Pump Station Alerting

Protect performance. Avoid downtime.

Keep pump stations running at peak efficiency with early, precise alerts.

Pumps can fail

suddenly.

We see it first.

Pump failure can be costly and time-consuming to repair. StormHarvester’s AI-powered Pump Station Alerting continuously monitors pump performance to identify early signs of decline or failure.

Detecting issues before they escalate, enables targeted interventions that minimise downtime, reduce repair costs,  and protect the environment while maintaining network reliability.

Pump station alerting for reliable,
efficient performance

From reducing repair costs to protecting the environment, StormHarvester helps utilities keep pumps operating at their best. Our precise alerts let you act before issues cause service disruption or costly downtime.

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Safeguard Environment

Early detection prevents pollution incidents and helps to maintain regulatory compliance.

Target Callouts

Send crews exactly where they’re needed, cutting response times and eliminating guesswork.

Optimise Assets

Maintaining a consistent performance reduces energy and maintenance costs.

How pump station alerting works

StormHarvester’s Pump Station Alerting uses pump start and stop data to create an analogue dataset representing the performance of each pump and pumping station in your network.

By combining this with rainfall data, the system applies AI analysis to detect and alert on a range of potential issues.

Pump Station Alerting Example, Utility X

1) The pump was taking longer than expected to drain down the wet well

2) An alert was generated by StormHarvester

3) Utility intervention removed a blockage from within the impeller of Pump 1

Pump ineffective

Pump performance declining

Shorter/longer than expected run-time

Wet well level low

Blockage detected

Discover how we keep pumps running

From extending equipment life to reducing maintenance costs, Pump Station Alerting helps you minimise downtime and maintain optimal network performance.

Optimise operations.

Enhance reliability.

Minimise downtime.

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View real-time pump performance with alert locations

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Monitor live pump run times, start frequency, and energy use

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Access historical trends, logs, and rainfall correlations

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Extend asset life and reduce unplanned maintenance costs through Condition Based Maintenance (CBM)

Inputs
Monitored

E
C

Stormharvester
AI Engine

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Real-time
Alerts

case studies

Our solutions drive transformative results for wastewater utilities, municipalities and environmental agencies, helping organisations meet regulatory, environmental, operational goals while protecting the environment.

Southern Water

Southern Water

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.

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South West Water

South West Water

South West Water started a trial with StormHarvester in 2023 for Blockage Detection, monitoring 300 sites initially. South West Water made the conscious decision to end the blockage detection trial early due to the StormHarvester platform demonstrating benefits to the business.

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Anglian Water

Anglian Water

StormHarvester and Anglian Water began working together in 2022 as part of Anglian Water’s Dynamic Sewer Visualisation (DSV) programme. The Anglian Water Dynamic Sewer Visualisation (DSV) programme was launched in February 2023.

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Our wastewater solutions

Stay ahead of challenges with real-time intelligence that helps you maximise network efficiency, reduce costs, and prevent pollution.

Inflow & Infiltration
Detection

Blockage
Detection

Rising Main Alerting

Spill
Verification

Built for performance. Trusted worldwide.

From the UK to New Zealand, water utilities rely on StormHarvester for
fewer incidents, faster response, and easier reporting, all powered by AI-driven insight and real-time data.

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