在传感器内实现故障诊断，仅用笔记本一小时完成搜索。
BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
- 基于硬件约束的神经架构搜索，极低内存占用（4-8kiB RAM）。
- 在STM32微控制器上实现99.5%诊断准确率，无需GPU。
- 适合嵌入式设备部署，可大规模生产应用。
本文提出BearingNAS，一种面向硬件感知的神经架构搜索框架，旨在通过传感器内处理将智能直接部署到传感器芯片上。该框架将搜索问题建模为受限优化问题，目标是在极端微预算下运行（RAM 4–8 kiB，Flash 16–32 kiB）。为避免依赖昂贵的独立GPU，我们设计了一种轻量级、无梯度的搜索策略，并采用单一数据流搜索空间，结合衰减核增长机制防止参数爆炸。我们在案例西部保留大学（CWRU）轴承基准上评估了该框架，针对STMicroelectronics三款设备进行架构优化：两款通用微控制器和LSM6DSO16IS智能传感器处理单元（ISPU）。整个搜索过程仅在笔记本电脑CPU上运行，不到一小时即收敛。最优的传感器内架构在ISPU上达到99.50%的高诊断准确率。结果表明，将机器学习任务迁移至传感器封装内部具有可行性，可实现低成本、可量产的轴承故障诊断系统。
原文摘要 · Abstract (English)
This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directly onto the sensor die via in-sensor processing. BearingNAS frames the search as a constrained optimization problem targeting extreme micro-budgets (4 to 8 kiB of RAM and 16 to 32 kiB of Flash). To eliminate the reliance on expensive discrete GPUs, we propose a lightweight, derivative-free search strategy paired with a single data-flow search space that leverages a decaying kernel growth formulation to prevent parameter explosion. We evaluate our framework on the Case Western Reserve University (CWRU) bearing benchmark, optimizing architectures for three STMicroelectronics targets: two commodity microcontrollers and the LSM6DSO16IS Intelligent Sensor Processing Unit (ISPU). Running entirely on a laptop CPU, the search converges in less than an hour. The resulting best in-sensor architecture achieves a highly competitive diagnostic accuracy of 99.50\% on the ISPU. These results demonstrate the viability of shifting the machine learning workload inside the sensor package, enabling low-cost, production-scale bearing fault diagnosis.
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