The client operated on a fixed preventive maintenance schedule — machines were serviced at set intervals regardless of actual condition, resulting in unnecessary downtime on healthy machines and missed failures on degrading ones. Sensor data from vibration monitors, temperature probes, and runtime meters was collected but never analysed beyond simple threshold alarms. When a machine failed unexpectedly, the production impact was severe — with no early warning, there was no time to pre-stage parts or reschedule production. Energy consumption spiked when machines ran outside their scheduled windows but there was no visibility into when or why. The maintenance team was reactive, not proactive.
We built an AI monitoring platform that continuously ingests sensor telemetry — vibration, temperature, bearing wear index, and runtime hours — from every machine on the floor. A machine learning model trained on historical failure data scores each machine with a health index (0–100) and calculates failure probability within a rolling time window. When a machine's health score drops below threshold or its failure probability exceeds a configurable limit, the system generates a ranked alert and can automatically create a maintenance work order routed to the right technician. An energy forecasting module uses production schedules and machine load profiles to predict plant-level energy consumption seven days ahead, detecting anomalies where actual consumption deviates from forecast and quantifying the cost exposure.
Continuous AI health index (0–100) per machine derived from vibration, temperature, bearing wear, and runtime — updated every 5 seconds
ML model predicts failure probability within a rolling time window so maintenance can be scheduled before a breakdown occurs
Ranked alert feed sorted by urgency — critical, high, medium, low — with one-click work order creation and acknowledgement
Per-machine sensor panels showing live vibration, temperature, and bearing wear with 24h trend sparklines and threshold breach highlights
7-day plant energy consumption forecast with actual vs forecast comparison and anomaly detection with cost impact quantification
Full history of preventive, predictive, and corrective maintenance records with technician, duration, and outcome tracking
Continuously retrained model with accuracy tracking — adapts to changing machine conditions and ageing equipment profiles
Full dashboard accessible on any device — field technicians receive alerts and update work orders without returning to a workstation
See it in action
Explore the interactive mockup — live data, full navigation, all modules.