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SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available MAGS-MINING-CRUSHER-TEAM-001 Agent Team

Agentic Automation Team for Liner Analysis and Scheduling For Crushers in The Mining Industry

Three coordinated agents autonomously manage liner wear, maintenance scheduling, and crusher performance — keeping humans on the loop while eliminating routine coordination overhead.

Mining Crusher Maintenance Optimization

Target outcome · Proactive crusher maintenance with minimized liner replacement frequency, reduced overlap conflicts, and consistent throughput — without operator manual intervention for routine decisions.

Business problem

In the mining industry, crushers are essential for processing high volumes of material, yet their maintenance is both challenging and resource-intensive. Liner wear and equipment downtime are persistent issues, often resulting in unplanned maintenance, production delays, and increased operational costs. Scheduling maintenance for multiple crushers to minimize overlap and ensure availability requires balancing wear rates, resource constraints, and throughput demands — a task that overwhelms manual coordination.

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Human operators typically handle these decisions manually, monitoring equipment metrics, anticipating maintenance needs, and resolving conflicts in resource allocation. This reactive approach burdens maintenance teams, limits operational flexibility, and creates a continuous cycle of firefighting that impacts overall plant performance.

What it does

Three specialized agents — Maintenance Coordination, Wear Rate Optimization, and Performance Monitoring — form a collaborative team within XMPro MAGS.

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The team autonomously manages routine crusher decisions including maintenance scheduling, liner wear control through power adjustments, and throughput monitoring. Agents follow a continuous observe-reflect-plan-act cycle connected to real-time sensor data through XMPro Data Stream Designer, keeping a minimum of five operational crushers available while reducing unplanned downtime and manual planning burden.

3-agent team

  • Maintenance Coordination Agent — dynamically schedules maintenance to mitigate overlaps and resource conflicts, ensuring minimum crusher availability targets are maintained
  • Wear Rate Optimization Agent — monitors liner wear rates and optimizes power settings and choke feeding to prolong liner life and reduce replacement frequency
  • Performance Monitoring Agent — continuously tracks throughput targets, deviations, and operational stability, alerting operators for significant variations

What the team handles

Handles

Maintenance schedule generation and conflict resolution, power setting adjustments to manage wear rate, work order triggering for liner replacement, throughput deviation alerting, real-time performance monitoring across the crusher fleet.

Does not handle

Capital replacement decisions, crusher design changes, procurement and supply chain management, process plant optimization beyond crusher scope, emergency safety system actuation.

Humans retain authority over

Strategic maintenance budget decisions, major operational mode changes, emergency interventions, approval of high-impact schedule changes affecting multiple crushers simultaneously, and performance target setting.

Team composition

These agents coordinate as a team to deliver the outcome above. Each can be scoped and deployed independently or as part of this team.

AI Agent

Agentic Equipment Monitoring Agent (Health Monitor)

Provides continuous, intelligent equipment health assessment across entire asset fleets by fusing multi-parameter sensor data with Composite AI to deliver prioritised, contextualised health alerts that operators can trust — eliminating alarm floods and enabling genuinely predictive maintenance.

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AI Agent

Agentic Maintenance Coordinator Agent (Predictive Maintenance Reliability Strategist)

Continuously monitors equipment health, predicts maintenance needs, and orchestrates resource allocation and scheduling across production systems — shifting teams from reactive fixes and rigid schedules to predictive, coordinated maintenance management.

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AI Agent

Agentic Simulation & Scenario Analysis Agent

Continuously runs process simulations and what-if analyses to evaluate proposed changes, optimisation strategies, and operational scenarios — providing predictive insights that enable other agents and human decision-makers to validate strategies before implementation and quantify risks and benefits of proposed actions.

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Current process vs. with Agent Team

TODAY · CRUSHER MAINTENANCE OPTIMIZATIONREACTIVE
×
Maintenance scheduling across multiple crushersManual coordination with frequent overlap conflicts and reactive rescheduling
×
Liner wear rate managementPeriodic manual review — excessive wear often detected after damage occurs
×
Throughput deviation responseOperator-detected with delayed response during shift changes
×
Liner replacement timingCalendar-based or failure-triggered — suboptimal and costly

Outcomes and measurement

Unplanned crusher downtime

Baseline Reactive response driven by unexpected liner failure or performance degradation
With agent Significant reduction through predictive scheduling and wear rate management

Liner replacement frequency

Baseline Conservatively high to avoid failures — increased costs
With agent Optimized to actual wear rate, extending liner life and reducing replacement costs

Maintenance scheduling conflicts

Baseline Manual coordination frequently results in overlapping downtime
With agent Conflicts eliminated through dynamic autonomous scheduling with fleet-level visibility

Operator manual planning burden

Baseline High — operators spend significant time on routine scheduling decisions
With agent Reduced to exception management and strategic oversight

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Crusher sensors

power drawthroughputfeed ratechoke levelvibrationliner wear indicators

CMMS

maintenance historytechnician availabilitywork orders

production schedules

operational logs

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. How many crushers are in scope and what is the minimum operational fleet availability target?
  2. What sensor data is currently available from each crusher and at what refresh rate?
  3. What are the current liner replacement intervals and what is the cost per liner change?
  4. What CMMS system is in use and does it support automated work order creation via API?
  5. What is the current unplanned downtime rate and its impact on production targets?

Want our AI to walk you through these scoping questions?

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Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Agentic Automation Team for Liner Analysis and Scheduling For Crushers in The Mining Industry fits your operations, what data you'd need, and what a scoping engagement typically looks like.

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