ARIA - Asset Reliability Intelligence
Detects anomaly patterns and reliability risks so maintenance and production teams can triage asset issues earlier.
Designed around a 72-hour early-warning target and a 20% unplanned-downtime reduction scenario.
The Challenge
Asset issues are often detected only after operating symptoms become downtime events, making maintenance response more reactive and expensive.
The Solution
Built an anomaly detection and reliability intelligence workflow for early warning, triage, and asset-health review.
Key Features
- Anomaly detection
- Reliability scoring
- Time-series monitoring
- Alert-ready architecture
Business Impact
- Frames maintenance action around earlier warning signals
- Supports downtime-reduction initiatives with measurable targets
- Improves prioritisation of reliability investigations
Lessons Learned
- Reliability AI must be tuned around real operating context.
- A model is useful only when it fits maintenance workflow.
Project Proof
Designed placeholder for architecture and evidence.
Real screenshots can be added later. For now, this proof layer documents the workflow, stack shape, and delivery logic behind the case study without inventing unavailable assets.
Architecture Summary
Built an anomaly detection and reliability intelligence workflow for early warning, triage, and asset-health review.
Workflow
Frame the operational question
Prepare and validate source data
Anomaly detection
Frames maintenance action around earlier warning signals
Stack Grouping
Data & ML
Infrastructure
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