01
Unexpected Equipment Failures
Unplanned downtime costs manufacturers 5–20% of productive capacity. Most failures are predictable weeks in advance yet go undetected until too late.
Predictive Maintenance
Prevent downtimebefore it costs production.
AI-powered monitoring that identifies equipment failure weeks before it happens across rotating assets, electrical systems, and process units in oil & gas, cement, utilities, and mining.
- ↓50%
- Unplanned downtime
- 3–9×
- Lower failure costs
- 24/7
- Continuous monitoring
- 6 wk
- To first pilot results
The challenge
Manufacturing maintenancepain points.
Heavy industry runs on margins that cannot absorb surprise. When equipment fails without warning, costs cascade far beyond repair.
02
Inefficient Maintenance Scheduling
Time-based maintenance wastes resources on healthy assets while missing actual failure patterns. Work orders stay reactive, not risk-ranked.
03
Lack of Real-Time Visibility
No unified view of equipment health across facilities creates blind spots. Operators make critical decisions with incomplete, delayed information.
04
Reactive Maintenance
Fixing after failure is 3–9× more expensive than predictive intervention. Emergency spend erodes margins and delays capital decisions.
The solution
AI-PoweredPredictive Maintenance
Keep machines running efficiently and maximize uptime with real-time equipment monitoring, predictive wear analysis, and optimized maintenance planning.
Real-Time Equipment Monitoring
Continuous sensor data analysis to detect anomalies before they become failures.
Predictive Wear Analysis
Machine learning models that forecast component degradation and remaining useful life.
Optimized Maintenance Scheduling
AI-driven scheduling that balances maintenance costs, production priorities, and failure risk.
Actionable Insights from Sensors to Dashboard
Transform raw sensor data into clear, prioritized maintenance actions.
Seamless Integration with Enterprise Systems
Connect to existing ERP, CMMS, and IoT infrastructure without disruption.
How it works
From sensorsto action.
A clear path from field data to maintenance decisions built for plants that need reliability without adding operational complexity.
01
Sensors
High-fidelity IoT sensors capture real-time vibration, temperature, pressure, and performance data from critical assets.
02
AI Model
Machine learning models trained on your operating envelope detect anomalies and predict failure probability in real time.
03
Dashboard
Unified health scores, risk assessments, and maintenance recommendations give operators clear, prioritized visibility.
04
Alerts
Intelligent alerts and work orders reach maintenance teams with actions, parts, and time windows before failures occur.
Key features
Production-gradecapabilities.
Six capabilities deployed as a connected suite or targeted against your most critical asset class first.
Capability 01
Real-time Anomaly Detection
Identify equipment irregularities milliseconds after they occur across vibration, thermal, acoustic, and electrical signatures.
Capability 02
Predictive Wear Analysis
Forecast component degradation and remaining useful life with high accuracy maintenance windows planned around actual condition.
Capability 03
Optimized Maintenance Scheduling
Coordinate maintenance windows with production schedules to minimize impact while keeping risk within tolerance.
Capability 04
Data-Driven Cost-Benefit Analysis
Quantify the ROI of every maintenance decision with financial modeling tied to your margins and production value.
Capability 05
ERP Integration
Sync maintenance data with SAP, Oracle, and other enterprise systems seamlessly your workflow stays intact.
Capability 06
Enterprise Security & Compliance
Industrial-grade security with role-based access, audit trails, and compliance-ready data handling for regulated environments.
Use cases
Built for assetsthat cannot fail.
Domain models trained on your industry's physics, failure modes, and operating constraints not generic AI applied to an industrial context.
Oil & Gas
Upstream & downstream assets
Monitor compressors, pumps, and rotating equipment across refineries and production facilities before failures disrupt throughput.
Manufacturing
Manufacturing Equipment
Predict failures in CNC machines, motors, conveyors, and production line equipment before they halt production.
Construction
Heavy equipment & site assets
Track cranes, excavators, generators, and critical site machinery to prevent costly delays and safety incidents on major projects.
Energy
Energy Management
Detect inefficiencies in motors, compressors, and drives to reduce energy waste and operating costs.
Chemical Industry
Chemical Processing Systems
Monitor reactors, pumps, compressors, and processing equipment for early fault detection, process instability, and component wear.
Fleet
Fleet Maintenance
Track vehicle health in real time, predict breakdowns, and optimize service schedules across entire fleets.
Expected outcomes
Outcomes you cantake to the board.
01
30–50% reduction in downtime
Anomaly detection shifts maintenance from reactive firefighting to condition-based intervention before failures occur.
02
3–9x lower repair costs
Predictive scheduling aligns spend with actual asset risk eliminating unnecessary interventions and emergency repair bills.
03
15–20% energy savings
Early degradation detection in motors, compressors, and drives reduces energy waste before secondary damage compounds.
Frequently asked questions
Preventive maintenance follows fixed schedules regardless of asset condition. Predictive maintenance uses live sensor data and machine learning to intervene only when degradation is detected reducing unnecessary work while catching failures earlier.
Most pilots begin with existing historian, SCADA, or IoT sensor streams vibration, temperature, pressure, and runtime data. We map your assets and data sources in the first two weeks and define success metrics before models are trained.
A focused pilot on one asset class or production line usually runs 6–10 weeks: discovery and integration in weeks 1–2, model training and validation in weeks 3–6, and live monitoring with ROI tracking through week 10.
Yes. INSUS connects to SAP PM, Oracle EAM, IBM Maximo, OSIsoft PI, and common SCADA platforms. Risk-ranked work orders and alerts can flow into the systems your maintenance teams already use.
Rotating equipment, compressors, motors, and critical process units in oil & gas, cement, mining, and manufacturing often show measurable downtime reduction within the first pilot especially where unplanned stoppages carry high production cost.
Yes. Deployments support on-premises, private cloud, and hybrid models. UAE data residency is available for organisations with data localisation requirements.
Models are trained on your operating envelope and back-tested against historical failures and known events. Pilots include transparent accuracy tracking, operator review workflows, and continuous retraining as conditions evolve.
Get started
Ready to predict the futureof your operations?
Book a predictive maintenance assessment. We will map your highest-impact assets and outline what a pilot could return no commitment required.
- ✓ UAE data residency
- ✓ Swiss engineering
- ✓ No lock-in