See It Work
Documentation Support Trust Center Pricing Partners
See It Work
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-BLOWER-PDM-ADV-001 AI Agent

Blower Predictive Maintenance Advisor

Tracks blower efficiency loss and predicts failures 14 to 60 days ahead, turning hidden energy waste into scheduled maintenance.

Water & Wastewater Predictive Maintenance

Target outcome · Blower unplanned failures reduced to zero. Fleet efficiency maintained within 2% of baseline.

Business problem

Blowers are the highest-energy and highest-failure-impact equipment in a wastewater plant. An unplanned blower failure forces the plant into backup aeration mode with a direct risk to biological process stability and effluent compliance. Blowers also degrade silently: inlet guide vane wear, bearing degradation, and seal leakage cause 5 to 15% efficiency loss before any alarm triggers. That efficiency loss is pure energy waste, invisible to threshold-based monitoring.

Read more Show less

A single major rebuild costs $80K to $200K. An unplanned failure during peak operating conditions compounds that with forced reduced-capacity operation. The degradation signature is detectable in efficiency trends, vibration patterns, and thermodynamic performance — but detecting it requires continuous comparison against OEM curves and a fleet baseline, which humans cannot do by eye.

What it does

Combines vibration analysis, thermodynamic performance monitoring (airflow versus power curve), and bearing and seal condition data.

Read more Show less

Produces a fleet-level efficiency dashboard with per-blower degradation trends, a predicted failure window for each unit, quantified monthly energy impact per blower, and a recommended rebuild sequence aligned with maintenance windows and process demand.

Current process vs. with AI Agent

TODAY · PREDICTIVE MAINTENANCEREACTIVE
×
Which blower to prioritise for rebuildRun hours and age
×
Rebuild timingOEM interval, or forced by failure
×
Efficiency loss investigationAnnual performance test

Outcomes and measurement

Blower unplanned failures

Baseline 1 to 3 per year
With agent Zero

Fleet average efficiency degradation

Baseline 5 to 10% per year drift
With agent Maintained within 2% of baseline

Efficiency-related energy cost

Baseline Hidden in aggregate bill
With agent Quantified per blower per month

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

Data inputs

SCADA

blower KWRPMinlet guide vane positionairflowdischarge pressurebearing temperaturesvibration

OEM performance curves

Other

maintenance historyrun-hour logs

*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 blowers and what service type (centrifugal, positive displacement, turbo)?
  2. What vibration monitoring already exists?
  3. Are OEM performance curves available in digital form?
  4. What is the current approach to efficiency testing?
  5. When was the last fleet-wide performance test and what did it find?

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 Blower Predictive Maintenance Advisor fits your operations, what data you'd need, and what a scoping engagement typically looks like.

← Browse all templates