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FictiveBox
AI & ML EngineeringSolution

Predictive Maintenance Engine

Anomaly detection and remaining-useful-life models wired into CMMS work orders for industrial fleets.

Problem

What we are solving

Operators react to failures instead of preventing them. Sensor data sits in historians while breakdowns drive overtime, parts AOG and SLA penalties on customer contracts.

Solution

How it works

A managed feature store fed by SCADA, OPC-UA and CAN telemetry, paired with anomaly detection and remaining-useful-life models trained per asset class. Outputs are pushed as scored work orders into Maximo, SAP PM or in-house CMMS, with operator feedback closing the retraining loop.

Use Cases

Where it ships

Rail

Bogie, traction and HVAC condition monitoring across mainline fleets to cut unscheduled withdrawals.

Manufacturing

CNC spindle and motor health scoring tied to MES so jobs are rerouted before the line stops.

Energy & Utilities

Transformer and substation asset health with thermal and DGA fusion to extend MTBF.

Stack

Tech stack

PyTorchMLflowKafkaTimescaleDBFastAPIAirflowMaximo API
What you get

Engagement deliverables

Architecture and reference implementation tailored to your environment
Integration into your existing systems of record and action
Production hardening: observability, SLOs, runbooks and on-call
Knowledge transfer and optional managed operations

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