Predictim PMPlus
Predictim PMPlus is an enterprise predictive maintenance platform, available as both a SaaS operational interface and developer API. Developed through rigorous applied research in collaboration with leading academic institutions, it applies across rail networks, port infrastructure, marine assets, energy installations, and industrial plant — forecasting asset degradation before it occurs and delivering ranked intervention priorities, optimised maintenance schedules, and network-wide health visibility that operations teams can act on immediately.
Illustrative results from representative deployments; not a guarantee of outcome.
What this platform does
Each capability is a deployed module — not a feature on a roadmap. Engagements typically begin with a 4-week scoping exercise to confirm fit against your operational data and constraints.
Failure risk forecasting
Continuously analyses real-time sensor readings and historical maintenance records to assign per-asset failure probability scores, ranked by urgency and safety impact.
Intelligent maintenance scheduling
Translates risk scores into optimised work order sequences, balancing safety thresholds, maintenance resource availability, and operational possession windows.
Network-wide health monitoring
Provides a spatial overview of asset degradation across the entire infrastructure network, flagging at-risk clusters before cascading service disruptions occur.
Proactive intervention planning
Recommends the appropriate intervention level — inspection, light repair, or full replacement — weeks in advance, enabling budget planning and contractor scheduling.
Cross-infrastructure adaptability
Core architecture is domain-agnostic: validated on railway tracks, directly applicable to marine pipelines, water distribution networks, energy installations, and port infrastructure.
Related reading
How to Forecast Equipment Replacement Timing: The Most Accurate Methods Compared
"Expected life" is a single number that throws away most of the information a maintenance team actually has. Survival analysis and machine learning both do better — but not for the same data, and not by the same margin.
ReadLogistic Regression for Predictive Maintenance: What It Can (and Cannot) Predict
Logistic regression is cheap, interpretable, and well understood — but it answers a narrower question than most maintenance teams think it does, and knowing exactly what that question is decides whether it belongs in the pipeline at all.
ReadEllipsoidal and Non-Parametric Approaches to Time Series Anomaly Detection in Industrial Data
A single-sensor threshold misses the failures that only show up as correlated drift across several signals at once — which is exactly the class of anomaly ellipsoidal and non-parametric methods are built to catch.
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ExploreReady to deploy Predictim PMPlus?
Request API access, or talk to our engineering team about integrating this into your existing operations.