Intelligent fault detection for heat networks
Traditional heat network monitoring relies on complaints - by which time the problem has already caused cost, disruption, and damage. HeatFlow's AI-powered detection catches issues at the data level, hours or days before they escalate.
Real-Time Fault Alerts
Instant notifications for offline meters, consumption spikes, flatline readings, low batteries, and signal degradation. Know about problems before they escalate.
AI Anomaly Detection
Machine learning algorithms analyse consumption patterns to detect leaks, HIU faults, and system imbalances that manual monitoring would miss.
Automated Job Creation
Critical alerts automatically generate maintenance jobs with SLA deadlines. Assign engineers, track progress, and close the loop - all within one platform.
48-Hour Average Resolution
With instant detection, automatic job creation, and SLA tracking, faults are resolved in an average of 48 hours - before they cause significant cost or disruption.
Predictive Maintenance
Battery life predictions, signal trend analysis, and component lifecycle tracking help you plan replacements before failures occur.
HIU Fault Detection
Detect heat interface unit problems through consumption pattern analysis. Identify units that are over-consuming, under-performing, or showing signs of mechanical failure.
What HeatFlow monitors
Meter Offline Detection
Alerts when meters stop reporting. Configurable thresholds (e.g. no reading for 6, 12, or 24 hours). Distinguishes between signal loss and hardware failure.
Consumption Spike Detection
Flags abnormal consumption increases that may indicate leaks, valve faults, or HIU problems. Thresholds adapt to seasonal patterns and building type.
Flatline Reading Detection
Identifies meters reporting zero or unchanging consumption. Could indicate a stuck meter, disconnected sensor, or vacant unit.
Battery & Signal Monitoring
Tracks battery depletion curves and signal strength trends. Predicts replacement dates and alerts when quality drops below operational thresholds.
Heat Loss & Imbalance
Compares bulk meter readings against the sum of individual meters to identify distribution losses, insulation failures, or metering errors.
Consumption Anomaly Detection
Flags meters where consumption deviates significantly from expected patterns, surfacing potential faults or leaks before they escalate.
Frequently Asked Questions
How does HeatFlow detect heat meter faults?
HeatFlow monitors every meter's readings, battery level, signal strength, and data completeness in real time. AI algorithms compare current behaviour against historical patterns and peer meters to detect anomalies such as offline meters, consumption spikes, flatline readings, and data gaps. Alerts are raised instantly with severity classification and suggested actions.
What is HIU fault detection?
Heat Interface Unit (HIU) fault detection uses consumption pattern analysis to identify malfunctioning HIUs. Signs include abnormal flow rates, excessive return temperatures, or consumption patterns that deviate significantly from historical norms. HeatFlow flags these automatically, allowing engineers to investigate before heating problems occur.
Can HeatFlow automatically create maintenance jobs from alerts?
Yes. When a critical alert is raised (e.g. meter offline for >24 hours, or leak detected), HeatFlow can automatically create a maintenance job, assign it to an engineer, and set SLA deadlines. The entire workflow from fault detection to job completion is tracked in one platform.
What is the average fault resolution time?
Across HeatFlow-managed portfolios, the average fault resolution time is 48 hours. This is achieved through instant detection (vs. waiting for a complaint), automatic job creation, SLA tracking, and engineer mobile access.
Does HeatFlow support predictive maintenance for heat networks?
Yes. HeatFlow tracks battery levels, signal trends, and component age to predict when meters and gateways will need replacement. This allows you to schedule replacements during planned maintenance windows rather than responding to emergency failures.