Asset-intensive operations · Maintenance

Explainable predictive maintenance

30% less downtime

Client context

An asset-intensive operation running continuous, multi-shift production across a fleet of critical rotating and process equipment.

Problem size

Thousands of unplanned-downtime hours a year under calendar-based maintenance — either replacing healthy parts early or finding failures after they had already stopped the line.

What KEPLER did
  • Engineered a dataset from sensor/SCADA, maintenance-log and work-order history
  • Built explainable asset-health models that flagged rising failure risk with a reason code
  • Pushed alerts into the CMMS as work orders — not another dashboard to check
  • Added a Power BI layer for reliability engineers to track asset health and model performance
Results

30% less unplanned downtime, with fewer premature part replacements and maintenance effort focused where failure risk is highest.

Anonymized client engagement; identifying details withheld for confidentiality.

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