AI-Powered Monitoring & Analysis Platform for Electrical Infrastructure — real-time monitoring, predictive analytics, and intelligent maintenance management.
MonCube is an end-to-end platform that connects IoT sensors on switchgears, cubicles, and transformers to AI-driven analytics — giving operators full visibility, predictive insights, and actionable maintenance workflows from a single dashboard.
Continuous surveillance of electrical infrastructure through IoT sensors — temperature, partial discharge, current, voltage, and more.
Machine learning models calculate Health, Critical, and Risk indexes to classify equipment condition and predict failures before they happen.
Shift from reactive to proactive maintenance — automated ticketing, mobile field operations, and SLA-driven resolution workflows.
Transform operational data into cost avoidance calculations, risk reports, and investment justification documentation.
Raw data from 9 sensor types is aggregated, processed by AI, and transformed into real-time dashboards and actionable alerts.
Four purpose-built views — each designed for a different operator, environment, and decision speed.
Real-time status per location with color-coded severity, trend graphs per parameter, and device condition classification. Operators see health indexes at a glance and drill into details instantly.
Dedicated large-screen mode built for control rooms and NOCs. Auto-refreshes data, auto-rotates between locations, and highlights critical alarms with prominent visual indicators.
Status overlays on a geographic map — drill down from a national overview to individual cubicles. Every point carries live health data and alarm state.
Compact monitoring on the go — field engineers receive push notifications for critical alarms, view cubicle status, and manage tickets from their mobile devices.
ML models calculate Health Index, Criticality Index, and Risk Index for every cubicle — automatically classifying equipment condition in real time.
The AI analyzes degradation trends and historical patterns to generate prioritized maintenance recommendations — telling operators what to fix, when, and why.
Partial discharge levels have increased 40% over the last 14 days. Pattern matches pre-failure signature from historical data.
Temperature difference between phases is increasing. Currently at ΔT 8°C, approaching the 10°C threshold.
3 units are approaching their scheduled maintenance cycle based on historical HI degradation patterns.
Ask questions in natural language — the AI retrieves data, analyzes trends, and provides contextual answers about any cubicle, region, or parameter.
Which cubicles need attention this week?
Based on current trends, Cubicle B-02 has PD levels approaching the warning threshold — I recommend scheduling an inspection. Cubicle A-02 shows a rising temperature delta (ΔT 8°C) that should be monitored closely.
What's the predicted failure timeline for B-02?
At the current degradation rate, HI has dropped from 72% → 38% over 14 days. Based on similar historical patterns, the estimated failure window is 30-45 days. Recommended action: immediate PD testing and thermography scan.
Generate a maintenance report for B-02
✅ Report generated. Includes: 14-day trend analysis, risk scoring breakdown, recommended actions, cost estimation, and comparison with 3 similar past incidents. Ready for download.
From alarm to resolution — a complete workflow that connects field engineers with real-time intelligence.
Tickets auto-generated from alarms or created manually by engineers. Full lifecycle tracking with SLA enforcement.
Field engineers receive real-time notifications, claim tickets, upload documentation, and update status on the go.
Every alarm event is timestamped and stored with before/after status. Fully searchable by location, date, or anomaly type.
Translate complex sensor telemetry and maintenance history into clear financial models, risk scorecards, and executive-ready investment proposals — automatically.
Automatically quantify operational risk from alarm trends and degradation curves. Compare repair-vs-replace scenarios with evidence-based cost models.
Scheduled weekly and monthly intelligence briefs — synthesizing alarm data, health trends, and financial impact into presentation-ready formats.
MonCube exposes processed data to existing SCADA systems via Modbus TCP — control rooms pull real-time AI insights directly.
MonCube acts as a Modbus TCP server — existing SCADA systems pull processed data using standard Modbus read operations on port 502.
Health indexes, alarm states, and sensor readings are mapped to Modbus registers — plug-and-play for any SCADA/HMI system.
Existing SCADA infrastructure gains AI-powered insights and predictive analytics without replacing any hardware or control systems.
Built for scale — multi-tenant isolation, role-based security, and responsive design across all devices.
Serve multiple business units from one centralized system.
Role-based access control for every user type.
Access from desktop, tablet, or mobile with optimized UX.
With MonCube, PT Staris Semesta Perkasa successfully transformed their electrical asset management into a proactive, data-driven system that scales with future industrial demands.
Discuss Your Project
