System for Real-Time Acoustic Health Monitoring in Software-Defined Automotive Systems

Overview

This invention relates to automotive diagnostics, software-defined vehicle architectures, and AI-based signal processing. It provides a real-time acoustic health monitoring system that acquires in-vehicle acoustic signals, performs edge inference to detect abnormal operating signatures of vehicle subsystems, correlates acoustic findings with vehicle telemetry and software states, and generates actionable health, safety, service, and maintenance outputs as a granted patent.

Key innovation highlights

• Acoustic sensor layer using one or more microphones or microphone arrays positioned across vehicle zones
• Audio preprocessing and feature extraction using noise filtering and compact acoustic embeddings
• Edge AI inference for anomaly detection, subsystem classification, health scoring, and severity grading
• Telemetry correlation with speed, acceleration, RPM, torque, braking, thermal, and electrical context
• Software-state correlation using OTA build identifiers, calibration versions, drive modes, and feature flags
• Alerting and evidence management with acoustic snippets, telemetry context, and recommended service actions
• Optional backend integration for fleet analytics, governed model updates, and predictive maintenance workflows

Architecture and system significance

Supports diagnostics architecture that fuses acoustic evidence with telemetry and software-defined state metadata to improve early warning and reduce false alarms under varying operating conditions.

Technical Architecture

• Acoustic sensing layer
• Audio preprocessing module
• Feature extraction module
• AI inference module
• Vehicle telemetry interface
• Software-state interface
• Correlation module
• Alerting and evidence management subsystem
• Optional backend integration layer for fleet analytics and model governance

Operational Flow

1. Microphones or acoustic sensor arrays capture vehicle-zone acoustic signals.
2. Audio preprocessing removes noise and isolates relevant subsystem frequency bands.
3. Feature extraction generates acoustic features and embeddings.
4. Edge AI models classify abnormal acoustic patterns and estimate anomaly confidence.
5. Vehicle telemetry and software-state metadata are collected and normalized.
6. Correlation logic fuses acoustic outputs with telemetry and software-state context.
7. The system generates health scores, anomaly confidence, and condition-normalized severity outputs.
8. Alerts and diagnostic evidence are stored for service verification, warranty analysis, and predictive maintenance.
9. Optional backend services aggregate fleet patterns and distribute governed model updates.

Problem Solved

The system addresses the gap between traditional reactive diagnostics and early-stage subsystem degradation detection. By correlating acoustic evidence with telemetry and software-defined states, the application supports earlier warnings and improved maintenance planning in modern software-defined vehicles.

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