01
Too much data, not enough clarity
Operators already collect huge volumes of turbine data. The challenge is turning it into a small number of reliable, prioritised actions.
AI-powered wind intelligence
Predict failures before they stop your turbines.
mimilix continuously analyses SCADA, sensor and maintenance data to identify early warning signals, prioritise risk and help wind farm teams act before a minor anomaly becomes costly downtime.
Built for operators, asset managers and maintenance teams.
Fleet health overview
Live monitoring
North Sea Cluster · 48 turbines
Gearbox temperature drift detected
Turbine WT-18 is deviating from its operating baseline under comparable load.
High priority
Fleet health
92%
Active anomalies
7
Critical alerts
2
Designed to fit modern wind operations
SCADA
CMMS
IoT Sensors
Weather Data
Work Orders
The operational challenge
Most failures start quietly.
The first signals are often hidden across thousands of tags, changing weather conditions and fragmented maintenance history. Static alarms react too late. mimilix is built to surface the pattern earlier.
02
Alarm thresholds miss gradual degradation
Components can move away from their normal operating behaviour without crossing a fixed alarm limit. Our models compare each turbine against its own baseline.
03
Maintenance decisions compete for attention
Risk scoring helps teams focus on the assets and components that need investigation first, while lower-risk deviations remain under observation.
One monitoring layer for the entire fleet
From raw signals to confident maintenance decisions.
mimilix combines machine learning, engineering logic and operational context in a single system built around the needs of wind farm teams.
Continuous anomaly detection
Detect deviations in temperature, vibration, pressure, power curves and other operating signals while accounting for load, seasonality and weather conditions.
Component-level health scoring
Track health indicators for gearboxes, bearings, generators, converters, pitch systems and other critical subsystems.
Failure risk forecasting
Estimate how quickly an anomaly is developing and support earlier inspection, parts planning and maintenance scheduling.
Actionable alert context
Every alert includes the affected signals, model confidence, operating context and suggested diagnostic steps.
Fleet-wide learning
Compare similar turbines and operating regimes to identify recurring failure patterns while preserving asset-specific baselines.
Monitoring samples
See what predictive monitoring looks like in practice.
Below are example views showing how the platform surfaces abnormal behaviour, tracks component health and visualises live turbine telemetry in a way that is easy for operations and maintenance teams to act on.
Sample monitor · SCADA analytics
Real-time stream
Trend monitor with animated signal overlays
Compare active power, wind speed and gearbox temperature across the same time window to detect subtle divergence before a hard alarm threshold is reached.
Power
Wind speed
Gearbox temp
Anomaly score
0.81
Deviation duration
37 min
Recommended action
Inspect WT-18
Sample monitor · Health score
Model active
Component health gauge
Roll multiple signals into one interpretable health score while still showing the underlying confidence and short-term signal movement.
Gearbox health
84 / 100
Sample monitor · Alert triage
Priority queue
Risk-ranked anomaly feed
Triage issues by severity, persistence and likely operational impact so teams can focus attention where it matters first.
WT-18 · Gearbox temperature drift
Persistent deviation at comparable wind regime
Critical
WT-07 · Bearing vibration increase
Pattern emerging over last 3 operating windows
Warning
WT-31 · Converter efficiency drop
Observe and compare against next production cycle
Observe
Sample monitor · Digital twin view
Telemetry live
Animated turbine telemetry snapshot
Visualise several turbines at once with rotating blades, live telemetry chips and signal links to show how the monitoring layer maps across an entire farm.
WT-04 97%
WT-12 84%
WT-18 Alert
How it works
Connect once. Learn continuously.
We start with the data you already have and build a monitoring layer that evolves with your fleet and maintenance feedback.
STEP 01
Connect your data
Ingest SCADA, CMS, weather, alarms, work orders and maintenance records securely.
STEP 02
Build operating baselines
Model normal behaviour by turbine, component, load range and environmental condition.
STEP 03
Detect and prioritise
Score anomalies by severity, persistence, confidence and likely operational impact.
STEP 04
Improve with feedback
Use engineer validation and maintenance outcomes to continuously refine the system.
Business outcomes
More uptime. Better planning. Less uncertainty.
Predictive monitoring is not another dashboard. It is a decision-support layer that helps teams move from reactive maintenance to planned intervention.
✓Reduce exposure to unexpected component failures
✓Prioritise inspections based on risk rather than alarm volume
✓Prepare labour, vessels, cranes and spare parts earlier
✓Capture engineering knowledge across the fleet
Cloud or private deployment
API-first architecture
Role-based access
Audit-ready events
Start with one fleet or one failure mode
Turn your turbine data into earlier decisions.
Tell us about your fleet, available data and the failures you want to detect. We will outline a focused pilot with clear technical scope and measurable success criteria.
✦ Technical discovery and data assessment
✦ Pilot scope built around your highest-value use case
✦ Transparent model validation with your engineers