Executive Summary

Executive Summary

This document outlines the FracAdapt Phase I project's capabilities and a systematic approach for incorporating Department of Defense (DoD) feedback to refine its scope. FracAdapt focuses on terrain-aware predictive maintenance for military ground vehicles by combining advanced AI and fractal analysis with US Army Condition-Based Maintenance (CBM) sensor data (demonstration dataset from a single vehicle, VIN00738). The methodology emphasizes aligning predictive maintenance systems with end-user needs, operational realities, and military logistics requirements.

Data Limitations and Assumptions

  • Real Data: Sensor data from a single vehicle (VIN00738) comprising 42 sensors and 1,163,360 readings collected during Afghanistan operations (2008–2012).

  • Simulated Components: Terrain classifications are derived from engineering estimates; elevation data from geographic estimates (SRTM, Copernicus, USGS 3DEP); stress calculations are based on terrain-based assumptions.

  • Data Linkage: No GPS coordinates are available to link sensor readings precisely to terrain. Terrain analysis uses engineering assumptions and inferred operational context rather than direct sensor–terrain correlation.

Important Note on Data Limitations and Methodology: The repository data contains sensor readings from VIN00738 and base names/indices from 8 Afghanistan bases, but lacks GPS coordinates or explicit location–sensor correlation. The terrain analysis approach uses engineering assumptions based on Afghanistan geography and operational context, representing a proof-of-concept methodology applicable to datasets with full location information.

The Innovation Challenge

FracAdapt addresses unpredictable vehicle degradation across varied terrain by layering terrain complexity analysis onto CBM sensor data. The approach integrates:

  • Fractal Analysis: Quantifies terrain roughness/complexity from geospatial data.

  • Long Short-Term Memory (LSTM) Networks: Processes time-series sensor data for RUL and failure probability predictions.

  • Generative Adversarial Networks (GANs): Generates synthetic terrain profiles and scenarios to augment training data, improving generalization.

This system uses fractal-derived terrain context alongside CBM sensors so LSTM models can produce terrain-aware predictions. GANs provide synthetic edge cases and diverse terrain profiles to strengthen training.

Context and Dataset Note Analysis of CBM data cited in Kuiper et al., 2024 references 200+ vehicles, but the publicly available repository contains only the single demonstration vehicle VIN00738 (with base names/indices from 8 Afghanistan bases: AFG-Apache, AFG-Kandahar, AFG-Masum Ghar, AFG-Lindsey, AFG-Frontenac, AFG-Pacemaker, AFG-Pasab, AFG-Zangabad). The FracAdapt methodology layers terrain analysis on top of this CBM data using engineering assumptions to demonstrate feasibility.

System Components Overview

  • CBM Data: VIN00738 sensor readings, fault codes, base names and indices (demonstration data).

  • Satellite Data: SRTM, Copernicus, USGS 3DEP elevation models.

  • Proposed Fractal Engine: Calculates 25+ terrain complexity metrics from elevation data.

  • Proposed GAN Terrain: Generates synthetic terrain profiles for training augmentation.

  • Proposed Feature Engineering: GPS-synchronized terrain features (based on engineering assumptions) combined with CBM sensor data.

  • RUL Predictions: Time-to-failure and route planning with confidence intervals delivered via API and dashboard.

High-level pipeline: INGEST → TRAIN → API → DASHBOARD → INTEGRATE

Real-World Implementation: Scenario (Route Planning Example)

SCENARIO: Morning Mission Briefing

  • 0600 Hours — TOC: Supply convoy to Forward Operating Base (85 km). Mission Urgency: MEDIUM.

Traditional Outcome:

  • Shortest route chosen (72 km mountain pass), mission completed, but one week later 2 vehicles have suspension failures. Cost: $4,000 repairs + downtime.

FracAdapt Outcome:

Step 1: Route Analysis (2 minutes)

  • System analyzes 3 route options using CBM data (VIN00738: base names but no GPS) augmented by terrain analysis (engineering assumptions).

    • Route 1 (Highway): 85 km, terrain complexity 2.3 → LOW RISK

    • Route 2 (Mountain): 72 km, terrain complexity 2.8 → HIGH RISK

    • Route 3 (Valley): 95 km, terrain complexity 2.4 → MEDIUM RISK

Step 2: Vehicle Assessment & RUL Prediction

  • Route 2 (Mountain, complexity 2.8) has wear multiplier 1.8x for suspension.

  • HMMWV-03: 45% RUL → completing Route 2 consumes ~30% → remaining ~15% → maintenance within ~10–12 days. HIGH RISK.

  • HMMWV-07: 42% RUL → completing Route 2 consumes ~30% → remaining ~12% → maintenance within ~10–12 days. HIGH RISK. Recommendation: Service vehicles or exclude from mission.

Step 3: Informed Decision

  • Risk calculations presented to commanders:

    • HMMWV-03: 85% probability of suspension failure within ~10–12 days post-mission.

    • HMMWV-07: 90% probability of suspension failure within ~10–12 days post-mission.

Cost–Benefit Summary

  • Option A: Take Route 2 (Mountain) — shorter travel time (~2 hours saved), but ~90% chance of 2 vehicle breakdowns; repair/recovery estimated $8,000–$12,000 per vehicle; potential mission delay 24–48 hours.

  • Option B: Take Route 1 (Highway) — low breakdown risk; HMMWV-03/07 RUL reduced to ~35% (failure in 30–35 days); adds ~2 hours travel; minimal immediate resource impact.

  • Option C: Pre-emptive maintenance then take Route 2 — 12–24 hour delay; cost $1,500–$2,500 per vehicle; ensures low risk and faster mission completion.

Decision Scenarios & Recommendations

  • Aggressive (High Urgency): Choose Route 2; commanders informed of high failure probability for contingency planning.

  • Balanced (Medium Urgency): Choose Route 1; reduce immediate risk and allow scheduled maintenance.

  • Conservative (Low Urgency): Pre-emptive maintenance (Option C); ensures readiness and long-term asset health.

Result: Informed decision-making before failures, moving from reactive repairs to proactive risk mitigation.

Expected Impact & Performance (Phase I Projections)

  • Projected maintenance savings: 30–40% reduction in unplanned maintenance events (hypothesis).

  • Terrain complexity features: 25 features (fractal-derived).

  • Key monitored components: 12 (engine, transmission, suspension, drivetrain, brakes, electrical, tires, etc.).

  • Terrain resolution: ~1 m (high-precision analysis for stress pattern prediction).

Current Project Scope (Baseline)

Core Objective

  • Demonstrate feasibility of terrain-aware predictive maintenance for military ground vehicles using fractal analysis, LSTM, and generative AI, focused on deep analysis of a single vehicle dataset (VIN00738).

Current Focus Areas (Baseline)

Area
Current Approach
Status

Vehicles

Single vehicle (VIN00738) from Kuiper et al. demonstration dataset

Baseline

Data Source

US Army CBM Dataset (Kuiper et al.) — publicly available demo contains VIN00738 (8 Afghan bases)

Identified

Terrain Analysis

Proposed fractal dimension, roughness, slope

Defined

Components

Suspension, brakes, drivetrain, electrical, tires

Comprehensive

Prediction Output

RUL, failure probability, maintenance timing

Specified

Dev, Ops & Deployment

AWS cloud infrastructure

Planned

Timeline

6 months (24 weeks)

Fixed

Key Assumptions (To Be Validated)

1

Vehicle Priority

  • Focus on the single vehicle (VIN00738).

Question for DoD: Is demonstrating deep analysis on this specific vehicle sufficient for Phase I validation?

Flexibility: Can pivot to other platforms if data becomes available in Phase II.

2

Component Priority

  • Equal weight to 5 systems (suspension, brakes, etc.).

Question for DoD: Which components cause the most operational issues?

Flexibility: Can focus on highest-impact components.

3

Terrain Types

  • Afghanistan terrain diversity (mountainous regions, desert plains, mixed combat environments).

Question for DoD: Do these match actual deployment environments for the analyzed vehicle?

Flexibility: Can adjust to actual operational terrains for expanded datasets.

4

Data Availability

  • Kuiper et al. dataset (VIN00738) accessible and sufficient for proof-of-concept.

Question for DoD: Is additional, larger fleet data available for future phases? Are there access constraints?

Flexibility: Can work with demonstration data and request additional sources for scaling.

5

Output Format

  • Technical predictions (RUL, probabilities).

Question for DoD: What format do maintenance officers actually need?

Flexibility: Can adapt outputs to operational workflows.

Phase I Objectives: Proving the Concept

Hypothesis to Validate "Combining US Army CBM sensor data from a single vehicle (VIN00738) with terrain complexity analysis will improve predictive maintenance accuracy by 25–40% compared to sensor-only approaches for that specific vehicle."

Phase I Deliverables (6 months)

1

Weeks 1–3: Feasibility Study

  • Literature review of terrain–vehicle interaction research.

  • Analysis of CBM dataset structure and variables from VIN00738 (note: base names present but no GPS).

  • Risk assessment of the technical approach.

2

Weeks 4–12: MVP Development

  • Build fractal analysis pipeline for terrain characterization.

  • Develop LSTM architecture for terrain-augmented predictions.

  • Create GAN system for synthetic terrain generation to augment training.

  • AWS infrastructure setup and integration.

  • Deliverable: 3-month report.

3

Weeks 13–20: Validation & Testing

  • Train models on historical CBM data + engineered terrain features for VIN00738.

  • Compare terrain-aware vs sensor-only predictive performance.

  • Validate on held-out test set (20% of VIN00738 data).

  • Document accuracy improvements and failure-case analyses.

4

Weeks 21–24: Phase I Report

  • Comprehensive results analysis based on single-vehicle data.

  • Technical feasibility confirmation.

  • Phase II architecture design for full fleet integration.

  • Commercialization pathway plan.

Success Goals (Phase I)

  • Demonstrate ≥25% improvement in failure prediction accuracy for the single vehicle (VIN00738).

  • Prove terrain features add predictive value beyond sensors alone for this vehicle.

  • Show the system works for vehicle type represented by VIN00738 (HMMWV) as proof-of-concept.

  • Validate approach can scale to new geographic regions with full fleet data in future phases.

CONCLUSION

We appreciate the opportunity to present FracAdapt Phase I. This document serves as a foundation to incorporate DoD feedback and refine the approach. Phase I focuses on demonstrating feasibility using deep analysis of a single vehicle dataset (VIN00738) and proving that terrain-aware predictive maintenance can materially improve prediction accuracy and operational decision-making. We intend to iterate with DoD stakeholders to adapt scope, integrations, and compliance posture for future phases aimed at full-fleet deployment.

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