Data Science & Analytics case study
Predictive maintenance and equipment telemetry forecasting
An anonymised summary of the challenge, approach and outcome. Client names and measured figures are published only with written consent.
Anonymised client work
We do not publish client names or identifying details without written consent, and we do not publish numbers we cannot substantiate. The summary below describes the shape of the work without exposing the client.
Client
Data Science & Analytics sector organisation, identity withheld pending consent
Challenge
High unplanned downtime and reactive maintenance on critical manufacturing assembly lines caused production bottlenecks and costly emergency repairs.
Solution
Acmez engineered an end-to-end data pipeline streaming sensor vibration, thermal, and acoustic metrics into time-series anomaly detection and remaining useful life (RUL) predictive models with automated maintenance alerting.
Outcome
Plant engineering transitioned from reactive repairs to condition-based servicing, extending machinery lifespan and providing leadership with live visibility into asset health.
Capability involved
Next step
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