Offline-capable real-time monitoring dashboard for wastewater management with multi-category anomaly detection, automated model retraining, and predictive output estimation.
Client's wastewater sensor network was generating continuous signals but the existing system had no mechanism to detect subtle anomalies like gradual drift, oscillation patterns, or frozen readings — causing issues to go unnoticed until they escalated.
Built a multi-layered anomaly detection pipeline using Isolation Forest and rule-based signal analysis to classify anomalies across five distinct behavioral categories. Wrapped this in a real-time offline dashboard with fully configurable detection thresholds, a predictive layer that maps input parameters to expected output values, and an automated retraining pipeline to keep the model accurate as operational conditions change.
Engineered a comprehensive real-time monitoring system for an industrial wastewater management client struggling with undetected signal anomalies in their sensor network. The existing infrastructure lacked the intelligence to distinguish meaningful deviations from normal operational variance, leading to delayed responses and compliance risks.
The system introduces a multi-category anomaly detection engine capable of identifying range violations, signal drift, oscillation cycles, spike events, and frozen/stale signals — all processed and visualized within an offline-first dashboard that operates independently of cloud connectivity. Additionally, the platform incorporates a predictive module that estimates key output parameters based on configurable input signals, empowering site operators to make proactive decisions without relying on external expertise.
To ensure the model stays accurate over time as sensor behavior and operational conditions evolve, an automated retraining pipeline was integrated — periodically retraining the Isolation Forest model on fresh signal data to prevent performance drift and maintain detection reliability in production.
Tangible Impact
Enabled operators to catch and respond to signal anomalies in real time; all detection parameters made user-configurable, reducing dependency on developers for threshold tuning at site level. Automated retraining ensured sustained model accuracy without manual intervention as sensor patterns evolved over time.
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