, Infor, or others — with the diagnostic context technicians need to arrive with the right parts and tools.
For equipment without failure history, predictive models must generalize from physics-based models or similar equipment fleets. Evaluate cold start capability for new equipment and equipment that rarely fails.
Additional sensors are often required for comprehensive predictive coverage. Evaluate the full instrumentation cost — sensor hardware, installation, connectivity, data ingestion — as part of the total solution TCO.
"RLM brought structure to a process we didn't know how to start. They asked the right questions, surfaced the right vendors, and kept us from making decisions we would have regretted."
"What set RLM apart was that they didn't have a preferred answer. They evaluated our options honestly and told us what they actually thought."
Start with a no-cost conversation with an RLM AI advisor — vendor neutral, no agenda, just clarity.
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