Dynamic Normalization and Fault Detection

Task

With artificial intelligence methods, large volumes of sensor data from RO systems (e.g., pressure, flow, TDS, temperature) must be normalized and analyzed to detect anomalies such as fouling, scaling, or mechanical faults. The goal is to support operators and engineers in proactive system diagnostics.

Solution

The AI-based solution developed by SensQuant uses machine learning models to process time-series data from multiple sensors in real-time. The system dynamically normalizes performance values based on operating conditions and identifies unusual patterns, displaying them visually with confidence scores. It also suggests probable causes and corrective actions.

Benefit

Traditionally, analyzing RO performance requires manual normalization and hours of engineering work. With SensQuant, anomalies are detected automatically, enabling rapid diagnosis and optimized maintenance decisions—significantly reducing downtime, cleaning frequency, and operational costs.