AUTHOR

StormHarvester

Related Topics

Asset Management

Wastewater Pumping

AI/Machine Learning

THE CHALLENGE

Southern Water previously relied on static Hi-Hi alarms to monitor its pump stations. During periods of heavy rainfall, these alarms would trigger, offering little clarity on which sites genuinely required attention.

As a result, many alerts were routinely “paused”, and critical issues often went undetected until pumps had already failed. This led to reactive callouts, often out of hours, and significant operational disruption.

Operations teams were often overwhelmed and felt unable to tell which alerts required responses.

THE SOLUTION

StormHarvester’s Pump Station Alerting was deployed to provide Southern Water with real-time, AI-drive insights into pump performance. The system analyses pump start/stops, wet well levels, and performance trends – correlating them with live weather conditions to detect anomalies before failure occurs. Descriptive alerts provide actionable insights to issues occurring at pump stations:

RESULTS

The implementation enabled Southern Water to transition from reactive to proactive operations, with alerts more trusted, more actionable, and often resolved during normal working hours as machine learning based dynamic alerts detected early signs of pump station failure.

Faults were identified early and rectified quickly, including pump blocked/restricted, worn/incorrect impellor or wear plate, pump not seated correctly, wet well build-up, and burst rising /pumped mains. Operators gained confidence in the system, and the alert hit rate increased to 88% following the resolution of data issues and adjustment of thresholds.