Overview#
A surveillance camera covering a critical infrastructure perimeter starts showing minor telemetry anomalies: operating temperature trending slightly higher, image processing latency increasing by small increments each day. On their own, these signals are easy to miss. Together, they indicate a cooling system degradation that will cause the camera to fail within ten days. Without predictive analytics, a technician only visits after the camera goes dark, often at the worst possible moment. With it, the work order is raised automatically, the technician arrives before failure, and the perimeter stays covered.
The Predictive Maintenance Analytics module applies machine learning to equipment telemetry to forecast failures before they happen. The shift from reactive repair to proactive maintenance scheduling has concrete effects on operational readiness: less unplanned downtime, extended asset lifespans, and maintenance resource allocation driven by actual equipment condition rather than calendar schedules.
Open Standards#
- ISO 55000 (Asset Management): Maintenance scheduling and health scoring align with the ISO 55000 / BSI PAS 55 framework for systematic asset lifecycle management, providing a recognised structure for risk-based inspection intervals and condition-based decision making.
- EN 13306 (Maintenance Terminology): Maintenance task types, work order classifications, and PM schedule categories follow the terminology defined in this European standard, ensuring interoperability with third-party computerised maintenance management systems (CMMS).
- EN 15341 (Maintenance Key Performance Indicators): Fleet-level analytics such as mean time between failures, availability trends, and maintenance cost ratios are structured around the KPI taxonomy defined in this European standard.
- IEEE C57.104 (Transformer Dissolved Gas Analysis): Transformer oil-testing work orders are generated with procedures conforming to this IEEE guide, including dissolved gas analysis (DGA) and dielectric strength criteria for fault assessment.
- NERC CIP (Critical Infrastructure Protection): Preventive maintenance schedules for electric utility assets such as transformers and substations carry the NERC CIP compliance framework tag, supporting audit trail requirements for bulk electric system operators.
- PHMSA 49 CFR Part 192 (Pipeline Safety): Gas main and gas valve inspection work orders are generated with procedures and operator qualification requirements derived from the US Pipeline and Hazardous Materials Safety Administration pipeline safety regulations, applicable also as a reference baseline for comparable EU pipeline codes.
- EPA Safe Drinking Water Act (SDWA): Water asset PM schedules for valves and hydrants are tagged to the EPA SDWA compliance framework, aligning inspection frequencies and record-keeping with regulatory requirements for public water systems.
- GraphQL: All predictive maintenance queries and mutations, health scores, failure forecasts, weather impact, and PM calendars, are exposed through a typed GraphQL API, enabling standards-compliant integration with dashboards and third-party operational platforms.
Last Reviewed: 2026-03-02 Last Updated: 2026-04-14
Key Features#
- Failure Prediction Models: Machine learning models trained on historical failure data and real-time telemetry detect degradation patterns that precede equipment failures, generating predictions with confidence scores and estimated time-to-failure
- Condition-Based Monitoring: Continuous analysis of equipment operating parameters against established healthy baselines, with automatic detection of trending anomalies that indicate developing problems
- Maintenance Scheduling Optimisation: Recommended maintenance windows balance failure risk against operational impact, considering equipment criticality, parts availability, and technician schedules
- Equipment Health Scoring: Real-time health scores for all monitored equipment based on multi-dimensional telemetry analysis, enabling at-a-glance fleet health assessment
- Root Cause Analysis: When failures do occur, the system analyses contributing telemetry patterns to identify root causes, updating prediction models and recommending preventive measures for similar equipment
- Spare Parts Forecasting: Predict spare parts requirements based on fleet health trends and upcoming maintenance schedules, enabling proactive procurement and inventory management
- Work Order Generation: Automatically create maintenance work orders from high-confidence failure predictions with pre-populated diagnostic information, recommended procedures, and required parts lists
- Fleet Analytics Dashboard: Aggregate maintenance metrics across equipment fleets showing failure rates, prediction accuracy, mean time between failures, maintenance costs, and availability trends
Use Cases#
- Camera Network Maintenance: Predict camera failures across surveillance networks based on telemetry trends, scheduling technician visits before cameras go offline and ensuring continuous coverage of monitored areas
- Infrastructure Asset Management: Monitor critical infrastructure equipment including generators, HVAC systems, and network hardware, scheduling maintenance during planned downtime windows
- Vehicle Fleet Management: Track vehicle health telemetry to predict mechanical issues and schedule preventive maintenance at optimal intervals based on usage patterns and component degradation rates
- Communication Equipment: Monitor radio systems, mobile devices, and communication infrastructure to predict failures that could impact operational readiness during critical activities
Integration#
Connects to the camera telemetry system for surveillance equipment monitoring, the work order execution system for automated maintenance task creation, the asset management platform for equipment history and lifecycle tracking, and the analytics dashboard for maintenance performance reporting. Prediction models are continuously updated with outcomes from completed maintenance activities.
Availability#
- Enterprise Plan: Full predictive maintenance suite included
- Professional Plan: Condition-based monitoring included; predictive models and automated work order generation available as an add-on