Case Study • outcomes-focused
Predictive Maintenance + Downtime Reduction
Anomaly detection and triage workflows for earlier failure detection.
Predictive Maintenance + Downtime Reduction
Anomaly detection and triage workflows for earlier failure detection.
🎯 Problem
What was happening
Unplanned downtime caused production losses and reactive maintenance. Alerts were noisy and lacked context.
✅ Outcome
What changed
- Earlier detection of failure modes (lead time improvement)
- Reduction in false alarms through calibration + thresholds
- Operational dashboard for trends, root causes, and interventions
Metrics shown are examples—replace with real results or anonymized ranges.
🧠 Approach
Method
Combined sensor + maintenance logs, engineered features, and trained anomaly + failure prediction models. Added alert routing and root-cause narratives.
🧱 Architecture
How it shipped
Event ingestion + feature pipeline, scoring service, dashboard layer, and alert webhooks. Monitoring for drift and alert volume.
🛡️ Governance
Safety + reliability
PII minimization, audit logs for interventions, drift and quality checks, and documented model cards for stakeholders.