A continuously running AI model watches sensor streams, work order history, and usage patterns - and surfaces failure risk before equipment goes offline.
Reactive maintenance is the most expensive kind. Equipment fails at the worst time - during peak production, on a remote site, in the middle of a contracted job. The repair costs are real, but the downtime cost is worse. Predictive maintenance replaces the "fix it when it breaks" model with continuous monitoring that flags failure risk weeks before a machine goes down.
TMI's predictive maintenance infrastructure combines sensor telemetry, work order history, and operational patterns into a continuously running model that ranks failure probability across the entire fleet. Not simple threshold alerts that fire too often to be useful - pattern recognition that surfaces which machines are trending toward failure and which maintenance actions will prevent it. The result is a maintenance program that runs on actual equipment condition, not a calendar.
Temperature, vibration, pressure, runtime hours, and operational load data collected continuously from every connected asset. Not sampled at intervals - live. Anomalies detected in the signal before they become symptoms.
The AI model watches patterns across the entire asset fleet - not just individual sensor thresholds. It correlates work order history, usage intensity, and failure patterns from similar equipment to rank failure probability by consequence, not just probability.
When failure risk reaches a defined threshold, a maintenance work order is generated automatically and scheduled for the next available service window. Maintenance happens on actual equipment condition - not a calendar that ignores how hard a machine has actually been working.
Two systems that work together - structured maintenance intervals enforced automatically, and AI that goes beyond intervals to surface failure risk from operational data.
Set maintenance intervals by runtime hours, mileage, or calendar date. The system schedules the work, notifies the crew, and closes the loop when it's done. Equipment stops failing by surprise.
A continuously running model that watches sensor streams, work order history, and usage patterns to surface equipment failure risk before failure happens. Not threshold alerts. Pattern recognition across the entire asset fleet, ranked by consequence, not just probability.
Predictive maintenance ROI is highest when equipment failure stops production, creates safety hazards, halts a fleet, or takes down the systems a business runs on - wherever that equipment lives.
Pump failures, compressor outages, and pipeline equipment breakdowns on remote sites cost production time and trigger emergency response. Predictive monitoring catches the signal weeks before the failure - scheduled maintenance at a planned outage window instead of an emergency crew at 2am.
Excavators, cranes, and heavy machinery taken offline mid-project cause cascading schedule failures. Predictive maintenance identifies the machines at risk before they stop a job. Planned downtime is always cheaper than unplanned downtime.
Production line equipment monitored continuously. Failure patterns identified across similar machines before individual units fail. OEE improves when downtime is planned, parts are staged, and repair windows are predictable instead of random.
Haul trucks, conveyors, and processing equipment operating in extreme conditions. Sensor data analyzed against failure history to rank which machines are trending toward failure. Maintenance scheduled in planned shift gaps, not forced emergency stops.
Grid infrastructure, pumping stations, generation equipment, and building or data-center systems monitored for early failure signals. Condition-based maintenance replaces rigid PM schedules that over-service some equipment and under-service the machines actually showing stress.
We'll map your current maintenance program, identify the highest-risk equipment, and show you what predictive monitoring looks like for your asset base.
FAQ
The system monitors equipment data streams continuously - temperature, vibration, pressure, runtime hours, power consumption - and identifies patterns that precede failure. When equipment is trending toward a problem, an alert fires with the confidence level and estimated time to failure, ranked by consequence of that failure on operations.
Preventive maintenance runs on a calendar schedule - service every X hours or X months. This wastes on equipment that is fine and misses failures that don't follow the calendar. Predictive maintenance triggers from actual equipment data. If a bearing is failing at 400 hours instead of the scheduled 600, the alert fires at 400. If a component is healthy at 600, it doesn't trigger unnecessary maintenance.
Studies consistently show 30-50% cost reduction in maintenance spending with predictive vs. reactive approaches. Emergency repairs typically cost 3-5x more than planned maintenance for the same work. Unplanned downtime in manufacturing or fleet operations adds significant additional cost on top of the repair itself.
The system connects to IoT sensors on equipment, SCADA systems, maintenance history databases, inspection records, and manufacturer data on failure modes. The more data sources connected, the more accurate the failure predictions. TMI builds the data infrastructure as part of the implementation.
Not all equipment failures are equal. A failed primary pump at an oil field costs differently than a failed service vehicle. The system ranks alerts by probability of failure AND consequence of that failure - production downtime cost, safety impact, customer impact. Operations teams see ranked priorities, not undifferentiated alert lists.
Predictive maintenance implementation depends on the number of assets being monitored and the complexity of the data infrastructure. A focused implementation on a specific equipment fleet typically takes 8-16 weeks. TMI stays in after launch to refine failure models as more data accumulates.
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