ADAPT Series — adaptive ML monitoring

Description

ADAPT Series (Adaptive Diagnostic and Predictive Technology) is an AI-powered monitoring system that automatically adapts to equipment characteristics and delivers high-accuracy predictive diagnostics.

Key features

Adaptation technology

• Machine learning - automatic learning from normal operating data
• Automatic classification - equipment operating mode identification
• Adaptive baselines - dynamic reference values for each operating mode
• Anomaly detection - detection of deviations from normal operation

Intelligent analytics

• Predictive models - defect progression forecasting
• Automatic diagnostics - fault type identification
• Prioritization - ranking issues by criticality
• Recommendations - recommended actions

Integration

• System 1 - full integration with the monitoring platform
• SCADA systems - bidirectional data exchange
• Historians - database recording for analysis
• Mobile devices - notifications and data access

Applications

Critical machinery

• Gas and steam turbines
• Large compressors
• Generators
• Pump units

Variable-duty equipment

• Variable load
• Changing operating conditions
• Multiple operating modes
• Seasonal changes

New equipment

• No historical data
• Unknown vibration characteristics
• Break-in period
• Rapid monitoring startup

ADAPT benefits

1. Rapid startup - no prior data required
2. Autonomy - automatic setup without an expert
3. Accuracy - accounts for specific equipment characteristics
4. Early detection - early-stage problem detection
5. Fewer false alarms - adaptation to operating modes

How it works

Stage 1: Learning

• Data collection under normal operating conditions
• Automatic operating mode detection
• Baseline building for each mode
• Typically takes 1–4 weeks

Stage 2: Monitoring

• Continuous vibration analysis
• Comparison with adaptive baselines
• Deviation detection
• Anomaly classification

Stage 3: Forecasting

• Trend analysis of defect development
• Remaining useful life forecast
• Maintenance recommendations
• Repair planning

Technical specifications

Parameter	Value
Inputs	Vibration, process variables
Processing	FFT, envelope, statistics
ML models	Neural networks, clustering
Update	Real-time
Data storage	Up to 5 years of historical data
Interface	Web, API, mobile

Comparison with traditional monitoring

Feature	Traditional	ADAPT Series
Setup	Manual (expert)	Automatic
Baseline	Fixed	Adaptive
Alarms	Static setpoints	Dynamic
Operating modes	Single baseline	Multiple baselines
Detection	Threshold exceedance	Anomaly-based
Time to deploy	Weeks/months	Days/weeks

Applications across industries

Oil & gas industry

• Compressor stations
• Oil production
• Processing

Power generation

• Combined heat and power (CHP) plants and condensing thermal power plants
• Hydroelectric plants
• Wind turbines

Manufacturing

• Production lines
• Machine tools and equipment
• Conveyor systems