Quantum-Assisted PINNs for Predictive Maintenance of Armored Fighting Vehicles (AFVs)

Predictive maintenance using machine learning is becoming essential in defense logistics, particularly for Armored Fighting Vehicles (AFVs). These assets operate under harsh conditions and require high reliability, making proactive fault detection a priority. 

By integrating machine learning with physics-based modeling—and now quantum assisted methodologies like Quantum-Assisted Physics-Informed Neural Networks (QA-PINNs) are emerging to address these challenges with better efficiency and accuracy.

Machine Learning Challenges in AFV Maintenance

Traditional predictive maintenance techniques rely heavily on historical data and statistical modeling. However, predictive maintenance present unique obstacles:

> Sparse failure data due to limited historical incidents.

> Highly nonlinear physical behavior under variable terrain and load conditions.

> Stringent operational requirements where failures can have mission-critical consequences.

These factors limit the effectiveness of conventional machine learning. As a result, hybrid approaches that incorporate physical laws (Physics-based) and domain expertise (data-driven) are increasingly essential.

From Data to Decision: Evolving Predictive Maintenance

The predictive maintenance process is not just about fault detection—it is a structured, data-driven decision-making framework. It involves:

> Data acquisition from onboard sensors and maintenance logs.

> Pattern recognition and anomaly detection via statistical and machine learning models.

> Risk estimation, which evaluates how detected anomalies might lead to component or system failure.

> Maintenance scheduling, where insights are converted into actionable plans to minimize downtime and risk.

However, accuracy and reliability remain major challenges in meeting mission goals in defense. This is where Physics-Informed Machine Learning becomes vital.

Decision Support using Physics-Informed Machine Learning

Standard machine learning models are effective in identifying fast-evolving anomalies but often fall short in modeling slow degradation or predicting long-term trends—especially in harsh environments. 

Physics-Informed Neural Networks (PINNs) help overcome this by embedding physical laws directly into the model architecture. These laws serve as constraints that improve interpretability, stability, and accuracy. When combined with domain knowledge (such as thermomechanical behavior of AFV components), these models yield better forecasts, particularly for rare failure scenarios. 

Quantum-Assisted PINNs (QA-PINNs) further enhance this capability by using quantum layers to accelerate training and reduce computational burden without compromising precision.

Streamlining Maintenance Optimization

Once an anomaly is detected, the next step is to determine its severity and impact, and to optimize the timing and type of intervention. This involves:

> Failure Mode Identification: Recognizing whether the issue is functional (e.g., sensor fault) or physical (e.g., component degradation).

> Time-to-Maintenance Estimation: Calculating how long before the anomaly becomes critical.

> Maintenance Planning: Selecting the most effective intervention with the least disruption.

The optimization process integrates real-time data with historical maintenance records and engineering models. It allows fleet managers to shift from reactive to proactive strategies—extending asset life and improving mission availability.

Functional Health Assessment Using QA-PINNs

A robust health assessment framework is essential to distinguish meaningful anomalies from benign noise or sensor errors. The QA-PINN framework adopts an 8-step procedure for anomaly detection and classification:

1. Signal Preprocessing: Raw data is filtered using rule-based logic.
2. Healthy Data Sampling: Training datasets are selected from confirmed nominal conditions.
3. Feature Extraction: Key indicators (e.g., statistical measures) are derived from signal segments.
4. Feature Reconstruction (AAKR): Features are rebuilt using historical baselines via Auto-Associative Kernel Regression.
5. Anomaly Detection: Deviation from the reconstructed baseline signals an anomaly.
   
   1. Example: AFV surface images can be analyzed for cracks using QA-PINNs enhanced by hybrid quantum-classical CNN architectures, such as VGG16 combined with Quantum Transfer Learning delivers 99.99% accuracy.
6. Anomaly Injection: Synthetic anomalies are added during training to improve model generalization.
7. Anomaly Classification: Logistic regression models assign classes to anomalies based on learned patterns.
8. Severity Assessment: Knowledge-based rules determine system impact and fault localization.

This procedure enables rapid and accurate diagnostics, which is critical when decisions must be made under tight operational constraints.

Hybrid Modeling for Risk of Failure

Modeling the risk of failure requires both data-driven insights and physics-based predictions. The QA-PINN framework uses a hybrid approach:

Direct Detection: Sensor-derived signals directly reveal known faults.

Indirect Inference: Analytics reveals aging effects that do not manifest through direct measurements.

In cases where physical degradation mechanisms (like thermal creep or fatigue) are not well understood, engineered features derived from domain expertise are embedded into the model. This ensures that even with sparse data, the risk estimation remains grounded in real-world behavior.

The goal is to define the Potential-to-Functional failure interval (P-F)—the window between early fault initiation and system failure. QA-PINNs use probabilistic forecasting to estimate this interval, presenting it as a damage distribution curve. The risk is assessed by evaluating the likelihood of this distribution exceeding failure thresholds, while also considering cascading effects across subsystems.

What is BQPhy’s Quantum-Assisted PINN?

Standard PINNs, while powerful, face challenges related to solution accuracy, increasing parameters and computing resources required. QA-PINNs address these through a hybrid architecture:

• A quantum hidden layer is introduced before classical layers.
• Rotation gate values serve as trainable parameters, enhancing feature extraction.
• The model retains high accuracy with fewer trainable parameters and faster training time.

Additionally, QA-PINNs adopt techniques from numerical methods, replacing traditional gradient-based training with differential operator approximations. This boosts convergence while aligning with the physical modeling approaches used in aerospace and defense engineering.

Enhanced Readiness Through Quantum-Driven Intelligence

Incorporating Quantum-Assisted PINNs into predictive maintenance systems for AFVs marks a pivotal step forward. These models combine the strengths of physics-informed learning, domain knowledge, and quantum computing to deliver:

> Improved accuracy in predicting both mechanical and electronic failures.

> Early fault detection, even with limited data.

> Faster training for real-time deployment in operational environments.

> Reduced downtime and optimized maintenance schedules.

As defense systems grow more complex, QA-PINNs offer a scalable, intelligent, and mission-ready solution—setting a new standard for reliability and performance in military asset management.

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