Agentic Workflow for Mining and Heavy Earth-Moving Equipment Diagnostics

A single unplanned failure of a haul truck drivetrain or hydraulic system in a remote pit can halt production and require flying in specialized crews and parts, costing $250k+ per day in lost revenue and emergency repair. A custom workflow with edge-based anomaly detection and predictive models for critical components (e.g., final drives, swing motors) identifies degradation weeks in advance, enabling planned interventions during scheduled outages. This shifts maintenance from a high-variable-cost reactive model to a predictable, lower-cost condition-based one.

Manually diagnosing faults and dispatching technicians and parts to remote sites is slow and inefficient. An orchestrated workflow uses diagnostic agents to fuse telemetry, satellite backhaul data, and parts inventory checks. It automatically generates prioritized work orders with required parts and skill sets, and routes them to the nearest available crew with the correct competencies. This slashes mean-time-to-repair (MTTR) by ensuring the right resources are mobilized the first time, dramatically increasing wrench time and crew utilization.

Equipment availability directly drives mine output and revenue. This workflow integrates diagnostic health scores with production scheduling systems. Agents can recommend dynamic derating of equipment (e.g., reducing excavator load to extend bearing life) to avoid failure during a critical production push, or trigger automated bypass sequences for redundant systems. By protecting uptime, the workflow directly safeguards planned tonnage, turning maintenance into a throughput assurance function.

Mining operations traditionally stock millions in spare parts 'just in case,' tying up capital. The predictive workflow connects Remaining Useful Life (RUL) forecasts directly to ERP and procurement systems. When a component's RUL falls below a threshold, an agent automatically reserves existing stock or triggers a just-in-time order with the supplier. This enables a shift from high-cost, on-site inventory to a leaner, forecast-driven model without increasing risk.

Unexpected failures can create hazardous conditions (e.g., loss of braking, hydraulic leaks). The workflow includes safety-specific agents that continuously verify the health of critical safety systems (e.g., brake pressure sensors, guarding interlocks) and predict failures. It automates Lockout-Tagout (LOTO) procedure guidance and compliance logging. By preventing safety-critical failures and enforcing procedures, the system reduces incident risk and creates an auditable digital trail for regulators.

Beyond daily operations, the aggregated diagnostic data and RUL forecasts become a strategic asset. An analytics agent synthesizes fleet-wide health, failure costs, and repair histories to generate financial forecasts. This supports data-driven decisions on asset refurbishment, replacement (CAPEX justification), and renegotiation of OEM service contracts. Maintenance transforms from a tactical cost center into a strategic function that directly informs capital planning and improves total cost of ownership.

This agent runs on ruggedized edge hardware (e.g., IoT gateways) at the mine site, ingesting high-frequency vibration, pressure, temperature, and GPS data from CAN bus and PLCs. It performs real-time anomaly detection using lightweight statistical and ML models, flagging deviations from healthy baselines. It's designed for intermittent satellite connectivity, caching data and syncing prioritized alerts upstream when a link is available.

This core agent receives anomaly alerts and performs multi-sensor fusion to diagnose the likely root cause. It queries a vector database of historical failure signatures (e.g., specific vibration patterns for final drive bearing spalls) and retrieves the most similar past incidents. It outputs a diagnosis with a confidence score, implicated component (e.g., hydraulic pump, swing bearing), and references to relevant SOPs.

This agent acts as the workflow's central controller. It takes the diagnostic output, checks the digital twin for operational context, and queries the ERP/CMMS (e.g., SAP, Maximo) for technician availability, skill sets, and spare parts inventory at the nearest depot. It then automatically generates and routes a detailed work order, reserving parts and scheduling the crew. For critical faults, it triggers pre-configured containment actions like autonomous derating.

A mobile-focused agent that delivers the work order, step-by-step repair procedures, and interactive diagrams to a technician's tablet, even in network-dead zones. It uses a local RAG system over maintenance manuals and allows the tech to log progress, request parts, or escalate complex issues. Updates sync when connectivity is restored, keeping the central CMMS current.

This predictive agent consumes historical telemetry and recent diagnostic results to model the degradation trajectory of critical components. Using survival analysis or regression models, it forecasts the RUL for components like turbochargers or hydraulic cylinders. These forecasts feed directly into the orchestrator for dynamic spare parts ordering and long-range maintenance scheduling, shifting from calendar-based to condition-based planning.

A governance agent that creates an immutable, timestamped record of every step in the diagnostic and maintenance loop. It logs the raw sensor data that triggered the alert, the diagnostic agent's reasoning chain, all automated decisions (e.g., work order creation, part reservation), and technician sign-offs. This provides a defensible audit trail for safety reviews, warranty claims, and operational performance analysis.

Primary Value: Slash mean-time-to-repair (MTTR) by 40-60% in remote sites by providing offline-capable diagnostic agents and AR-guided repair instructions directly to technicians' rugged tablets.

Build Role: Provide failure mode libraries, validate diagnostic accuracy of AI agents against real-world faults, and define the escalation paths for high-severity alerts. They are the ultimate end-users of the self-healing recommendations.

Primary Value: Protect production throughput and margin by avoiding unplanned downtime events that can cost over $100k per hour for a stalled haul truck fleet. Dynamic derating logic trades minor speed loss for catastrophic failure prevention.

Build Role: Define operational constraints (e.g., no maintenance during peak blasting windows), approve the business logic for autonomous load-shedding, and mandate the integration points with existing SCADA and dispatch systems.

Primary Value: Create a scalable, secure data fabric that ingests high-frequency telemetry from CAN bus, vibration sensors, and satellite backhaul, enabling real-time edge inference and cloud-based model retraining.

Build Role: Architect the hybrid edge-cloud pipeline, ensure data integrity and sensor calibration monitoring, and manage integrations between the diagnostic agents and legacy CMMS (e.g., SAP PM, Maximo) and ERP systems for parts procurement.

Primary Value: Optimize working capital by transitioning from bulk spare parts inventory to just-in-time delivery triggered by RUL forecasts, reducing carrying costs by 25%+.

Build Role: Integrate supplier APIs and ERP systems to enable automated parts reservation and ordering. Define rules for the agent to evaluate supplier risk and trigger sourcing from alternates during geopolitical or lead-time disruptions.

Primary Value: Transform maintenance from a cost center to a predictive function with clear ROI. Automate tracking of avoided downtime costs vs. reactive failure scenarios to justify continued CAPEX in predictive technologies.

Build Role: Define the cost models and KPIs (e.g., Cost Avoidance, Maintenance Budget Forecast Accuracy) that the workflow's reporting agents must calculate. Mandate audit trails for warranty claim automation and financial validation of self-healing actions.

Primary Value: Proactively prevent incidents by automating safety system checks (e.g., brake pressure, roll-over protection) and ensuring environmental controls (e.g., emission scrubbers) are maintained before regulatory breaches occur.

Build Role: Dictate the guardrails and approval gates for any autonomous machine adjustment. Define the immutable digital audit trail requirements for Lockout-Tagout (LOTO) procedure compliance and emission reporting generated by the workflow.