Predictive Maintenance for Heavy Machinery
Transform raw telemetry from excavators, haul trucks, and loaders into predictive maintenance intelligence. SQL-native access to CAN bus data, IoT sensors, and OEM telematics—no API limits, no data silos.
- Direct SQL access to raw sensor streams: engine telemetry, hydraulic parameters, vibration data
- Build ML models on historical failure patterns with PostgreSQL-compatible analytics
- Real-time dashboards in Power BI, Tableau, or Grafana—connect via standard PostgreSQL
Real-Time Telemetry Dashboard
Live sensor data from mining fleet
Unplanned downtime costs mining operations millions. Data silos prevent predictive maintenance.
Heavy machinery generates terabytes of telemetry data—engine parameters, hydraulic pressures, vibration signatures, temperature profiles. But this data is locked in proprietary systems, paginated APIs, or static reports. You can't query it freely, join it with maintenance records, or build predictive models without extensive IT support.
IoT Query changes this:
- SQL-native access to all telemetry—query CAN bus data, IoT sensors, and OEM feeds with standard SQL, no API rate limits
- Raw data fidelity in the Bronze layer—millisecond-level sensor readings preserved for anomaly detection and root cause analysis
- Predictive analytics—build ML models on historical failure patterns, correlate multi-variable sensor deviations, predict Remaining Useful Life (RUL)
- Real-time BI integration—connect Power BI, Tableau, or Grafana directly to IoT Query for live maintenance dashboards
- Zero infrastructure overhead—Navixy hosts and manages the entire Private Telematics Lakehouse (PTL), scaling automatically
Three Pillars of Predictive Maintenance Intelligence
From raw telemetry to actionable maintenance insights—all in one SQL-accessible platform.
Bronze Layer: Raw Telemetry Access
Direct SQL access to unprocessed sensor streams and CAN bus data.
- •High-frequency engine telemetry (RPM, throttle position, coolant temp, oil pressure)
- •Hydraulic system parameters (pressure, flow rates, pump efficiency)
- •Vibration and temperature sensors from critical components
- •GPS coordinates with operational context (idle, working, transit)
- •Device health metrics and communication status
Outcome: Complete data fidelity for anomaly detection and root cause analysis.
Technical: Query raw time-series data at millisecond granularity. No aggregation, no loss.
Predictive Analytics Engine
Build ML models on historical failure patterns and real-time sensor correlations.
- •Multi-variable correlation analysis across engine, hydraulic, and structural systems
- •Failure mode identification using pattern recognition on sensor deviations
- •Remaining Useful Life (RUL) predictions for critical components
- •Early warning thresholds based on operational baselines
- •Integration with CMMS for automated work order generation
Outcome: Shift from reactive to predictive maintenance, reducing unplanned downtime by 40-60%.
Technical: Leverage PostgreSQL-compatible analytics functions and geospatial queries (PostGIS).
Operational Intelligence Dashboards
Real-time fleet health monitoring and maintenance cost analytics.
- •Asset utilization metrics: engine hours, idle time, productive vs. non-productive cycles
- •Fuel consumption analysis by equipment type, operator, and site conditions
- •Maintenance cost tracking: parts, labor, and downtime costs per asset
- •Comparative performance benchmarking across fleets and sites
- •Compliance reporting for safety inspections and regulatory requirements
Outcome: Data-driven decisions on equipment replacement, operator training, and site optimization.
Technical: Connect Power BI, Tableau, or Grafana directly to IoT Query via PostgreSQL connection strings.
Comprehensive Telemetry Integration
Device-agnostic platform supporting all major telemetry sources for heavy machinery.
CAN Bus Integration
Direct J1939/J1708 protocol decoding for heavy machinery
- Engine Control Unit (ECU) parameters
- Transmission and drivetrain diagnostics
- Hydraulic system monitoring
- Fault code capture and interpretation
IoT Sensor Networks
Multi-sensor arrays for comprehensive equipment monitoring
- Vibration sensors on bearings and rotating components
- Temperature probes for engine, hydraulic, and structural monitoring
- Pressure transducers for hydraulic and pneumatic systems
- Oil analysis sensors (viscosity, contamination, wear particles)
OEM Telematics Gateways
Native integration with manufacturer telematics platforms
- Caterpillar, Komatsu, Liebherr API connectivity
- John Deere JDLink, Volvo ActiveCare integration
- Proprietary protocol support via IoT Logic
- Bidirectional data exchange for remote diagnostics
Real-World Predictive Maintenance Use Cases
Proven applications of IoT Query analytics in mining operations.
Hydraulic System Failure Prevention
Detect pump degradation before catastrophic failure
Technical approach: Correlate pressure fluctuations, temperature trends, and vibration patterns to predict pump seal failure 2-3 weeks in advance.
Engine Overhaul Optimization
Optimize rebuild schedules based on actual wear patterns
Technical approach: Analyze oil analysis data, coolant temperature profiles, and load cycles to determine optimal rebuild intervals.
Undercarriage Wear Prediction
Predict track and roller replacement needs
Technical approach: Combine GPS-based terrain analysis, operational hours, and load data to forecast wear rates and schedule replacements proactively.
Operator Performance Impact
Quantify operator behavior on equipment health
Technical approach: Cross-reference harsh events (rapid acceleration, excessive idling) with component failure rates to identify training opportunities.
Measurable Impact on Mining Operations
Reduction in unplanned downtime
Lower maintenance costs per operating hour
Equipment availability improvement
Trusted by Mining Operations Worldwide
"We reduced unplanned downtime by 45% in the first year. IoT Query gave us the granular sensor data we needed to build predictive models that actually work."
"Finally, we can query our telemetry data like any other database. No more waiting for API exports or dealing with pagination limits."
"The ability to correlate hydraulic pressure, vibration, and temperature data in real-time SQL queries transformed our maintenance strategy."
Enterprise Security & Compliance
- TLS encryption in transit, AES-256 encryption at rest with region-specific key management
- Tenant isolation—each client's data in a dedicated database schema, preventing cross-tenant access
- Role-based access control and optional two-factor authentication for user access
- Data residency options—host IoT Query in EU, Middle East, Americas, or APAC to meet regional compliance requirements
- SOC 2 Type II and ISO 27001 aligned (certifications in progress)
- High availability with redundant data centers and disaster recovery processes
Frequently Asked Questions
Transform Your Mining Fleet Data into Predictive Intelligence
Schedule a demo to see how IoT Query enables SQL-native analytics on CAN bus telemetry, IoT sensors, and OEM data. Build predictive maintenance models, reduce downtime, and optimize costs—all with zero infrastructure overhead.