BridgeOps Intelligence Layer
Explainable maintenance recommendations for industrial operations with a clear human-in-the-loop operating model.
Risk scores alone do not tell teams what to do next.
Combine health, risk, and RUL modeling with explainable recommendation logic.
Prioritized maintenance actions with impact context and governance readiness.
Many predictive maintenance programs stop at risk scoring. Operations and maintenance teams still need practical decisions: which asset to prioritize now, which action to take first, and what business impact to expect from acting versus waiting.
This project is the BridgeOps Intelligence Layer between the Data Layer and Automation Layer. It connects data quality, knowledge retrieval context, and model-driven recommendations into practical decision intelligence. Execution is intentionally human-in-the-loop and governance-ready.
The solution combines health-state modeling, failure-risk forecasting, remaining useful life estimation, and recommendation logic. Instead of delivering predictions alone, it provides explainable maintenance actions with prioritization and impact context.
This project serves as a reference implementation for maintenance decision intelligence concepts and demonstrates how operational data can be transformed into actionable maintenance recommendations.
The focus is on decision support, workflow design, and system architecture rather than production deployment.
Asset signals, operating context, and degradation features are integrated and validated for data quality.
Health states, failure probability, and RUL are estimated through interpretable model logic.
Recommendation logic prioritizes actions such as Monitor, Inspect, Schedule Maintenance, or Immediate Review.
A controlled preview shows how validated recommendations can flow into downstream workflow automation.
This project is a reference implementation designed to demonstrate how reliability engineering, predictive analytics, and decision-support workflows can be combined into a maintenance intelligence platform.
The dashboards, health scores, recommendations, and economic estimates shown throughout the project use realistic synthetic operating data rather than production customer data.
The objective is to illustrate system design, decision workflows, and user experience patterns while avoiding the disclosure of proprietary operational information.
Streamlit dashboard for asset-level decisions, portfolio prioritization, and recommendation explainability.
Rule-guided recommendations with thresholds, priority classes, and explicit rationale.
Health-state logic, failure risk scoring, and RUL estimation with reproducible inputs and tests.
Traceable rationale, documented architecture, and a human-in-the-loop review gate before automation.
Modeled scenarios indicate stronger asset prioritization, lower unplanned downtime risk, and more consistent maintenance actions.
Technical recommendations are linked to modeled cost avoidance and estimated savings opportunity for transparent decision tradeoffs.
Clear evidence of the Intelligence layer in the Data → Knowledge → Intelligence → Automation sequence.
These visuals focus on operational decisions: where to act now, what is likely to happen next, and which intervention creates the highest impact.
Visual 1
Answers: Which assets require attention?
| Asset ID | Health Score | Risk Level | Status | Last Inspection |
|---|---|---|---|---|
| CNC-104 | 92 | Low | Healthy | 2026-06-05 |
| PRESS-218 | 81 | Moderate | Watch | 2026-05-31 |
| PUMP-067 | 68 | Moderate | Watch | 2026-05-27 |
| FURN-011 | 84 | Low | Healthy | 2026-06-02 |
| COMP-332 | 39 | High | Critical | 2026-05-20 |
| MIX-145 | 76 | Moderate | Watch | 2026-05-30 |
| PACK-508 | 88 | Low | Healthy | 2026-06-06 |
| CHILL-074 | 80 | Moderate | Watch | 2026-06-01 |
| ROBOT-919 | 33 | High | Critical | 2026-05-19 |
| BLOW-223 | 91 | Low | Healthy | 2026-06-04 |
| CONV-440 | 41 | High | Critical | 2026-05-22 |
| WELD-150 | 86 | Low | Healthy | 2026-06-03 |
Immediate attention: ROBOT-919, COMP-332, and CONV-440 are critical and should enter this week planning queue.
Visual 2
Answers: What is likely to happen next?
Monitored Asset: ROBOT-919
Current health score: 33 and falling by approximately 3.5 points per week.
Modeled RUL estimate: 19 days before crossing operating threshold under current load profile.
Projection: Without intervention, this asset is expected to enter high-probability failure zone within the next planning cycle.
Visual 3
Answers: What should we do?
Recommended action: Replace spindle bearing and realign axis drive.
Maintenance priority: P1 - Execute within 72 hours.
Potential downtime reduction: 14.5 hours
Estimated savings opportunity: $42,800
Recommended intervention date: 2026-06-19 (night shift window)
Recommended action: Schedule compressor seal replacement and vibration balancing.
Maintenance priority: P1 - Execute within 5 days.
Potential downtime reduction: 9.0 hours
Estimated savings opportunity: $28,400
Recommended intervention date: 2026-06-21 (planned utility shutdown)
Recommended action: Replace conveyor motor coupling and inspect chain tension assembly.
Maintenance priority: P2 - Execute within 7 days.
Potential downtime reduction: 6.5 hours
Estimated savings opportunity: $17,200
Recommended intervention date: 2026-06-23 (line changeover slot)
Reviewers can inspect architecture, setup steps, demo runbook, and BridgeOps positioning directly in the repository.
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