Predictive maintenance data

Predictive maintenance promises fewer failures and lower costs, but it depends on the right data. This guide covers the internal and external data behind predictive maintenance and how to source what is missing.

Why data makes or breaks predictive maintenance

Predicting failures requires data on how equipment behaves and fails. Many programmes stall not on algorithms but on data: too few failure examples, inconsistent sensors, or missing context. Sourcing fills those gaps.

The data involved

• Telemetry: sensor and condition data from equipment.
• Failure and maintenance: records of faults and interventions.
• Asset metadata: make, model, age and configuration.
• External context: weather and operating environment.

The failure-data problem

Failures are rare, so labelled failure data is scarce, which limits models. Synthetic data and pooled or external datasets can supplement scarce failure examples.

External context improves models

Operating conditions, weather, load, environment, drive wear. Adding external context data often improves predictions beyond telemetry alone.

Sourcing considerations

The EU Data Act affects access to machine data. Harmonising sensors and combining telemetry with context and external datasets is central, and device metadata underpins quality.

In a managed model

A managed partner can source supplementary failure and context data, harmonise it with telemetry, and deliver model-ready datasets.

The failure-data problem

Predictive maintenance stalls more often on data than on algorithms. Failures are rare, so labelled failure examples are scarce, which limits model accuracy. Synthetic data and pooled or external datasets can supplement those examples, and external context, weather, load, operating environment, often improves predictions beyond on-machine telemetry alone.

Harmonising telemetry and access

Machine data uses varied protocols and data models, so harmonising tags, units and timestamps, and capturing asset metadata, is essential. The EU Data Act now strengthens access to data connected equipment generates, changing the negotiating position with OEMs. Combining harmonised internal telemetry with external context is where reliable predictive-maintenance datasets come from.

Sources & further reading

• European Commission: The Data Act (connected-product data).
• Industry references on condition monitoring and reliability.
• ISO standards on condition monitoring.
• Internal practice: DataSupplier industrial sourcing.