Enhancing Rolling Stock Availability through AI-Powered Predictive Diagnostics
The client faces an operational urgency to maximize the uptime of their rolling stock. Compounding this challenge is an aging crisis, where outdated equipment leads to frequent failures. To navigate these issues, there's a proactive mandate to predict equipment failures, both to prevent operational downtime and to fine-tune preventive maintenance plans.
What we did
Challenge
Data Transmission: Installation of telemetry devices for data collection.
Timely Detection: Accurate and timely fault detection.
Rapid Tech Deployment: Quick development of ML and AI tools within a non-existent data architecture.
Our approach
Data Centralization: Aggregate all relevant data into a unified data model for anomaly detection.
Custom ML Models: Train machine learning models tailored for each type of failure.
Expert Validation: Collaborate with system engineers to validate and confirm anomaly detection criteria.
Production-Ready Models: Deploy anomaly detection models that integrate both machine-learned rules and engineering expertise.
Outcome
Automated Alerts: Real-time notifications sent to operations managers.
Quick Diagnostics: Automated data collection for faster and more efficient interventions.
Asset Intelligence: Improved understanding of equipment life cycles to optimize maintenance and replacement plans. Self-Sufficient Teams: Capable of quickly developing and deploying predictive maintenance models.
Data-Driven Processes: Diagnostic and maintenance processes enhanced by a data lake.
Future Roadmap: 18-month implementation plan with quantified business gains presented to the executive committee.
