Crusher Liner Wear Management System
Published:
A full-stack platform for tracking and forecasting the wear of crusher liners in minerals processing operations, built from raw 3D laser scan data through to production deployment.
Business impact
This platform replaced manual caliper measurements with automated 3D point cloud analysis, transforming liner wear assessment from imprecise periodic snapshots into continuous, data-driven forecasting. Maintenance scheduling shifted from fixed intervals to remaining-useful-life predictions — reducing both premature replacements and the risk of catastrophic failure. Dual deployment (desktop for remote mine sites, web for centralized management) ensured adoption across operational contexts.
| Metric | Result |
|---|---|
| Measurement | 3D point cloud vs. manual calipers |
| Prediction | Remaining useful life forecasting |
| Deployment | Desktop (offline) + Web (centralized) |
| Coverage | Concave and mantle wear profiles |
Strategic context
Crusher liner replacement is one of the highest-cost maintenance activities in mineral processing — each change involves days of downtime and hundreds of thousands in parts and labor. Replacing liners too early wastes material; too late risks catastrophic failure that can shut down the entire crushing circuit. This system provides the quantitative basis to make that decision optimally, turning a high-stakes judgment call into a data-informed planning activity.
Key Performance Indicators — Process impact
The system redirects expert time from measurement to analysis, while providing richer wear profiles than manual methods can achieve.
| KPI | Baseline (manual) | With system | Impact |
|---|---|---|---|
| Processing time | Hours (manual caliper measurements) | ~80% reduction (automated 3D scan processing) | Expert time redirected to analysis and evaluation |
| Profile coverage | Specific cross-section cuts | Full point cloud: max-wear, mean-wear, min-wear profiles | Complete wear characterization, not sampled |
| Delivery time | Days for manual reporting | Maintained or reduced despite richer analysis | Faster decisions with more information |
| Wear projection | Experience-based estimates | Data-driven remaining-life forecasting from trend models | Optimal replacement timing, not conservative/late |
The challenge
Gyratory and cone crushers are among the largest and most critical machines in a minerals processing plant. Their concave and mantle liners degrade continuously under extreme loads, and knowing when to schedule a liner change is a costly decision: too early wastes material, too late risks catastrophic failure. Traditionally, wear is estimated with manual caliper measurements taken during maintenance windows – a slow, imprecise, and sometimes dangerous process. 3D laser scanning offers a far richer picture, but the raw point clouds (millions of points in DXF or PTS format) require significant processing before they become actionable.
System architecture
- Point cloud ingestion and parsing: Raw scan files (DXF, PTS – typically millions of points per scan) are parsed and transformed into cylindrical coordinate representations (r, theta, z) aligned to the crusher axis via least-squares axis fitting. Aggregation routines bin points by angular sector and axial elevation, collapsing the dense point cloud into interpretable radial-axial wear profiles that capture the geometry of concave and mantle surfaces.
- Campaign and survey management: Each crusher liner installation defines a campaign. Multiple surveys (scans) are registered within a campaign, enabling wear progression tracking over time. The system manages metadata, alignment corrections, and reference geometries.
- Wear trend modeling and forecasting: Wear rates are computed per profile zone, and regression models project remaining useful life. The system generates recommended change dates with confidence bounds, supporting maintenance planning.
- Dual deployment architecture: The platform operates in two modes. A desktop application (Streamlit/Dash packaged with PyInstaller) serves offline mine sites with no internet connectivity. A web platform (Next.js frontend, FastAPI backend, PostgreSQL database, Redis caching) provides centralized management for multi-site operations.
- Infrastructure: Docker Swarm orchestration with Traefik as reverse proxy and load balancer. Ansible playbooks automate provisioning and deployment across environments.
Technology stack
- Backend: FastAPI (Python), PostgreSQL, Redis, Celery for async tasks
- Frontend: Next.js (React/TypeScript)
- Desktop: Streamlit and Dash applications, packaged via PyInstaller
- 3D Processing: NumPy, SciPy, Open3D for point cloud manipulation
- Infrastructure: Docker Swarm, Traefik, Ansible, Nginx
- ML/Forecasting: scikit-learn, custom regression models for wear projection
This project is part of proprietary consulting work. Source code is not publicly available.