Anomaly Detection
Anomaly Detection AI Agent
Spots early warning signs in sensor data and operation logs, reducing equipment downtime risk and maintenance costs.
Overview
We build anomaly detection models tailored to your site's data characteristics, delivering an end-to-end flow from alerts and visualization through to reporting. It eliminates the reliance on individual experts for threshold design and raises the precision of preventive maintenance.
Benefits
- Catches anomaly signs early, reducing downtime risk
- Standardizes monitoring rules and reduces operational overhead
Target Industries
- Manufacturing
- Industrial Plants
- Equipment Maintenance
- Energy
- Quality Assurance
Challenges We Solve
Before
- Missed anomaly signs lead to equipment shutdowns
- Thresholds and monitoring rules depend on individual experience
- Root-cause analysis after alerts takes too long
After
- Anomaly scores are visualized, enabling early response
- Standardized detection logic levels out operations
- Dashboards accelerate root-cause analysis
Software Features
Data Ingestion & Preprocessing
Imputes missing values and normalizes time-series data to make it ready for analysis.
Anomaly Score Computation
Combines statistical methods with machine learning to quantify anomaly signals.
Alerts & Visualization
Supports on-site decision-making through alert notifications and dashboards.
Input Formats
Sensor data (CSV / JSON), databases, OPC UA, and more
Output Formats
Anomaly scores, alert history, daily reports (PDF / CSV)
Integrations
SCADA / MES, monitoring notifications (email / chat), cloud platforms
Technologies
- Time-Series Analysis
- Statistical Anomaly Detection
- Machine Learning
Output Examples
Contact Us
We propose detection models and operational workflows tailored to your specific data environment.
- PoC and validation support for anomaly detection
- Standardization of threshold design and monitoring rules
- Design of alert notifications and dashboards