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Predictive Maintenance and Machine Health Analytics

A live health score per machine from vibration, temperature, current and SCADA data; anomaly detection and remaining useful life (RUL) prediction that drive maintenance decisions before a failure occurs.

We tie every machine in the field to a measurable health score. We clean the data flowing from existing sensors and SCADA/PLC logs, learn each machine’s normal behavior and catch deviations without requiring labeled data. Physics-based fault classification (bearing, gearbox, misalignment, electrical) turns an alarm into a maintenance work order, while remaining useful life prediction lets you plan maintenance around the machine’s actual condition rather than the calendar. The result: unplanned downtime and spare parts inventory go down, and the maintenance team gets to the right machine at the right time.

Highlights

  • Live health score per machine (0–1)
  • Label-free anomaly detection — Isolation Forest, autoencoder
  • Physics-based fault classification and root-cause hints
  • Remaining useful life (RUL) and recommended maintenance window
  • CMMS / ERP work order integration
1–48 h
Warning window before failure
90%
Conformal prediction interval confidence
6
Physics-based fault categories
0
Labeled failure data required (at start)

Capabilities

  1. 01

    Data Acquisition and Cleaning

    Time series ingestion from OPC UA, Modbus, MQTT and SCADA exports; automatic repair of missing and faulty readings, and separation of downtime periods.

  2. 02

    Health Score and Anomaly Detection

    A “normal” envelope learned for each machine; deviations in the vibration spectrum, temperature gradient and load curve are reduced to a single health score, and thresholds are calibrated with operational experience.

  3. 03

    Fault Classification and RUL

    Bearing, gear, misalignment, imbalance and electrical fault categories; a remaining life estimate fitted to the degradation curve, with a 90% confidence interval.

  4. 04

    Maintenance Decision Loop

    Alarm → prioritization → CMMS/ERP work order → feedback. The technician’s findings are written back to the model; the system learns more with every maintenance job.

How we work

  1. Step 01

    Sensor and data inventory

    Which machine, which signal, which frequency? First data flow from your existing SCADA/PLC within 2 weeks.

  2. Step 02

    Learning normal behavior

    A per-machine baseline from 4–8 weeks of historical data; thresholds calibrated together with your operations team.

  3. Step 03

    Pilot and validation

    Live alarms on a selected line or turbine group; every alarm is checked against the technician’s findings.

  4. Step 04

    Rollout

    CMMS/ERP work order integration, dashboards, a retraining loop and SLA-backed monitoring.

Industries

  • Wind Energy
  • Production Lines
  • Pump & Compressor Fleets
  • HVAC & Facilities

Technology

  • Python / FastAPI
  • scikit-learn / LightGBM
  • TimescaleDB
  • OPC UA / MQTT
  • Grafana

Related projects

All →

Let’s look at your line or site together.

Book a discovery call[email protected]

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