FracAdapt Data Details and Data Flow
Data Details and Data Flow
Our Phase I methodology integrates advanced AI techniques with real-world military vehicle data to quantify terrain impact on vehicle health and predict maintenance needs for military vehicle predictive maintenance. This approach aims to move beyond reactive fault detection towards proactive predictive maintenance, focusing on the logical flow from historical failure patterns to generative terrain modeling.
Army CBM Dataset Foundation
Our work begins with a robust foundation of proven military vehicle failure patterns. We will leverage comprehensive data from the US Army Condition-Based Maintenance (CBM) demonstration program, encompassing data from a single vehicle (VIN00738) from the publicly available demonstration dataset, which includes recordings from a HMMWV platform.
Dataset Details: Time-series sensor data from actual combat operational deployments in Afghanistan, meticulously labeled with fault events (Diagnostic Trouble Codes - DTCs). The CBM data includes base names and indices from 8 Afghanistan bases (but no GPS coordinates or location-sensor correlation) for this single vehicle's deployments (e.g., near Afghanistan military bases: AFG-Apache, AFG-Kandahar, AFG-Masum Ghar, AFG-Lindsey, AFG-Frontenac, AFG-Pacemaker, AFG-Pasab, AFG-Zangabad). Our innovation is to explore the impact of generalized terrain characteristics on this existing data. No GPS coordinates linking sensors to specific terrain locations.
Key Value: This dataset, from a single vehicle, provides invaluable insights into combat-proven failure modes under diverse operational conditions encountered by that vehicle, featuring real sensor data from Afghanistan operations, but without location-specific terrain correlation. This makes the data more valuable than synthetic training data because it represents real combat conditions, allowing identification of the "what" and "when" of vehicle failures for military vehicle predictive maintenance based on a detailed single-vehicle analysis.
Reference: This approach builds upon methodologies like "Generative Learning for Simulation of Vehicle Faults" (Kuiper et al., Duke/Stanford) [Note: While paper references 200+ vehicles, available dataset contains only 1 vehicle with location metadata], utilizing similar data structures for initial analysis.
Data Structure & Format
The US Army CBM demonstration dataset for VIN00738, enriched with Afghanistan combat data, is characterized by its high granularity and comprehensive coverage of vehicle operational parameters. This foundation is critical for developing robust predictive models based on single-vehicle behavior.
Specific Sensor Types and Data Formats:
All sensor data is numeric, typically floating-point values, and collected with precise timestamps.
Engine Parameters: RPM, oil pressure, coolant temperature, fuel consumption.
Transmission Data: Gear position, fluid temperature, clutch slip.
Suspension & Chassis: Vertical acceleration (e.g., from accelerometers on axles/chassis), suspension travel, wheel speed.
Braking System: Brake pressure, temperature readings.
Electrical System: Battery voltage, alternator output.
Load/Weight Data: Payload sensors, axle weight distribution, cargo weight.
Operational Load: Towing capacity utilization, passenger count.
Time-Series Structure:
Data typically sampled at 1 Hz frequency.
Approximately 25–30 distinct sensor channels for this single vehicle, producing a rich multivariate time-series.
Fault Labeling System:
Fault events labeled with Diagnostic Trouble Codes (DTCs) and timestamps, enabling precise correlation with preceding sensor data.
Vehicle Metadata included:
Vehicle Identification Number (VIN00738)
Model and year (HMMWV variant)
Total mileage/hours of operation
Maintenance history
Deployment history (base names/operational durations)
Vehicle age
Current odometer reading
Load history and weight class/capacity specifications
Key Data Linkages
Correlation of Sensor Readings with Component Failures: Identify statistical and temporal correlations between sensor signatures and recorded DTCs from VIN00738.
Temporal Patterns Preceding Failures: Investigate 3–5+ hours of degradation signals (subtle shifts: vibrations, pressure fluctuations, temperature creep) preceding faults.
Environmental Context: Use base names as generalized terrain context (mountainous vs desert vs mixed) to hypothesize terrain-driven wear patterns for VIN00738.
Vehicle Type Differences: Contextualize HMMWV-specific behavior and susceptibilities compared to other platforms referenced in CBM literature.
Load and Age Correlations: Analyze how age (>36 months) and high operational loads (>80% capacity) affect failure patterns for VIN00738.
Concrete Data Examples
To illustrate dataset richness, consider representative examples expected from VIN00738 analysis (see next steps for assessments and predictions).
Proposed Methodology: Fractal Analysis — Quantifying Terrain Complexity
We will apply fractal analysis to Digital Elevation Model (DEM) data (or synthetic terrain representations) to quantify terrain complexity. This provides a numerical representation of the "roughness" and "stress-inducing" characteristics of terrain sections, reflecting generalized terrain challenges from Afghanistan geography. This step links environmental factors to CBM data for military vehicle predictive maintenance, particularly for VIN00738's operational history. This is a proposed methodology using fractal analysis.
Proposed Methodology: LSTM Networks — Learning Failure Patterns
Long Short-Term Memory (LSTM) networks will be trained on US Army CBM time-series sensor data from VIN00738's Afghanistan operations, correlated with terrain complexity metrics derived from fractal analysis. The LSTM will learn complex temporal patterns that precede component failures, establishing a link between vehicle stress signatures and the operational context observed in Afghanistan. This is where the predictive model for military vehicle predictive maintenance is developed, focused on VIN00738.
Proposed Methodology: GAN Terrain Generation — Global Deployment Readiness
The integrated approach aims to use a Generative Adversarial Network (GAN) to generate synthetic terrain profiles informed by the LSTM's understanding of terrain-induced stress leading to failures (derived from VIN00738 data). The GAN augments training data with diverse terrain-vehicle interactions and edge cases not present historically, enabling proactive scenario testing and route optimization for global deployment readiness.
Complete Example: HMMWV Suspension Failure
Consider a recurring HMMWV suspension failure observed in VIN00738's CBM dataset from Afghanistan operations.
Individual Vehicle Risk Assessment (example outputs for VIN00738):
VIN00738 Suspension: ~45% remaining useful life; HIGH RISK if deployed on Route 2 (high-complexity terrain).
Terrain-Specific Wear Multipliers: Route 2 (fractal dimension >2.7) → 1.2× accelerated suspension wear vs flat terrain (FD <2.1).
Timeline-Based Maintenance Predictions: Critical suspension components predicted to require maintenance within ~1.5 weeks under projected missions.
Distance-Based Degradation: Flat-terrain baseline critical threshold ~1,500 km; Route 2 reduces this to ~1,250 km.
Quantitative Risk: Deploying on Route 2 increases suspension failure probability from baseline 5% to 25% within next 200 km — suggests pre-mission inspection or re-routing.
These outputs illustrate how terrain-aware analysis can inform command decisions for VIN00738.
Clear Input/Output Specification
This section outlines required data and predictive capabilities for Phase I, specifically leveraging VIN00738's Afghanistan combat data.
Inputs (required)
US Army CBM Demo Data (VIN00738): time-series sensor data + DTC fault labels + base names (8 Afghanistan bases; no GPS per-sensor correlation).
Sensor Readings: engine temp, transmission temp, vertical acceleration, suspension travel, brake temps, oil pressure, RPM, speed.
Optional: GPS traces (if available) to enable precise terrain correlation.
Digital Elevation Model (DEM) data: high-resolution terrain data for fractal analysis (external).
Vehicle metadata: VIN, model/year, maintenance history, load history, age, odometer.
Outputs (what the system will predict)
Remaining Useful Life (RUL) estimates for components (e.g., suspension) for VIN00738.
Component-specific next-failure predictions (suspension, brakes, engine, transmission, electrical).
Failure probability within a given distance or mission plan (e.g., next 500 km).
Route- and terrain-aware wear projections and route optimization recommendations.
Maintenance scheduling recommendations based on planned routes and terrain context.
Success Metrics (Phase I)
Predict RUL for VIN00738 within ±150 km (validated against available data).
Classify failure vs. no-failure for VIN00738 with >80% accuracy.
Identify failure mode for VIN00738 with >70% accuracy.
Demonstrate conceptual cost savings via route-optimization scenarios for VIN00738.
Important Note: The repository data contains sensor readings from VIN00738 and base names for 8 Afghanistan locations, but lacks GPS coordinates or explicit per-sensor location correlation. The terrain analysis approach uses engineering assumptions based on Afghanistan geography and operational context (generalized base terrain characteristics) rather than direct terrain-sensor correlation from the repository. This is a proof-of-concept methodology pending precise location data.
Data Limitations and Future Scope
The publicly available US Army CBM dataset for VIN00738 provides in-depth information for a single vehicle. While this allows deep, granular analysis of terrain impact on this HMMWV, it limits fleet-wide conclusions or cross-platform analysis in Phase I. Future phases should leverage larger datasets to extend methodology to broader fleets. The eight Afghanistan locations referenced are categories associated with VIN00738's deployment history, not distinct deployment sites for multiple vehicles.
Terrain Integration Strategy:
Use the 8 Afghanistan base names (AFG-Apache, AFG-Kandahar, AFG-Masum Ghar, AFG-Lindsey, AFG-Frontenac, AFG-Pacemaker, AFG-Pasab, AFG-Zangabad) for generalized terrain characteristics until GPS-sensor correlation is available.
Apply known Afghanistan terrain types (desert, mountain, mixed) to operational scenarios.
Estimate elevation and stress factors using geographic knowledge of these bases.
Simulate terrain-specific sensor patterns for proof-of-concept validation.
Demonstrate methodology potential when complete GPS-sensor correlation becomes available.
Sensor Value Ranges and Example Failure Thresholds (for VIN00738 context)
Engine Oil Pressure: normal 30–60 PSI. Sustained <20 PSI may indicate impending pump failure; <10 PSI could trigger a low oil pressure DTC.
Transmission Fluid Temperature: normal 170–200°F. Sustained >230°F often precedes fluid degradation and transmission wear.
Vertical Acceleration (Z-axis, chassis): baseline 0.5–1.5 G. Peaks consistently >3–4 G during sustained off-road travel may indicate shock loads leading to suspension fatigue.
Example DTC codes and indications (as observed/categorized for VIN00738)
P0700: Transmission Control System Malfunction.
C0040: Right Front Wheel Speed Sensor Circuit Malfunction.
B1001: Engine Coolant Temperature Sensor Circuit High.
Hypothesized Correlations (to be tested)
Vehicle Load: Sustained >80% capacity combined with high fractal-dimension terrain (FD >2.6) may correlate with increased transmission overheating and suspension wear for VIN00738.
Vehicle Age: Older vehicles (>36 months) may exhibit accelerated electrical DTCs under combat terrain stresses.
Combat-Proven Terrain Correlations: Example hypotheses include high vertical acceleration precedes 85% of suspension failures when operating in generalized mountainous regions (AFG-Masum Ghar, AFG-Zangabad).
Feeds into Fractal Analysis Requirements
Use VIN00738 failure modes and deployment base names to guide DEM selection and fractal-analysis focus (e.g., prioritize high-resolution DEMs around regions linked to prevalent suspension failures).
Informs LSTM Training Objectives
Combine VIN00738 time-series sensor data, fault labels, and derived terrain complexity metrics as primary LSTM training data to learn temporal sequences leading to failures.
Necessitates GAN Augmentation for Global Deployment
Use GANs to generate synthetic terrain profiles to augment the training set beyond terrains VIN00738 actually traversed, enabling robust, globally applicable predictive models.
Failure Pattern Identification & Sensor-to-Terrain Correlation
Identify sensor signatures (increased vertical acceleration, extended suspension travel) consistently preceding suspension failures.
Proposed LSTM will analyze these sensor patterns against generalized terrain metrics to quantify increased failure probabilities linked to highly fractal terrain.
Predictive Terrain Generation (GAN)
Use LSTM-derived insights to inform GAN generation of synthetic terrains that produce stress signatures similar to observed failure precursors, enabling better model generalization and route planning.
US Army CBM Demonstration Dataset Summary (VIN00738)
Time-series sensor data + DTCs + base names for 8 Afghanistan bases (no GPS correlation).
Sensor readings: engine temp, transmission temp, vertical acceleration, suspension travel, brake temps, oil pressure, RPM, speed.
Optional GPS traces (if acquired) to enable precise terrain correlation.
DEM data (external) for fractal analysis.
Expected outputs: RUL, component-specific predictions, failure probabilities, route optimization, and maintenance scheduling for VIN00738.
Performance Targets for Phase I (VIN00738-focused)
Predict RUL within ±150 km.
Classify failure/no-failure with >80% accuracy.
Identify failure mode with >70% accuracy.
Demonstrate conceptual cost savings via route optimization scenarios.
Appendix: Example Hypothetical Outcomes (illustrative)
Route A causes 2.5% wear on suspension for VIN00738; Route B causes 0.8% wear — actionable input for mission planners.
Heavy load (>80%) + rough terrain (FD >2.6) → 4× increased suspension failure probability for VIN00738.
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