用双流架构融合物理模型与无监督学习，精准监测车辆持续负载疲劳。
A Dual-Stream Physics-Augmented Unsupervised Architecture for Runtime Embedded Vehicle Health Monitoring
- 双流设计：一路检测表面异常，一路用物理模型估算累积负载。
- 在资源受限的嵌入式平台运行，计算开销低，适合边缘实时监控。
- 能识别看似正常但高负荷的驾驶状态，如重载爬坡，提升维护精度。
实时量化车辆运行强度对商业及重型车队的预测性维护和状态监控至关重要。传统里程指标无法反映机械负担，而无监督深度学习模型虽能检测统计异常（通常为瞬时冲击），却常将统计稳定性误判为机械静止。我们识别出这一关键盲点：高负载稳态（如重载爬坡）看似统计平稳，实则造成显著传动系统疲劳。为此，提出双流架构，融合无监督学习用于表面异常检测，结合宏观物理代理进行累积负载估计。该方法利用低频传感器数据生成多维健康向量，区分动态风险与持续机械负荷。在RISC-V嵌入式平台上验证，计算开销低，支持资源受限的ECU上实现全面、边缘化的健康监控，避免云端监控带来的延迟与带宽成本。
原文摘要 · Abstract (English)
Runtime quantification of vehicle operational intensity is essential for predictive maintenance and condition monitoring in commercial and heavy-duty fleets. Traditional metrics like mileage fail to capture mechanical burden, while unsupervised deep learning models detect statistical anomalies, typically transient surface shocks, but often conflate statistical stability with mechanical rest. We identify this as a critical blind spot: high-load steady states, such as hill climbing with heavy payloads, appear statistically normal yet impose significant drivetrain fatigue. To resolve this, we propose a Dual-Stream Architecture that fuses unsupervised learning for surface anomaly detection with macroscopic physics proxies for cumulative load estimation. This approach leverages low-frequency sensor data to generate a multi-dimensional health vector, distinguishing between dynamic hazards and sustained mechanical effort. Validated on a RISC-V embedded platform, the architecture demonstrates low computational overhead, enabling comprehensive, edge-based health monitoring on resource-constrained ECUs without the latency or bandwidth costs of cloud-based monitoring.
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