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SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available WATER-WWTP-PUMP-PDM-ADV-001 AI Agent

Pump Predictive Maintenance Advisor

Detects pump degradation 7 to 30 days before failure and ranks the fleet by business-criticality-weighted risk.

Water & Wastewater Predictive Maintenance

Target outcome · 50% reduction in unplanned pump failures. 40% reduction in emergency maintenance cost.

Business problem

Municipal wastewater plants run dozens to hundreds of pumps: raw influent, return and waste activated sludge, chemical dosing, effluent transfer, storm pumps. Current monitoring relies on threshold alarms that fire at the moment of failure, not in advance. When a critical pump fails, the process feels it before the alarm does, and emergency maintenance costs two to three times what planned work costs.

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Industry data is clear: 70 to 80% of pump failures show detectable degradation patterns 7 to 30 days in advance. The signals are in vibration, motor current, flow, and pressure data. The bottleneck is not data availability. It is the pattern-recognition capacity to synthesise across the fleet in real time.

What it does

Synthesises continuous vibration, motor current, flow, pressure, and bearing temperature data across every pump.

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Uses hybrid physics and ML models trained on the site's own historical failures plus reference data from similar fleets. Produces a per-pump health score with trend, a predicted failure mode with confidence, an estimated time-to-action window, and a business-criticality-weighted ranking across the fleet.

Operational profile

Predict pump failures across the fleet 7–30 days in advance and rank intervention by business criticality.

Control mode: Human-Approved

User it helps
Reliability engineers, maintenance planners, plant managers.
Context it uses
Continuous vibration, motor current, flow, pressure and bearing temperature telemetry · CMMS work-order history · OEM performance curves · failure-mode library.
Decision supported
Which pumps to intervene on this week, in what order, and with what work scope.
Action or output
Per-pump health score with trend · predicted failure mode + confidence · time-to-action window · ranked maintenance recommendations ready for work-request creation.
Evidence captured
Input telemetry snapshot, model version, prediction confidence, recommended action, planner acceptance/override, downstream work order ID, post-intervention outcome.

Current process vs. with AI Agent

TODAY · PREDICTIVE MAINTENANCEREACTIVE
×
Which pumps need attention this weekWalkdown, gut feel, reactive alarm review
×
Parts to order ahead of shutdownWorst-case stocking guess
×
Maintenance interval adjustmentOEM calendar, rarely changed
×
Escalation to reliability engineeringShift complaints, repeat alarms

Outcomes and measurement

Unplanned pump failures per year

Baseline 10 to 30% of fleet
With agent 50% reduction

Emergency maintenance cost

Baseline $150K to $400K/yr at mid-size plants
With agent 40% reduction

Alert true-positive rate

Baseline Not yet measured
With agent ≥80%

Weekly planner adoption

Baseline Not yet measured
With agent ≥70% use the ranked list

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

SCADA

vibrationmotor currentflowpressurebearing temperatures

CMMS

work order and failure history

Equipment

asset register with criticality rankingOEM pump curves

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. How many pumps are in scope, grouped by service class?
  2. What is the current unplanned failure rate and annual emergency maintenance cost?
  3. What condition monitoring already exists?
  4. Is vibration data available continuously or only from periodic contractor surveys?
  5. What CMMS system holds maintenance history and for how long?

Want our AI to walk you through these scoping questions?

SPEAK WITH OUR TEAM

Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Pump Predictive Maintenance Advisor fits your operations, what data you'd need, and what a scoping engagement typically looks like.

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