Case studies
IoT Flowmeter Anomaly Detection
Leveraging AI to detect anomalies in real-time IoT flowmeter data for industrial optimization
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Summary
This project involved developing an advanced anomaly detection system for IoT-enabled flowmeters deployed across multiple manufacturing facilities. By leveraging machine learning algorithms trained on historical sensor data, we built a real-time monitoring system capable of identifying irregular flow patterns, equipment degradation, and potential failures before they occur. The system processes thousands of data points per second and provides actionable alerts to maintenance teams.
Duration
6 months
Team Size
8
01
Challenges
The primary challenges included handling massive volumes of streaming sensor data with varying quality and reliability, establishing baseline patterns across different flowmeter types and operating conditions, minimizing false positives while maintaining high detection sensitivity, and integrating with legacy SCADA systems already in place at client facilities.
02
Innovation
We developed a hybrid approach combining statistical process control with deep learning autoencoders. The system uses an ensemble of models — each specialized for different anomaly types (point anomalies, contextual anomalies, and collective anomalies). A novel feature engineering pipeline extracts temporal patterns at multiple time scales, enabling the system to distinguish between normal operational variations and genuine anomalies.
03
Impact
The deployed system achieved a 95% detection rate for critical anomalies with less than 2% false positive rate. Early detection of equipment issues reduced unplanned downtime by 40% and maintenance costs by 25%. The predictive capabilities enabled a shift from reactive to proactive maintenance strategies across all monitored facilities.
Impact metrics
Our impact in the vertical
Quantifying our success to showcase how we bring transformative AI solutions to healthcare
40%
Reduction in Downtime
95%
Anomaly Detection Rate
25%
Maintenance Cost Savings
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