AI Pump Cavitation Detection

The Problem

“Detect pump cavitation before costly failures”

Organizations face these key challenges:

Intermittent cavitation is difficult to detect with periodic inspections and simple vibration thresholds, leading to late discovery after damage occurs

High false-alarm rates from conventional monitoring cause alarm fatigue and delayed response, especially when cavitation resembles other hydraulic/mechanical issues

Root-cause identification (NPSH deficit, suction blockage, vapor pressure changes, entrained gas, off-curve operation) is time-consuming and requires scarce rotating equipment expertise

Impact When Solved

20–40% reduction in pump-related unplanned downtime via early cavitation detection and targeted operating adjustments15–30% reduction in cavitation-driven repairs (impeller, seal, bearing replacements), extending mean time between repairs by 10–25%$0.5–5.0M annual value per critical pump train by avoiding 1–2 major cavitation-induced outages and improving throughput/reliability

Real-World Use Cases

Predictive Maintenance Framework for Wind Turbine Blade Erosion

This is like putting a smart ‘health monitor’ on wind turbine blades so you can tell when their edges are wearing down long before they fail, and schedule service at the best time instead of waiting for breakdowns.

Time-SeriesEmerging Standard

8.5

AI Condition Monitoring for Wind Turbines

This AI framework monitors wind turbines to detect any problems early, helping to prevent energy losses.

anomaly detectionemerging

8.5